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class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3182</span> Emotional Analysis for Text Search Queries on Internet</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gemma%20Garc%C3%ADa%20L%C3%B3pez">Gemma García López</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this study is to analyze if search queries carried out in search engines such as Google, can offer emotional information about the user that performs them. Knowing the emotional state in which the Internet user is located can be a key to achieve the maximum personalization of content and the detection of worrying behaviors. For this, two studies were carried out using tools with advanced natural language processing techniques. The first study determines if a query can be classified as positive, negative or neutral, while the second study extracts emotional content from words and applies the categorical and dimensional models for the representation of emotions. In addition, we use search queries in Spanish and English to establish similarities and differences between two languages. The results revealed that text search queries performed by users on the Internet can be classified emotionally. This allows us to better understand the emotional state of the user at the time of the search, which could involve adapting the technology and personalizing the responses to different emotional states. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20classification" title="emotion classification">emotion classification</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20search%20queries" title=" text search queries"> text search queries</a>, <a href="https://publications.waset.org/abstracts/search?q=emotional%20analysis" title=" emotional analysis"> emotional analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis%20in%20text" title=" sentiment analysis in text"> sentiment analysis in text</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/98327/emotional-analysis-for-text-search-queries-on-internet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98327.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">141</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">3181</span> Interactive, Topic-Oriented Search Support by a Centroid-Based Text Categorisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Kubek">Mario Kubek</a>, <a href="https://publications.waset.org/abstracts/search?q=Herwig%20Unger"> Herwig Unger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Centroid terms are single words that semantically and topically characterise text documents and so may serve as their very compact representation in automatic text processing. In the present paper, centroids are used to measure the relevance of text documents with respect to a given search query. Thus, a new graphbased paradigm for searching texts in large corpora is proposed and evaluated against keyword-based methods. The first, promising experimental results demonstrate the usefulness of the centroid-based search procedure. It is shown that especially the routing of search queries in interactive and decentralised search systems can be greatly improved by applying this approach. A detailed discussion on further fields of its application completes this contribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=search%20algorithm" title="search algorithm">search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=centroid" title=" centroid"> centroid</a>, <a href="https://publications.waset.org/abstracts/search?q=query" title=" query"> query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=co-occurrence" title=" co-occurrence"> co-occurrence</a>, <a href="https://publications.waset.org/abstracts/search?q=categorisation" title=" categorisation"> categorisation</a> </p> <a href="https://publications.waset.org/abstracts/82581/interactive-topic-oriented-search-support-by-a-centroid-based-text-categorisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82581.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">282</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">3180</span> Use of Interpretable Evolved Search Query Classifiers for Sinhala Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prasanna%20Haddela">Prasanna Haddela</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolved%20search%20queries" title="evolved search queries">evolved search queries</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinhala%20document%20classification" title=" Sinhala document classification"> Sinhala document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Lucene%20Sinhala%20analyzer" title=" Lucene Sinhala analyzer"> Lucene Sinhala analyzer</a>, <a href="https://publications.waset.org/abstracts/search?q=interpretable%20text%20classification" title=" interpretable text classification"> interpretable text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/126324/use-of-interpretable-evolved-search-query-classifiers-for-sinhala-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126324.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">114</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">3179</span> General Time-Dependent Sequenced Route Queries in Road Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hossein%20Ahmadi">Mohammad Hossein Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Haghighatdoost"> Vahid Haghighatdoost</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial databases have been an active area of research over years. In this paper, we study how to answer the General Time-Dependent Sequenced Route queries. Given the origin and destination of a user over a time-dependent road network graph, an ordered list of categories of interests and a departure time interval, our goal is to find the minimum travel time path along with the best departure time that minimizes the total travel time from the source location to the given destination passing through a sequence of points of interests belonging to each of the specified categories of interest. The challenge of this problem is the added complexity to the optimal sequenced route queries, where we assume that first the road network is time dependent, and secondly the user defines a departure time interval instead of one single departure time instance. For processing general time-dependent sequenced route queries, we propose two solutions as Discrete-Time and Continuous-Time Sequenced Route approaches, finding approximate and exact solutions, respectively. Our proposed approaches traverse the road network based on A*-search paradigm equipped with an efficient heuristic function, for shrinking the search space. Extensive experiments are conducted to verify the efficiency of our proposed approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trip%20planning" title="trip planning">trip planning</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20dependent" title=" time dependent"> time dependent</a>, <a href="https://publications.waset.org/abstracts/search?q=sequenced%20route%20query" title=" sequenced route query"> sequenced route query</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20networks" title=" road networks"> road networks</a> </p> <a href="https://publications.waset.org/abstracts/72792/general-time-dependent-sequenced-route-queries-in-road-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72792.