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domain</title> <meta name="description" content="Search results for: knowledge domain"> <meta name="keywords" content="knowledge domain"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="knowledge domain"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8949</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: knowledge domain</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8949</span> Fake News Detection Based on Fusion of Domain Knowledge and Expert Knowledge</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yulan%20Wu">Yulan Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The spread of fake news on social media has posed significant societal harm to the public and the nation, with its threats spanning various domains, including politics, economics, health, and more. News on social media often covers multiple domains, and existing models studied by researchers and relevant organizations often perform well on datasets from a single domain. However, when these methods are applied to social platforms with news spanning multiple domains, their performance significantly deteriorates. Existing research has attempted to enhance the detection performance of multi-domain datasets by adding single-domain labels to the data. However, these methods overlook the fact that a news article typically belongs to multiple domains, leading to the loss of domain knowledge information contained within the news text. To address this issue, research has found that news records in different domains often use different vocabularies to describe their content. In this paper, we propose a fake news detection framework that combines domain knowledge and expert knowledge. Firstly, it utilizes an unsupervised domain discovery module to generate a low-dimensional vector for each news article, representing domain embeddings, which can retain multi-domain knowledge of the news content. Then, a feature extraction module uses the domain embeddings discovered through unsupervised domain knowledge to guide multiple experts in extracting news knowledge for the total feature representation. Finally, a classifier is used to determine whether the news is fake or not. Experiments show that this approach can improve multi-domain fake news detection performance while reducing the cost of manually labeling domain labels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fake%20news" title="fake news">fake news</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20domains" title=" multiple domains"> multiple domains</a> </p> <a href="https://publications.waset.org/abstracts/173899/fake-news-detection-based-on-fusion-of-domain-knowledge-and-expert-knowledge" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173899.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8948</span> Needs Analysis Survey of Hearing Impaired Students’ Teachers in Elementary Schools for Designing Curriculum Plans and Improving Human Resources</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Rashno%20Seydari">F. Rashno Seydari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Nikafrooz"> M. Nikafrooz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper intends to study needs analysis of hearing-impaired students’ teachers in elementary schools all over Iran. The subjects of this study were 275 teachers who were teaching hearing-impaired students in elementary schools. The participants were selected by a quota sampling method. To collect the data, questionnaires of training needs consisting of 41 knowledge items and 31 performance items were used. The collected data were analyzed by using SPSS software in the form of descriptive analyses (frequency and mean) and inferential analyses (one sample t-test, paired t-test, independent t-test, and Pearson correlation coefficient). The findings of the study indicated that teachers generally have considerable needs in knowledge and performance domains. In 32 items out of the total 41 knowledge domain items and in the 27 items out of the total 31 performance domain items, the teachers had considerable needs. From the quantitative point of view, the needs of the performance domain were more than those of the knowledge domain, so they have to be considered as the first priority in training these teachers. There was no difference between the level of the needs of male and female teachers. There was a significant difference between the knowledge and performance domain needs and the teachers’ teaching experience, 0.354 and 0.322 respectively. The teachers who had been trained in working with hearing-impaired students expressed more training needs (both knowledge and performance). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=educational%20needs%20analysis" title="educational needs analysis">educational needs analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=teachers%20of%20hearing%20impaired%20students" title=" teachers of hearing impaired students"> teachers of hearing impaired students</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20domain" title=" knowledge domain"> knowledge domain</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20domain" title=" function domain"> function domain</a> </p> <a href="https://publications.waset.org/abstracts/124636/needs-analysis-survey-of-hearing-impaired-students-teachers-in-elementary-schools-for-designing-curriculum-plans-and-improving-human-resources" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124636.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8947</span> Method of Cluster Based Cross-Domain Knowledge Acquisition for Biologically Inspired Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shen%20Jian">Shen Jian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hu%20Jie"> Hu Jie</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma%20Jin"> Ma Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Ying%20Hong"> Peng Ying Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Yi"> Fang Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Liu%20Wen%20Hai"> Liu Wen Hai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biologically inspired design inspires inventions and new technologies in the field of engineering by mimicking functions, principles, and structures in the biological domain. To deal with the obstacles of cross-domain knowledge acquisition in the existing biologically inspired design process, functional semantic clustering based on functional feature semantic correlation and environmental constraint clustering composition based on environmental characteristic constraining adaptability are proposed. A knowledge cell clustering algorithm and the corresponding prototype system is developed. Finally, the effectiveness of the method is verified by the visual prosthetic device design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20clustering" title="knowledge clustering">knowledge clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20acquisition" title=" knowledge acquisition"> knowledge acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20based%20engineering" title=" knowledge based engineering"> knowledge based engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20cell" title=" knowledge cell"> knowledge cell</a>, <a href="https://publications.waset.org/abstracts/search?q=biologically%20inspired%20design" title=" biologically inspired design"> biologically inspired design</a> </p> <a href="https://publications.waset.org/abstracts/70669/method-of-cluster-based-cross-domain-knowledge-acquisition-for-biologically-inspired-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70669.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8946</span> Research on Construction of Subject Knowledge Base Based on Literature Knowledge Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yumeng%20Ma">Yumeng Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Wang"> Fang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinxia%20Huang"> Jinxia Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Researchers put forward higher requirements for efficient acquisition and utilization of domain knowledge in the big data era. As literature is an effective way for researchers to quickly and accurately understand the research situation in their field, the knowledge discovery based on literature has become a new research method. As a tool to organize and manage knowledge in a specific domain, the subject knowledge base can be used to mine and present the knowledge behind the literature to meet the users' personalized needs. This study designs the construction route of the subject knowledge base for specific research problems. Information extraction method based on knowledge engineering is adopted. Firstly, the subject knowledge model is built through the abstraction of the research elements. Then under the guidance of the knowledge model, extraction rules of knowledge points are compiled to analyze, extract and correlate entities, relations, and attributes in literature. Finally, a database platform based on this structured knowledge is developed that can provide a variety of services such as knowledge retrieval, knowledge browsing, knowledge q&a, and visualization correlation. Taking the construction practices in the field of activating blood circulation and removing stasis as an example, this study analyzes how to construct subject knowledge base based on literature knowledge extraction. As the system functional test shows, this subject knowledge base can realize the expected service scenarios such as a quick query of knowledge, related discovery of knowledge and literature, knowledge organization. As this study enables subject knowledge base to help researchers locate and acquire deep domain knowledge quickly and accurately, it provides a transformation mode of knowledge resource construction and personalized precision knowledge services in the data-intensive research environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20model" title="knowledge model">knowledge model</a>, <a href="https://publications.waset.org/abstracts/search?q=literature%20knowledge%20extraction" title=" literature knowledge extraction"> literature knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20knowledge%20services" title=" precision knowledge services"> precision knowledge services</a>, <a href="https://publications.waset.org/abstracts/search?q=subject%20knowledge%20base" title=" subject knowledge base"> subject knowledge base</a> </p> <a href="https://publications.waset.org/abstracts/103587/research-on-construction-of-subject-knowledge-base-based-on-literature-knowledge-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103587.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8945</span> AI Tutor: A Computer Science Domain Knowledge Graph-Based QA System on JADE platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yingqi%20Cui">Yingqi Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Changran%20Huang"> Changran Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Lee"> Raymond Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed an AI Tutor using ontology and natural language process techniques to generate a computer science domain knowledge graph and answer users’ questions based on the knowledge graph. We define eight types of relation to extract relationships between entities according to the computer science domain text. The AI tutor is separated into two agents: learning agent and Question-Answer (QA) agent and developed on JADE (a multi-agent system) platform. The learning agent is responsible for reading text to extract information and generate a corresponding knowledge graph by defined patterns. The QA agent can understand the users’ questions and answer humans’ questions based on the knowledge graph generated by the learning agent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20Language%20processing" title=" natural Language processing"> natural Language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graph" title=" knowledge graph"> knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20agents" title=" intelligent agents"> intelligent agents</a>, <a href="https://publications.waset.org/abstracts/search?q=QA%20system" title=" QA system"> QA system</a> </p> <a href="https://publications.waset.org/abstracts/131977/ai-tutor-a-computer-science-domain-knowledge-graph-based-qa-system-on-jade-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131977.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8944</span> Development of Fuzzy Logic Control Ontology for E-Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Sollehhuddin%20A.%20Jalil">Muhammad Sollehhuddin A. Jalil</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Ibrahim%20Shapiai"> Mohd Ibrahim Shapiai</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubiyah%20Yusof"> Rubiyah Yusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, ontology is common in many areas like artificial intelligence, bioinformatics, e-commerce, education and many more. Ontology is one of the focus areas in the field of Information Retrieval. The purpose of an ontology is to describe a conceptual representation of concepts and their relationships within a particular domain. In other words, ontology provides a common vocabulary for anyone who needs to share information in the domain. There are several ontology domains in various fields including engineering and non-engineering knowledge. However, there are only a few available ontology for engineering knowledge. Fuzzy logic as engineering knowledge is still not available as ontology domain. In general, fuzzy logic requires step-by-step guidelines and instructions of lab experiments. In this study, we presented domain ontology for Fuzzy Logic Control (FLC) knowledge. We give Table of Content (ToC) with middle strategy based on the Uschold and King method to develop FLC ontology. The proposed framework is developed using Protégé as the ontology tool. The Protégé’s ontology reasoner, known as the Pellet reasoner is then used to validate the presented framework. The presented framework offers better performance based on consistency and classification parameter index. In general, this ontology can provide a platform to anyone who needs to understand FLC knowledge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=engineering%20knowledge" title="engineering knowledge">engineering knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic%20control%20ontology" title=" fuzzy logic control ontology"> fuzzy logic control ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20development" title=" ontology development"> ontology development</a>, <a href="https://publications.waset.org/abstracts/search?q=table%20of%20content" title=" table of content"> table of content</a> </p> <a href="https://publications.waset.org/abstracts/54717/development-of-fuzzy-logic-control-ontology-for-e-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54717.