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">321</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">3178</span> On the Interactive Search with Web Documents </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Kubek">Mario Kubek</a>, <a href="https://publications.waset.org/abstracts/search?q=Herwig%20Unger"> Herwig Unger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the large amount of information in the World Wide Web (WWW, web) and the lengthy and usually linearly ordered result lists of web search engines that do not indicate semantic relationships between their entries, the search for topically similar and related documents can become a tedious task. Especially, the process of formulating queries with proper terms representing specific information needs requires much effort from the user. This problem gets even bigger when the user's knowledge on a subject and its technical terms is not sufficient enough to do so. This article presents the new and interactive search application DocAnalyser that addresses this problem by enabling users to find similar and related web documents based on automatic query formulation and state-of-the-art search word extraction. Additionally, this tool can be used to track topics across semantically connected web documents <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DocAnalyser" title="DocAnalyser">DocAnalyser</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20web%20search" title=" interactive web search"> interactive web search</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20word%20extraction" title=" search word extraction"> search word extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20formulation" title=" query formulation"> query formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20topic%20detection" title=" source topic detection"> source topic detection</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20tracking" title=" topic tracking "> topic tracking </a> </p> <a href="https://publications.waset.org/abstracts/17687/on-the-interactive-search-with-web-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17687.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">393</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">3177</span> Computing Continuous Skyline Queries without Discriminating between Static and Dynamic Attributes </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Gomaa">Ibrahim Gomaa</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20M.%20O.%20Mokhtar"> Hoda M. O. Mokhtar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although most of the existing skyline queries algorithms focused basically on querying static points through static databases; with the expanding number of sensors, wireless communications and mobile applications, the demand for continuous skyline queries has increased. Unlike traditional skyline queries which only consider static attributes, continuous skyline queries include dynamic attributes, as well as the static ones. However, as skyline queries computation is based on checking the domination of skyline points over all dimensions, considering both the static and dynamic attributes without separation is required. In this paper, we present an efficient algorithm for computing continuous skyline queries without discriminating between static and dynamic attributes. Our algorithm in brief proceeds as follows: First, it excludes the points which will not be in the initial skyline result; this pruning phase reduces the required number of comparisons. Second, the association between the spatial positions of data points is examined; this phase gives an idea of where changes in the result might occur and consequently enables us to efficiently update the skyline result (continuous update) rather than computing the skyline from scratch. Finally, experimental evaluation is provided which demonstrates the accuracy, performance and efficiency of our algorithm over other existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=continuous%20query%20processing" title="continuous query processing">continuous query processing</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20database" title=" dynamic database"> dynamic database</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20object" title=" moving object"> moving object</a>, <a href="https://publications.waset.org/abstracts/search?q=skyline%20queries" title=" skyline queries"> skyline queries</a> </p> <a href="https://publications.waset.org/abstracts/54845/computing-continuous-skyline-queries-without-discriminating-between-static-and-dynamic-attributes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54845.