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8943</span> A Web-Based Self-Learning Grammar for Spoken Language Understanding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Biondi">S. Biondi</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Catania"> V. Catania</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Di%20Natale"> R. Di Natale</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Intilisano"> A. R. Intilisano</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Panno"> D. Panno</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the major goals of Spoken Dialog Systems (SDS) is to understand what the user utters. In the SDS domain, the Spoken Language Understanding (SLU) Module classifies user utterances by means of a pre-definite conceptual knowledge. The SLU module is able to recognize only the meaning previously included in its knowledge base. Due the vastity of that knowledge, the information storing is a very expensive process. Updating and managing the knowledge base are time-consuming and error-prone processes because of the rapidly growing number of entities like proper nouns and domain-specific nouns. This paper proposes a solution to the problem of Name Entity Recognition (NER) applied to a SDS domain. The proposed solution attempts to automatically recognize the meaning associated with an utterance by using the PANKOW (Pattern based Annotation through Knowledge On the Web) method at runtime. The method being proposed extracts information from the Web to increase the SLU knowledge module and reduces the development effort. In particular, the Google Search Engine is used to extract information from the Facebook social network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spoken%20dialog%20system" title="spoken dialog system">spoken dialog system</a>, <a href="https://publications.waset.org/abstracts/search?q=spoken%20language%20understanding" title=" spoken language understanding"> spoken language understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20semantic" title=" web semantic"> web semantic</a>, <a href="https://publications.waset.org/abstracts/search?q=name%20entity%20recognition" title=" name entity recognition"> name entity recognition</a> </p> <a href="https://publications.waset.org/abstracts/12862/a-web-based-self-learning-grammar-for-spoken-language-understanding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12862.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8942</span> Ontology Mapping with R-GNN for IT Infrastructure: Enhancing Ontology Construction and Knowledge Graph Expansion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20Khalov">Andrey Khalov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth of unstructured data necessitates advanced methods for transforming raw information into structured knowledge, particularly in domain-specific contexts such as IT service management and outsourcing. This paper presents a methodology for automatically constructing domain ontologies using the DOLCE framework as the base ontology. The research focuses on expanding ITIL-based ontologies by integrating concepts from ITSMO, followed by the extraction of entities and relationships from domain-specific texts through transformers and statistical methods like formal concept analysis (FCA). In particular, this work introduces an R-GNN-based approach for ontology mapping, enabling more efficient entity extraction and ontology alignment with existing knowledge bases. Additionally, the research explores transfer learning techniques using pre-trained transformer models (e.g., DeBERTa-v3-large) fine-tuned on synthetic datasets generated via large language models such as LLaMA. The resulting ontology, termed IT Ontology (ITO), is evaluated against existing methodologies, highlighting significant improvements in precision and recall. This study advances the field of ontology engineering by automating the extraction, expansion, and refinement of ontologies tailored to the IT domain, thus bridging the gap between unstructured data and actionable knowledge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ontology%20mapping" title="ontology mapping">ontology mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graphs" title=" knowledge graphs"> knowledge graphs</a>, <a href="https://publications.waset.org/abstracts/search?q=R-GNN" title=" R-GNN"> R-GNN</a>, <a href="https://publications.waset.org/abstracts/search?q=ITIL" title=" ITIL"> ITIL</a>, <a href="https://publications.waset.org/abstracts/search?q=NER" title=" NER"> NER</a> </p> <a href="https://publications.waset.org/abstracts/192575/ontology-mapping-with-r-gnn-for-it-infrastructure-enhancing-ontology-construction-and-knowledge-graph-expansion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192575.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">16</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8941</span> A Framework for Chinese Domain-Specific Distant Supervised Named Entity Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qin%20Long">Qin Long</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Xiaoge"> Li Xiaoge</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distant%20named%20entity%20recognition" title="distant named entity recognition">distant named entity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=entity%20linking" title=" entity linking"> entity linking</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graph" title=" knowledge graph"> knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20neural%20network" title=" graph attention neural network"> graph attention neural network</a> </p> <a href="https://publications.waset.org/abstracts/145772/a-framework-for-chinese-domain-specific-distant-supervised-named-entity-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145772.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8940</span> Epistemic Stance in Chinese Medicine Translation: A Systemic Functional Perspective</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Yue">Yan Yue</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epistemic stance refers to the writer’s judgement about the certainty of the proposition, which demonstrates writer’s degree of commitment and confidence to the status of the information. Epistemic stance can exert great consequence to the validity or reliability of the values of a statement, however, to date, it receives little attention in translations studies, especially from the perspective of systemic functional linguistics (SFL) and with the relation to translator’s domain knowledge. This study is corpus-based research carried out in SFL perspective, which investigates translator’s epistemic stance pattern in Chinese medicine discourse translations by translators with and without medical domain knowledge. Overall, our findings show that all translators tend to be neither too assertive nor too doubted about Chinese medicine statements, and they all tend to express their epistemic stance in a subjective rather than objective way. Individually, there is a clear pattern of epistemic stance marked off by translators’ medical expertise, which further consolidates the previous finding that epistemic asymmetry is found most salient between lay people and professionals. However, contrary to our hypothesis, translators as clinicians who have more medical knowledge are found to be more tentative to TCM statements than translators as non-clinicians. This finding could serve to refine the statements about the relation between writer’s domain knowledge and epistemic stance-taking and the current debate whether Chinese medicine texts should only be translated by clinicians. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epistemic%20stance" title="epistemic stance">epistemic stance</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20knowledge" title=" domain knowledge"> domain knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=SFL" title=" SFL"> SFL</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20translation" title=" medical translation"> medical translation</a> </p> <a href="https://publications.waset.org/abstracts/109106/epistemic-stance-in-chinese-medicine-translation-a-systemic-functional-perspective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8939</span> RASPE: Risk Advisory Smart System for Pipeline Projects in Egypt</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nael%20Y.%20Zabel">Nael Y. Zabel</a>, <a href="https://publications.waset.org/abstracts/search?q=Maged%20E.%20Georgy"> Maged E. Georgy</a>, <a href="https://publications.waset.org/abstracts/search?q=Moheeb%20E.%20Ibrahim"> Moheeb E. Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A knowledge-based expert system with the acronym RASPE is developed as an application tool to help decision makers in construction companies make informed decisions about managing risks in pipeline construction projects. Choosing to use expert systems from all available artificial intelligence techniques is due to the fact that an expert system is more suited to representing a domain’s knowledge and the reasoning behind domain-specific decisions. The knowledge-based expert system can capture the knowledge in the form of conditional rules which represent various project scenarios and potential risk mitigation/response actions. The built knowledge in RASPE is utilized through the underlying inference engine that allows the firing of rules relevant to a project scenario into consideration. This paper provides an overview of the knowledge acquisition process and goes about describing the knowledge structure which is divided up into four major modules. The paper shows one module in full detail for illustration purposes and concludes with insightful remarks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title="expert system">expert system</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=pipeline%20projects" title=" pipeline projects"> pipeline projects</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20mismanagement" title=" risk mismanagement"> risk mismanagement</a> </p> <a href="https://publications.waset.org/abstracts/16022/raspe-risk-advisory-smart-system-for-pipeline-projects-in-egypt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16022.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">312</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8938</span> An Unsupervised Domain-Knowledge Discovery Framework for Fake News Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yulan%20Wu">Yulan Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fake%20news" title="fake news">fake news</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20domains" title=" multiple domains"> multiple domains</a> </p> <a href="https://publications.waset.org/abstracts/167789/an-unsupervised-domain-knowledge-discovery-framework-for-fake-news-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167789.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8937</span> An Overview of Domain Models of Urban Quantitative Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohan%20Li">Mohan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, intelligent research technology is more and more important than traditional research methods in urban research work, and this proportion will greatly increase in the next few decades. Frequently such analyzing work cannot be carried without some software engineering knowledge. And here, domain models of urban research will be necessary when applying software engineering knowledge to urban work. In many urban plan practice projects, making rational models, feeding reliable data, and providing enough computation all make indispensable assistance in producing good urban planning. During the whole work process, domain models can optimize workflow design. At present, human beings have entered the era of big data. The amount of digital data generated by cities every day will increase at an exponential rate, and new data forms are constantly emerging. How to select a suitable data set from the massive amount of data, manage and process it has become an ability that more and more planners and urban researchers need to possess. This paper summarizes and makes predictions of the emergence of technologies and technological iterations that may affect urban research in the future, discover urban problems, and implement targeted sustainable urban strategies. They are summarized into seven major domain models. They are urban and rural regional domain model, urban ecological domain model, urban industry domain model, development dynamic domain model, urban social and cultural domain model, urban traffic domain model, and urban space domain model. These seven domain models can be used to guide the construction of systematic urban research topics and help researchers organize a series of intelligent analytical tools, such as Python, R, GIS, etc. These seven models make full use of quantitative spatial analysis, machine learning, and other technologies to achieve higher efficiency and accuracy in urban research, assisting people in making reasonable decisions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20model" title=" domain model"> domain model</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20planning" title=" urban planning"> urban planning</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20quantitative%20analysis" title=" urban quantitative analysis"> urban quantitative analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=workflow%20design" title=" workflow design"> workflow design</a> </p> <a href="https://publications.waset.org/abstracts/135455/an-overview-of-domain-models-of-urban-quantitative-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135455.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8936</span> Knowledge Diffusion via Automated Organizational Cartography (Autocart)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Kehal">Mounir Kehal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The post-globalization epoch has placed businesses everywhere in new and different competitive situations where knowledgeable, effective and efficient behavior has come to provide the competitive and comparative edge. Enterprises have turned to explicit - and even conceptualizing on tacit - knowledge management to elaborate a systematic approach to develop and sustain the intellectual capital needed to succeed. To be able to do that, you have to be able to visualize your organization as consisting of nothing but knowledge and knowledge flows, whilst being presented in a graphical and visual framework, referred to as automated organizational cartography. Hence, creating the ability of further actively classifying existing organizational content evolving from and within data feeds, in an algorithmic manner, potentially giving insightful schemes and dynamics by which organizational know-how is visualized. It is discussed and elaborated on most recent and applicable definitions and classifications of knowledge management, representing a wide range of views from mechanistic (systematic, data driven) to a more socially (psychologically, cognitive/metadata driven) orientated. More elaborate continuum models, for knowledge acquisition and reasoning purposes, are being used for effectively representing the domain of information that an end user may contain in their decision making process for utilization of available organizational intellectual resources (i.e. Autocart). In this paper, we present an empirical research study conducted previously to try and explore knowledge diffusion in a specialist knowledge domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title="knowledge management">knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20maps" title=" knowledge maps"> knowledge maps</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20diffusion" title=" knowledge diffusion"> knowledge diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20cartography" title=" organizational cartography"> organizational cartography</a> </p> <a href="https://publications.waset.org/abstracts/6016/knowledge-diffusion-via-automated-organizational-cartography-autocart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6016.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8935</span> Domain specific Ontology-Based Knowledge Extraction Using R-GNN and Large Language Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20Khalov">Andrey Khalov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid proliferation of unstructured data in IT infrastructure management demands innovative approaches for extracting actionable knowledge. This paper presents a framework for ontology-based knowledge extraction that combines relational graph neural networks (R-GNN) with large language models (LLMs). The proposed method leverages the DOLCE framework as the foundational ontology, extending it with concepts from ITSMO for domain-specific applications in IT service management and outsourcing. A key component of this research is the use of transformer-based models, such as DeBERTa-v3-large, for automatic entity and relationship extraction from unstructured texts. Furthermore, the paper explores how transfer learning techniques can be applied to fine-tune large language models (LLaMA) for using to generate synthetic datasets to improve precision in BERT-based entity recognition and ontology alignment. The resulting IT Ontology (ITO) serves as a comprehensive knowledge base that integrates domain-specific insights from ITIL processes, enabling more efficient decision-making. Experimental results demonstrate significant improvements in knowledge extraction and relationship mapping, offering a cutting-edge solution for enhancing cognitive computing in IT service environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ontology%20mapping" title="ontology mapping">ontology mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=R-GNN" title=" R-GNN"> R-GNN</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20extraction" title=" knowledge extraction"> knowledge extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20language%20models" title=" large language models"> large language models</a>, <a href="https://publications.waset.org/abstracts/search?q=NER" title=" NER"> NER</a>, <a href="https://publications.waset.org/abstracts/search?q=knowlege%20graph" title=" knowlege graph"> knowlege graph</a> </p> <a href="https://publications.waset.org/abstracts/192578/domain-specific-ontology-based-knowledge-extraction-using-r-gnn-and-large-language-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192578.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">16</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8934</span> Knowledge Diffusion via Automated Organizational Cartography: Autocart</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Kehal">Mounir Kehal</a>, <a href="https://publications.waset.org/abstracts/search?q=Adel%20Al%20Araifi"> Adel Al Araifi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The post-globalisation epoch has placed businesses everywhere in new and different competitive situations where knowledgeable, effective and efficient behaviour has come to provide the competitive and comparative edge. Enterprises have turned to explicit- and even conceptualising on tacit- Knowledge Management to elaborate a systematic approach to develop and sustain the Intellectual Capital needed to succeed. To be able to do that, you have to be able to visualize your organization as consisting of nothing but knowledge and knowledge flows, whilst being presented in a graphical and visual framework, referred to as automated organizational cartography. Hence, creating the ability of further actively classifying existing organizational content evolving from and within data feeds, in an algorithmic manner, potentially giving insightful schemes and dynamics by which organizational know-how is visualised. It is discussed and elaborated on most recent and applicable definitions and classifications of knowledge management, representing a wide range of views from mechanistic (systematic, data driven) to a more socially (psychologically, cognitive/metadata driven) orientated. More elaborate continuum models, for knowledge acquisition and reasoning purposes, are being used for effectively representing the domain of information that an end user may contain in their decision making process for utilization of available organizational intellectual resources (i.e. Autocart). In this paper we present likewise an empirical research study conducted previously to try and explore knowledge diffusion in a specialist knowledge domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title="knowledge management">knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20maps" title=" knowledge maps"> knowledge maps</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20diffusion" title=" knowledge diffusion"> knowledge diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=organizational%20cartography" title=" organizational cartography"> organizational cartography</a> </p> <a href="https://publications.waset.org/abstracts/27145/knowledge-diffusion-via-automated-organizational-cartography-autocart" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27145.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">417</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8933</span> An Approach for Association Rules Ranking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rihab%20Idoudi">Rihab Idoudi</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Saheb%20Ettabaa"> Karim Saheb Ettabaa</a>, <a href="https://publications.waset.org/abstracts/search?q=Basel%20Solaiman"> Basel Solaiman</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Hamrouni"> Kamel Hamrouni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Medical association rules induction is used to discover useful correlations between pertinent concepts from large medical databases. Nevertheless, ARs algorithms produce huge amount of delivered rules and do not guarantee the usefulness and interestingness of the generated knowledge. To overcome this drawback, we propose an ontology based interestingness measure for ARs ranking. According to domain expert, the goal of the use of ARs is to discover implicit relationships between items of different categories such as ‘clinical features and disorders’, ‘clinical features and radiological observations’, etc. That’s to say, the itemsets which are composed of ‘similar’ items are uninteresting. Therefore, the dissimilarity between the rule’s items can be used to judge the interestingness of association rules; the more different are the items, the more interesting the rule is. In this paper, we design a distinct approach for ranking semantically interesting association rules involving the use of an ontology knowledge mining approach. The basic idea is to organize the ontology’s concepts into a hierarchical structure of conceptual clusters of targeted subjects, where each cluster encapsulates ‘similar’ concepts suggesting a specific category of the domain knowledge. The interestingness of association rules is, then, defined as the dissimilarity between corresponding clusters. That is to say, the further are the clusters of the items in the AR, the more interesting the rule is. We apply the method in our domain of interest – mammographic domain- using an existing mammographic ontology called Mammo with the goal of deriving interesting rules from past experiences, to discover implicit relationships between concepts modeling the domain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=association%20rule" title="association rule">association rule</a>, <a href="https://publications.waset.org/abstracts/search?q=conceptual%20clusters" title=" conceptual clusters"> conceptual clusters</a>, <a href="https://publications.waset.org/abstracts/search?q=interestingness%20measures" title=" interestingness measures"> interestingness measures</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20knowledge%20mining" title=" ontology knowledge mining"> ontology knowledge mining</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking" title=" ranking"> ranking</a> </p> <a href="https://publications.waset.org/abstracts/47971/an-approach-for-association-rules-ranking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47971.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">322</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8932</span> Knowledge Elicitation Approach for Formal Ontology Design: An Exploratory Study Applied in Industry for Knowledge Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ouassila%20Labbani-Narsis">Ouassila Labbani-Narsis</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Nicolle"> Christophe Nicolle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Building formal ontologies remains a complex process for companies. In the literature, this process is based on the technical knowledge and expertise of domain experts, without further details on the used methodologies. Possible problems of disagreements between experts, expression of tacit knowledge related to high level know-how rarely verbalized, qualification of results by using cases, or simply adhesion of the group of experts, remain currently unsolved. This paper proposes a methodological approach based on knowledge elicitation for the conception of formal, consensual, and shared ontologies. The proposed approach is experimentally tested on industrial collaboration projects in the field of manufacturing (associating knowledge sources from multinational companies) and in the field of viticulture (associating explicit knowledge and implicit knowledge acquired through observation). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collaborative%20ontology%20engineering" title="collaborative ontology engineering">collaborative ontology engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20elicitation" title=" knowledge elicitation"> knowledge elicitation</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20engineering" title=" knowledge engineering"> knowledge engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/160107/knowledge-elicitation-approach-for-formal-ontology-design-an-exploratory-study-applied-in-industry-for-knowledge-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8931</span> Comparison of Frequency-Domain Contention Schemes in Wireless LANs </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Feng">Li Feng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In IEEE 802.11 networks, it is well known that the traditional time-domain contention often leads to low channel utilization. The first frequency-domain contention scheme, the time to frequency (T2F), has recently been proposed to improve the channel utilization and has attracted a great deal of attention. In this paper, we survey the latest research progress on the weighed frequency-domain contention. We present the basic ideas, work principles of these related schemes and point out their differences. This paper is very useful for further study on frequency-domain contention. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=802.11" title="802.11">802.11</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20LANs" title=" wireless LANs"> wireless LANs</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency-domain%20contention" title=" frequency-domain contention"> frequency-domain contention</a>, <a href="https://publications.waset.org/abstracts/search?q=T2F" title=" T2F"> T2F</a> </p> <a href="https://publications.waset.org/abstracts/42959/comparison-of-frequency-domain-contention-schemes-in-wireless-lans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42959.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">459</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8930</span> Investigating the Interaction of Individuals' Knowledge Sharing Constructs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eugene%20Okyere-Kwakye">Eugene Okyere-Kwakye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowledge sharing is a practice where individuals commonly exchange both tacit and explicit knowledge to jointly create a new knowledge. Knowledge management literature vividly express that knowledge sharing is the keystone and perhaps it is the most important aspect of knowledge management. To enhance the understanding of knowledge sharing domain, this study is aimed to investigate some factors that could influence employee’s attitude and behaviour to share their knowledge. The researchers employed the social exchange theory as a theoretical foundation for this study. Three essential factors namely: Trust, mutual reciprocity and perceived enjoyment that could influence knowledge sharing behaviour has been incorporated into a research model. To empirically validate this model, data was collected from one hundred and twenty respondents. The multiple regression analysis was employed to analyse the data. The results indicate that perceived enjoyment and trust have a significant influence on knowledge sharing. Surprisingly, mutual reciprocity did not influence knowledge sharing. The paper concludes by highlight the practical implications of the findings and areas for future research to consider. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=perceived%20enjoyment" title="perceived enjoyment">perceived enjoyment</a>, <a href="https://publications.waset.org/abstracts/search?q=trust" title=" trust"> trust</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20sharing" title=" knowledge sharing"> knowledge sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/4311/investigating-the-interaction-of-individuals-knowledge-sharing-constructs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4311.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8929</span> Domain-Specific Languages Evaluation: A Literature Review and Experience Report</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sofia%20Meacham">Sofia Meacham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this abstract paper, the Domain-Specific Languages (DSL) evaluation will be presented based on existing literature and years of experience developing DSLs for several domains. The domains we worked on ranged from AI, business applications, and finances/accounting to health. In general, DSLs have been utilised in many domains to provide tailored and efficient solutions to address specific problems. Although they are a reputable method among highly technical circles and have also been used by non-technical experts with success, according to our knowledge, there isn’t a commonly accepted method for evaluating them. There are some methods that define criteria that are adaptations from the general software engineering quality criteria. Other literature focuses on the DSL usability aspect of evaluation and applies methods such as Human-Computer Interaction (HCI) and goal modeling. All these approaches are either hard to introduce, such as the goal modeling, or seem to ignore the domain-specific focus of the DSLs. From our experience, the DSLs have domain-specificity in their core, and consequently, the methods to evaluate them should also include domain-specific criteria in their core. The domain-specific criteria would require synergy between the domain experts and the DSL developers in the same way that DSLs cannot be developed without domain-experts involvement. Methods from agile and other software engineering practices, such as co-creation workshops, should be further emphasised and explored to facilitate this direction. Concluding, our latest experience and plans for DSLs evaluation will be presented and open for discussion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=domain-specific%20languages" title="domain-specific languages">domain-specific languages</a>, <a href="https://publications.waset.org/abstracts/search?q=DSL%20evaluation" title=" DSL evaluation"> DSL evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=DSL%20usability" title=" DSL usability"> DSL usability</a>, <a href="https://publications.waset.org/abstracts/search?q=DSL%20quality%20metrics" title=" DSL quality metrics"> DSL quality metrics</a> </p> <a href="https://publications.waset.org/abstracts/163949/domain-specific-languages-evaluation-a-literature-review-and-experience-report" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163949.