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">210</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">3176</span> Unsupervised Domain Adaptive Text Retrieval with Query Generation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rui%20Yin">Rui Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Haojie%20Wang"> Haojie Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xun%20Li"> Xun Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, mainstream dense retrieval methods have obtained state-of-the-art results on some datasets and tasks. However, they require large amounts of training data, which is not available in most domains. The severe performance degradation of dense retrievers on new data domains has limited the use of dense retrieval methods to only a few domains with large training datasets. In this paper, we propose an unsupervised domain-adaptive approach based on query generation. First, a generative model is used to generate relevant queries for each passage in the target corpus, and then the generated queries are used for mining negative passages. Finally, the query-passage pairs are labeled with a cross-encoder and used to train a domain-adapted dense retriever. Experiments show that our approach is more robust than previous methods in target domains that require less unlabeled data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dense%20retrieval" title="dense retrieval">dense retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20generation" title=" query generation"> query generation</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20training" title=" unsupervised training"> unsupervised training</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20retrieval" title=" text retrieval"> text retrieval</a> </p> <a href="https://publications.waset.org/abstracts/173903/unsupervised-domain-adaptive-text-retrieval-with-query-generation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173903.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">3175</span> Enhanced Arabic Semantic Information Retrieval System Based on Arabic Text Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Elsehemy">A. Elsehemy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdeen"> M. Abdeen </a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Nazmy"> T. Nazmy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20text%20classification" title="Arabic text classification">Arabic text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20based%20retrieval" title=" ontology based retrieval"> ontology based retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20semantic%20web" title=" Arabic semantic web"> Arabic semantic web</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=Arabic%20ontology" title=" Arabic ontology"> Arabic ontology</a> </p> <a href="https://publications.waset.org/abstracts/34945/enhanced-arabic-semantic-information-retrieval-system-based-on-arabic-text-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34945.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">526</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">3174</span> A Framework of Product Information Service System Using Mobile Image Retrieval and Text Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mei-Yi%20Wu">Mei-Yi Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shang-Ming%20Huang"> Shang-Ming Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The online shoppers nowadays often search the product information on the Internet using some keywords of products. To use this kind of information searching model, shoppers should have a preliminary understanding about their interesting products and choose the correct keywords. However, if the products are first contact (for example, the worn clothes or backpack of passengers which you do not have any idea about the brands), these products cannot be retrieved due to insufficient information. In this paper, we discuss and study the applications in E-commerce using image retrieval and text mining techniques. We design a reasonable E-commerce application system containing three layers in the architecture to provide users product information. The system can automatically search and retrieval similar images and corresponding web pages on Internet according to the target pictures which taken by users. Then text mining techniques are applied to extract important keywords from these retrieval web pages and search the prices on different online shopping stores with these keywords using a web crawler. Finally, the users can obtain the product information including photos and prices of their favorite products. The experiments shows the efficiency of proposed system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20image%20retrieval" title="mobile image retrieval">mobile image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20information%20service%20system" title=" product information service system"> product information service system</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20marketing" title=" online marketing"> online marketing</a> </p> <a href="https://publications.waset.org/abstracts/33483/a-framework-of-product-information-service-system-using-mobile-image-retrieval-and-text-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33483.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">359</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">3173</span> Interactive Image Search for Mobile Devices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Komal%20V.%20Aher">Komal V. Aher</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjay%20B.%20Waykar"> Sanjay B. Waykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays every individual having mobile device with them. In both computer vision and information retrieval Image search is currently hot topic with many applications. The proposed intelligent image search system is fully utilizing multimodal and multi-touch functionalities of smart phones which allows search with Image, Voice, and Text on mobile phones. The system will be more useful for users who already have pictures in their minds but have no proper descriptions or names to address them. The paper gives system with ability to form composite visual query to express user’s intention more clearly which helps to give more precise or appropriate results to user. The proposed algorithm will considerably get better in different aspects. System also uses Context based Image retrieval scheme to give significant outcomes. So system is able to achieve gain in terms of search performance, accuracy and user satisfaction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20space" title="color space">color space</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram" title=" histogram"> histogram</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20device" title=" mobile device"> mobile device</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20visual%20search" title=" mobile visual search"> mobile visual search</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20search" title=" multimodal search "> multimodal search </a> </p> <a href="https://publications.waset.org/abstracts/33265/interactive-image-search-for-mobile-devices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33265.