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">103</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8928</span> Method of Visual Prosthesis Design Based on Biologically Inspired Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shen%20Jian">Shen Jian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hu%20Jie"> Hu Jie</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhu%20Guo%20Niu"> Zhu Guo Niu</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Ying%20Hong"> Peng Ying Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are two issues exited in the traditional visual prosthesis: lacking systematic method and the low level of humanization. To tackcle those obstacles, a visual prosthesis design method based on biologically inspired design is proposed. Firstly, a constrained FBS knowledge cell model is applied to construct the functional model of visual prosthesis in biological field. Then the clustering results of engineering domain are ob-tained with the use of the cross-domain knowledge cell clustering algorithm. Finally, a prototype system is designed to support the bio-logically inspired design where the conflict is digested by TRIZ and other tools, and the validity of the method is verified by the solution scheme <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge-based%20engineering" title="knowledge-based engineering">knowledge-based engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20prosthesis" title=" visual prosthesis"> visual prosthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=biologically%20inspired%20design" title=" biologically inspired design"> biologically inspired design</a>, <a href="https://publications.waset.org/abstracts/search?q=biomedical%20engineering" title=" biomedical engineering"> biomedical engineering</a> </p> <a href="https://publications.waset.org/abstracts/87977/method-of-visual-prosthesis-design-based-on-biologically-inspired-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87977.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">192</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8927</span> Medical Knowledge Management since the Integration of Heterogeneous Data until the Knowledge Exploitation in a Decision-Making System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadjat%20Zerf%20Boudjettou">Nadjat Zerf Boudjettou</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahima%20Nader"> Fahima Nader</a>, <a href="https://publications.waset.org/abstracts/search?q=Rachid%20Chalal"> Rachid Chalal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowledge management is to acquire and represent knowledge relevant to a domain, a task or a specific organization in order to facilitate access, reuse and evolution. This usually means building, maintaining and evolving an explicit representation of knowledge. The next step is to provide access to that knowledge, that is to say, the spread in order to enable effective use. Knowledge management in the medical field aims to improve the performance of the medical organization by allowing individuals in the care facility (doctors, nurses, paramedics, etc.) to capture, share and apply collective knowledge in order to make optimal decisions in real time. In this paper, we propose a knowledge management approach based on integration technique of heterogeneous data in the medical field by creating a data warehouse, a technique of extracting knowledge from medical data by choosing a technique of data mining, and finally an exploitation technique of that knowledge in a case-based reasoning system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20warehouse" title="data warehouse">data warehouse</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20discovery%20in%20database" title=" knowledge discovery in database"> knowledge discovery in database</a>, <a href="https://publications.waset.org/abstracts/search?q=KDD" title=" KDD"> KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20knowledge%20management" title=" medical knowledge management"> medical knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20networks" title=" Bayesian networks"> Bayesian networks</a> </p> <a href="https://publications.waset.org/abstracts/14543/medical-knowledge-management-since-the-integration-of-heterogeneous-data-until-the-knowledge-exploitation-in-a-decision-making-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14543.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8926</span> Omni-Modeler: Dynamic Learning for Pedestrian Redetection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20Karnes">Michael Karnes</a>, <a href="https://publications.waset.org/abstracts/search?q=Alper%20Yilmaz"> Alper Yilmaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dynamic%20learning" title="dynamic learning">dynamic learning</a>, <a href="https://publications.waset.org/abstracts/search?q=few-shot%20learning" title=" few-shot learning"> few-shot learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20redetection" title=" pedestrian redetection"> pedestrian redetection</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20recognition" title=" visual recognition"> visual recognition</a> </p> <a href="https://publications.waset.org/abstracts/172265/omni-modeler-dynamic-learning-for-pedestrian-redetection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172265.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">76</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8925</span> Domain Switching Characteristics of Lead Zirconate Titanate Piezoelectric Ceramic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitsuhiro%20Okayasu">Mitsuhiro Okayasu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To better understand the lattice characteristics of lead zirconate titanate (PZT) ceramics, the lattice orientations and domain-switching characteristics have been directly examined during loading and unloading using various experimental techniques. Upon loading, the PZT ceramics are fractured linear and nonlinearly during the compressive loading process. The strain characteristics of the PZT ceramic were directly affected by both the lattice and domain switching strain. Due to the piezoelectric ceramic, electrical activity of lightning-like behavior occurs in the PZT ceramics, which attributed to the severe domain-switching leading to weak piezoelectric property. The characteristics of domain-switching and reverse switching are detected during the loading and unloading processes. The amount of domain-switching depends on the grain, due to different stress levels. In addition, two patterns of 90˚ domain-switching systems are characterized, namely (i) 90˚ turn about the tetragonal c-axis and (ii) 90˚ rotation of the tetragonal a-axis. In this case, PZT ceramic was loaded by the thermal stress at 80°C. Extent of domain switching is related to the direction of c-axis of the tetragonal structure, e.g., that axis, orientated close to the loading direction, makes severe domain switching. It is considered that there is 90˚ domain switching, but in actual, the angle of domain switching is less than 90˚, e.g., 85.4° ~ 90.0°. In situ TEM observation of the domain switching characteristics of PZT ceramic has been conducted with increasing the sample temperature from 25°C to 300°C, and the domain switching like behavior is directly observed from the lattice image, where the severe domain switching occurs less than 100°C. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PZT" title="PZT">PZT</a>, <a href="https://publications.waset.org/abstracts/search?q=lead%20zirconate%20titanate" title=" lead zirconate titanate"> lead zirconate titanate</a>, <a href="https://publications.waset.org/abstracts/search?q=piezoelectric%20ceramic" title=" piezoelectric ceramic"> piezoelectric ceramic</a>, <a href="https://publications.waset.org/abstracts/search?q=domain%20switching" title=" domain switching"> domain switching</a>, <a href="https://publications.waset.org/abstracts/search?q=material%20property" title=" material property"> material property</a> </p> <a href="https://publications.waset.org/abstracts/89317/domain-switching-characteristics-of-lead-zirconate-titanate-piezoelectric-ceramic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89317.