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">369</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">3172</span> Semantic Search Engine Based on Query Expansion with Google Ranking and Similarity Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Shahin">Ahmad Shahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadi%20Chakik"> Fadi Chakik</a>, <a href="https://publications.waset.org/abstracts/search?q=Walid%20Moudani"> Walid Moudani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our study is about elaborating a potential solution for a search engine that involves semantic technology to retrieve information and display it significantly. Semantic search engines are not used widely over the web as the majorities are still in Beta stage or under construction. Many problems face the current applications in semantic search, the major problem is to analyze and calculate the meaning of query in order to retrieve relevant information. Another problem is the ontology based index and its updates. Ranking results according to concept meaning and its relation with query is another challenge. In this paper, we are offering a light meta-engine (QESM) which uses Google search, and therefore Google’s index, with some adaptations to its returned results by adding multi-query expansion. The mission was to find a reliable ranking algorithm that involves semantics and uses concepts and meanings to rank results. At the beginning, the engine finds synonyms of each query term entered by the user based on a lexical database. Then, query expansion is applied to generate different semantically analogous sentences. These are generated randomly by combining the found synonyms and the original query terms. Our model suggests the use of semantic similarity measures between two sentences. Practically, we used this method to calculate semantic similarity between each query and the description of each page’s content generated by Google. The generated sentences are sent to Google engine one by one, and ranked again all together with the adapted ranking method (QESM). Finally, our system will place Google pages with higher similarities on the top of the results. We have conducted experimentations with 6 different queries. We have observed that most ranked results with QESM were altered with Google’s original generated pages. With our experimented queries, QESM generates frequently better accuracy than Google. In some worst cases, it behaves like Google. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semantic%20search%20engine" title="semantic search engine">semantic search engine</a>, <a href="https://publications.waset.org/abstracts/search?q=Google%20indexing" title=" Google indexing"> Google indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20expansion" title=" query expansion"> query expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20measures" title=" similarity measures"> similarity measures</a> </p> <a href="https://publications.waset.org/abstracts/10857/semantic-search-engine-based-on-query-expansion-with-google-ranking-and-similarity-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10857.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">425</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">3171</span> Multiple Query Optimization in Wireless Sensor Networks Using Data Correlation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elaheh%20Vaezpour">Elaheh Vaezpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title="wireless sensor networks">wireless sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20query%20optimization" title=" multiple query optimization"> multiple query optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20correlation" title=" data correlation"> data correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=reducing%20energy%20consumption" title=" reducing energy consumption"> reducing energy consumption</a> </p> <a href="https://publications.waset.org/abstracts/73399/multiple-query-optimization-in-wireless-sensor-networks-using-data-correlation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73399.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">334</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">3170</span> Graph-Based Semantical Extractive Text Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Samizadeh">Mina Samizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction" title="keyword extraction">keyword extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=n-gram%20extraction" title=" n-gram extraction"> n-gram extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20clustering" title=" topic clustering"> topic clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title=" semantic analysis"> semantic analysis</a> </p> <a href="https://publications.waset.org/abstracts/160526/graph-based-semantical-extractive-text-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160526.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">71</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">3169</span> Synthetic Method of Contextual Knowledge Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olga%20Kononova">Olga Kononova</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Lyapin"> Sergey Lyapin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Global information society requirements are transparency and reliability of data, as well as ability to manage information resources independently; particularly to search, to analyze, to evaluate information, thereby obtaining new expertise. Moreover, it is satisfying the society information needs that increases the efficiency of the enterprise management and public administration. The study of structurally organized thematic and semantic contexts of different types, automatically extracted from unstructured data, is one of the important tasks for the application of information technologies in education, science, culture, governance and business. The objectives of this study are the contextual knowledge typologization, selection or creation of effective tools for extracting and analyzing contextual knowledge. Explication of various kinds and forms of the contextual knowledge involves the development and use full-text search information systems. For the implementation purposes, the authors use an e-library 'Humanitariana' services such as the contextual search, different types of queries (paragraph-oriented query, frequency-ranked query), automatic extraction of knowledge from the scientific texts. The multifunctional e-library «Humanitariana» is realized in the Internet-architecture in WWS-configuration (Web-browser / Web-server / SQL-server). Advantage of use 'Humanitariana' is in the possibility of combining the resources of several organizations. Scholars and research groups may work in a local network mode and in distributed IT environments with ability to appeal to resources of any participating organizations servers. Paper discusses some specific cases of the contextual knowledge explication with the use of the e-library services and focuses on possibilities of new types of the contextual knowledge. Experimental research base are science texts about 'e-government' and 'computer games'. An analysis of the subject-themed texts trends allowed to propose the content analysis methodology, that combines a full-text search with automatic construction of 'terminogramma' and expert analysis of the selected contexts. 'Terminogramma' is made out as a table that contains a column with a frequency-ranked list of words (nouns), as well as columns with an indication of the absolute frequency (number) and the relative frequency of occurrence of the word (in %% ppm). The analysis of 'e-government' materials showed, that the state takes a dominant position in the processes of the electronic interaction between the authorities and society in modern Russia. The media credited the main role in these processes to the government, which provided public services through specialized portals. Factor analysis revealed two factors statistically describing the used terms: human interaction (the user) and the state (government, processes organizer); interaction management (public officer, processes performer) and technology (infrastructure). Isolation of these factors will lead to changes in the model of electronic interaction between government and society. In this study, the dominant social problems and the prevalence of different categories of subjects of computer gaming in science papers from 2005 to 2015 were identified. Therefore, there is an evident identification of several types of contextual knowledge: micro context; macro context; dynamic context; thematic collection of queries (interactive contextual knowledge expanding a composition of e-library information resources); multimodal context (functional integration of iconographic and full-text resources through hybrid quasi-semantic algorithm of search). Further studies can be pursued both in terms of expanding the resource base on which they are held, and in terms of the development of appropriate tools. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contextual%20knowledge" title="contextual knowledge">contextual knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual%20search" title=" contextual search"> contextual search</a>, <a href="https://publications.waset.org/abstracts/search?q=e-library%20services" title=" e-library services"> e-library services</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency-ranked%20query" title=" frequency-ranked query"> frequency-ranked query</a>, <a href="https://publications.waset.org/abstracts/search?q=paragraph-oriented%20query" title=" paragraph-oriented query"> paragraph-oriented query</a>, <a href="https://publications.waset.org/abstracts/search?q=technologies%20of%20the%20contextual%20knowledge%20extraction" title=" technologies of the contextual knowledge extraction"> technologies of the contextual knowledge extraction</a> </p> <a href="https://publications.waset.org/abstracts/67954/synthetic-method-of-contextual-knowledge-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67954.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">359</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">3168</span> Extraction of Text Subtitles in Multimedia Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarjit%20Singh">Amarjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video" title="video">video</a>, <a href="https://publications.waset.org/abstracts/search?q=subtitles" title=" subtitles"> subtitles</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=frames" title=" frames"> frames</a> </p> <a href="https://publications.waset.org/abstracts/24441/extraction-of-text-subtitles-in-multimedia-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24441.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">601</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">3167</span> A Summary-Based Text Classification Model for Graph Attention Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuo%20Liu">Shuo Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20natural%20language%20processing" title="Chinese natural language processing">Chinese natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=abstract%20extraction" title=" abstract extraction"> abstract extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title=" graph attention network"> graph attention network</a> </p> <a href="https://publications.waset.org/abstracts/158060/a-summary-based-text-classification-model-for-graph-attention-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158060.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">100</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">3166</span> Literature Review on Text Comparison Techniques: Analysis of Text Extraction, Main Comparison and Visual Representation Tools</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andriana%20Mkrtchyan">Andriana Mkrtchyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Vahe%20Khlghatyan"> Vahe Khlghatyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20psychology" title="color psychology">color psychology</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition%2Fextraction" title=" data acquisition/extraction"> data acquisition/extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=disambiguation" title=" disambiguation"> disambiguation</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=outlier%20detection" title=" outlier detection"> outlier detection</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20similarity" title=" semantic similarity"> semantic similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=text-mining" title=" text-mining"> text-mining</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20evaluation" title=" user evaluation"> user evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20search" title=" visual search"> visual search</a> </p> <a href="https://publications.waset.org/abstracts/161588/literature-review-on-text-comparison-techniques-analysis-of-text-extraction-main-comparison-and-visual-representation-tools" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161588.