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8924</span> Conformational Switch of hRAGE upon Self-Association</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ikhlas%20Ahmed">Ikhlas Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Jamillah%20Zamoon"> Jamillah Zamoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The human receptor for advanced glycation end product is a plasma membrane receptor with an intrinsically disordered region. The protein consists of three extracellular domains, a single membrane spanning transmembrane domain, and a cytosolic domain which is intrinsically disordered and responsible for signaling. The disordered nature of the cytosolic domain allows it to be dynamic in solution. This receptor self-associates to higher forms. The association is triggered by ligand, metal or by the extracellular domain. Fluorescence spectroscopy technique is used to test the self-association of the different concentrations of the cytosolic domain. This work has concluded that the cytosolic domain of this receptor also self-associates. Moreover, the self-association does not require ligand or metal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fluorescence%20spectroscopy" title="fluorescence spectroscopy">fluorescence spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=hRAGE" title=" hRAGE"> hRAGE</a>, <a href="https://publications.waset.org/abstracts/search?q=IDP" title=" IDP"> IDP</a>, <a href="https://publications.waset.org/abstracts/search?q=Self-association" title=" Self-association"> Self-association</a> </p> <a href="https://publications.waset.org/abstracts/44509/conformational-switch-of-hrage-upon-self-association" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44509.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">361</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8923</span> Business Domain Modelling Using an Integrated Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Hasan%20Salahat">Mohammed Hasan Salahat</a>, <a href="https://publications.waset.org/abstracts/search?q=Stave%20Wade"> Stave Wade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modeling Language (UML), and an implementation pattern knows as ‘Naked Objects’. This framework have been used in action research projects that have involved the investigation and modeling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study ‘Information Retrieval System for Academic Research’ is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modeling. The framework is overviewed and justified as multi-methodology using Mingers Multi-Methodology ideas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SSM" title="SSM">SSM</a>, <a href="https://publications.waset.org/abstracts/search?q=UML" title=" UML"> UML</a>, <a href="https://publications.waset.org/abstracts/search?q=domain-driven%20design" title=" domain-driven design"> domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20domain-driven%20design" title=" soft domain-driven design"> soft domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=naked%20objects" title=" naked objects"> naked objects</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20language" title=" soft language"> soft language</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=multimethodology" title=" multimethodology"> multimethodology</a> </p> <a href="https://publications.waset.org/abstracts/32073/business-domain-modelling-using-an-integrated-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">560</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8922</span> Kannudi- A Reference Editor for Kannada (Based on OPOK! and OHOK! Principles, and Domain Knowledge)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishweshwar%20V.%20Dixit">Vishweshwar V. Dixit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Kannudi is a reference editor introducing a method of input for Kannada, called OHOK!, that is, Ottu Hāku Ottu Koḍu!. This is especially suited for pressure-sensitive input devices, though the current online implementation uses the regular mechanical keyboard. OHOK! has three possible modes, namely, sva-ottu (self-conjunct), kandante (as you see), and andante (as you say). It may be noted that kandante mode does not follow the phonetic order. However, this model may work well for those who are inclined to visualize as they type rather than vocalize the sounds. Kannudi also demonstrates how domain knowledge can be effectively used to potentially increase speed, accuracy, and user-friendliness. For example, selection of a default vowel, automatic shunyification, and arkification. Also implemented are four types of Deletes that are necessary for phono-syllabic languages like Kannada. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=kannada" title="kannada">kannada</a>, <a href="https://publications.waset.org/abstracts/search?q=conjunct" title=" conjunct"> conjunct</a>, <a href="https://publications.waset.org/abstracts/search?q=reference%20editor" title=" reference editor"> reference editor</a>, <a href="https://publications.waset.org/abstracts/search?q=pressure%20input" title=" pressure input"> pressure input</a> </p> <a href="https://publications.waset.org/abstracts/153904/kannudi-a-reference-editor-for-kannada-based-on-opok-and-ohok-principles-and-domain-knowledge" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153904.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8921</span> Domain Driven Design vs Soft Domain Driven Design Frameworks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Salahat">Mohammed Salahat</a>, <a href="https://publications.waset.org/abstracts/search?q=Steve%20Wade"> Steve Wade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents and compares the SSDDD “Systematic Soft Domain Driven Design Framework” to DDD “Domain Driven Design Framework” as a soft system approach of information systems development. The framework use SSM as a guiding methodology within which we have embedded a sequence of design tasks based on the UML leading to the implementation of a software system using the Naked Objects framework. This framework has been used in action research projects that have involved the investigation and modelling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, a comparison between SSDDD and DDD is presented in this paper, to show how SSDDD improved DDD as an approach to modelling and implementing business domain perspectives for Information Systems Development. The comparison process, the results, and the improvements are presented in the following sections of this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=domain-driven%20design" title="domain-driven design">domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20domain-driven%20design" title=" soft domain-driven design"> soft domain-driven design</a>, <a href="https://publications.waset.org/abstracts/search?q=naked%20objects" title=" naked objects"> naked objects</a>, <a href="https://publications.waset.org/abstracts/search?q=soft%20language" title=" soft language"> soft language</a> </p> <a href="https://publications.waset.org/abstracts/53604/domain-driven-design-vs-soft-domain-driven-design-frameworks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53604.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">298</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8920</span> Conceptualizing the Knowledge to Manage and Utilize Data Assets in the Context of Digitization: Case Studies of Multinational Industrial Enterprises</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20B%C3%B6hmer">Martin Böhmer</a>, <a href="https://publications.waset.org/abstracts/search?q=Agatha%20Dabrowski"> Agatha Dabrowski</a>, <a href="https://publications.waset.org/abstracts/search?q=Boris%20Otto"> Boris Otto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20management" title="data management">data management</a>, <a href="https://publications.waset.org/abstracts/search?q=digitization" title=" digitization"> digitization</a>, <a href="https://publications.waset.org/abstracts/search?q=industry%204.0" title=" industry 4.0"> industry 4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20engineering" title=" knowledge engineering"> knowledge engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=metamodel" title=" metamodel"> metamodel</a> </p> <a href="https://publications.waset.org/abstracts/65473/conceptualizing-the-knowledge-to-manage-and-utilize-data-assets-in-the-context-of-digitization-case-studies-of-multinational-industrial-enterprises" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20domain&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=knowledge%20domain&page=3">3</a></li> <li class="page-item"><a class="page-link" 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