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">77</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">3165</span> Urdu Text Extraction Method from Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samabia%20Tehsin">Samabia Tehsin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumaira%20Kausar"> Sumaira Kausar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=caption%20text" title="caption text">caption text</a>, <a href="https://publications.waset.org/abstracts/search?q=content-based%20image%20retrieval" title=" content-based image retrieval"> content-based image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title=" document analysis"> document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20extraction" title=" text extraction"> text extraction</a> </p> <a href="https://publications.waset.org/abstracts/9566/urdu-text-extraction-method-from-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9566.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">516</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">3164</span> Smart Online Library Catalog System with Query Expansion for the University of the Cordilleras</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vincent%20Ballola">Vincent Ballola</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymund%20Dilan"> Raymund Dilan</a>, <a href="https://publications.waset.org/abstracts/search?q=Thelma%20Palaoag"> Thelma Palaoag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Smart Online Library Catalog System with Query Expansion seeks to address the low usage of the library because of the emergence of the Internet. Library users are not accustomed to catalog systems that need a query to have the exact words without any mistakes for decent results to appear. The graphical user interface of the current system has a rather skewed learning curve for users to adapt with. With a simple graphical user interface inspired by Google, users can search quickly just by inputting their query and hitting the search button. Because of the query expansion techniques incorporated into the new system such as stemming, thesaurus search, and weighted search, users can have more efficient results from their query. The system will be adding the root words of the user's query to the query itself which will then be cross-referenced to a thesaurus database to search for any synonyms that will be added to the query. The results will then be arranged by the number of times the word has been searched. Online queries will also be added to the results for additional references. Users showed notable increases in efficiency and usability due to the familiar interface and query expansion techniques incorporated in the system. The simple yet familiar design led to a better user experience. Users also said that they would be more inclined in using the library because of the new system. The incorporation of query expansion techniques gives a notable increase of results to users that in turn gives them a wider range of resources found in the library. Used books mean more knowledge imparted to the users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=query%20expansion" title="query expansion">query expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=catalog%20system" title=" catalog system"> catalog system</a>, <a href="https://publications.waset.org/abstracts/search?q=stemming" title=" stemming"> stemming</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20search" title=" weighted search"> weighted search</a>, <a href="https://publications.waset.org/abstracts/search?q=usability" title=" usability"> usability</a>, <a href="https://publications.waset.org/abstracts/search?q=thesaurus%20search" title=" thesaurus search"> thesaurus search</a> </p> <a href="https://publications.waset.org/abstracts/62358/smart-online-library-catalog-system-with-query-expansion-for-the-university-of-the-cordilleras" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62358.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">388</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">3163</span> Small Text Extraction from Documents and Chart Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rominkumar%20Busa">Rominkumar Busa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahira%20K.%20C."> Shahira K. C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lijiya%20A."> Lijiya A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20text%20extraction" title="small text extraction">small text extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20text%20recognition" title=" scene text recognition"> scene text recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN" title=" CRNN"> CRNN</a> </p> <a href="https://publications.waset.org/abstracts/150310/small-text-extraction-from-documents-and-chart-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150310.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">126</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">3162</span> Finding the Optimal Meeting Point Based on Travel Plans in Road Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20H.%20Ahmadi">Mohammad H. Ahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Haghighatdoost"> Vahid Haghighatdoost</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given a set of source locations for a group of friends, and a set of trip plans for each group member as a sequence of Categories-of-Interests (COIs) (e.g., restaurant), and finally a specific COI as a common destination that all group members will gather together, in Meeting Point Based on Trip Plans (MPTPs) queries our goal is to find a Point-of-Interest (POI) from different COIs, such that the aggregate travel distance for the group is minimized. In this work, we considered two cases for aggregate function as Sum and Max. For solving this query, we propose an efficient pruning technique for shrinking the search space. Our approach contains three steps. In the first step, it prunes the search space around the source locations. In the second step, it prunes the search space around the centroid of source locations. Finally, we compute the intersection of all pruned areas as the final refined search space. We prove that the POIs beyond the refined area cannot be part of optimal answer set. The paper also covers an extensive performance study of the proposed technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=meeting%20point" title="meeting point">meeting point</a>, <a href="https://publications.waset.org/abstracts/search?q=trip%20plans" title=" trip plans"> trip plans</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20networks" title=" road networks"> road networks</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20databases" title=" spatial databases"> spatial databases</a> </p> <a href="https://publications.waset.org/abstracts/72988/finding-the-optimal-meeting-point-based-on-travel-plans-in-road-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72988.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">185</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">3161</span> Text Data Preprocessing Library: Bilingual Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kabil%20Boukhari">Kabil Boukhari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the context of information retrieval, the selection of the most relevant words is a very important step. In fact, the text cleaning allows keeping only the most representative words for a better use. In this paper, we propose a library for the purpose text preprocessing within an implemented application to facilitate this task. This study has two purposes. The first, is to present the related work of the various steps involved in text preprocessing, presenting the segmentation, stemming and lemmatization algorithms that could be efficient in the rest of study. The second, is to implement a developed tool for text preprocessing in French and English. This library accepts unstructured text as input and provides the preprocessed text as output, based on a set of rules and on a base of stop words for both languages. The proposed library has been made on diﬀerent corpora and gave an interesting result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20preprocessing" title="text preprocessing">text preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</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=normalization" title=" normalization"> normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20generation" title=" text generation"> text generation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a> </p> <a href="https://publications.waset.org/abstracts/150846/text-data-preprocessing-library-bilingual-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150846.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">94</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">3160</span> Improve B-Tree Index’s Performance Using Lock-Free Hash Table</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhanfeng%20Ma">Zhanfeng Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiping%20Xiong"> Zhiping Xiong</a>, <a href="https://publications.waset.org/abstracts/search?q=Hu%20Yin"> Hu Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhengwei%20She"> Zhengwei She</a>, <a href="https://publications.waset.org/abstracts/search?q=Aditya%20P.%20Gurajada"> Aditya P. Gurajada</a>, <a href="https://publications.waset.org/abstracts/search?q=Tianlun%20Chen"> Tianlun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Li"> Ying Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many RDBMS vendors use B-tree index to achieve high performance for point queries and range queries, and some of them also employ hash index to further enhance the performance as hash table is more efficient for point queries. However, there are extra overheads to maintain a separate hash index, for example, hash mapping for all data records must always be maintained, which results in more memory space consumption; locking, logging and other mechanisms are needed to guarantee ACID, which affects the concurrency and scalability of the system. To relieve the overheads, Hash Cached B-tree (HCB) index is proposed in this paper, which consists of a standard disk-based B-tree index and an additional in-memory lock-free hash table. Initially, only the B-tree index is constructed for all data records, the hash table is built on the fly based on runtime workload, only data records accessed by point queries are indexed using hash table, this helps reduce the memory footprint. Changes to hash table are done using compare-and-swap (CAS) without performing locking and logging, this helps improve the concurrency and avoid contention. The hash table is also optimized to be cache conscious. HCB index is implemented in SAP ASE database, compared with the standard B-tree index, early experiments and customer adoptions show significant performance improvement. This paper provides an overview of the design of HCB index and reports the experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=B-tree" title="B-tree">B-tree</a>, <a href="https://publications.waset.org/abstracts/search?q=compare-and-swap" title=" compare-and-swap"> compare-and-swap</a>, <a href="https://publications.waset.org/abstracts/search?q=lock-free%20hash%20table" title=" lock-free hash table"> lock-free hash table</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20queries" title=" point queries"> point queries</a>, <a href="https://publications.waset.org/abstracts/search?q=range%20queries" title=" range queries"> range queries</a>, <a href="https://publications.waset.org/abstracts/search?q=SAP%20ASE%20database" title=" SAP ASE database"> SAP ASE database</a> </p> <a href="https://publications.waset.org/abstracts/72665/improve-b-tree-indexs-performance-using-lock-free-hash-table" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72665.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">286</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">3159</span> Pattern Recognition Search: An Advancement Over Interpolation Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahpar%20Yilmaz">Shahpar Yilmaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasir%20Nadeem"> Yasir Nadeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20A.%20Mehdi"> Syed A. Mehdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Searching for a record in a dataset is always a frequent task for any data structure-related application. Hence, a fast and efficient algorithm for the approach has its importance in yielding the quickest results and enhancing the overall productivity of the company. Interpolation search is one such technique used to search through a sorted set of elements. This paper proposes a new algorithm, an advancement over interpolation search for the application of search over a sorted array. Pattern Recognition Search or PR Search (PRS), like interpolation search, is a pattern-based divide and conquer algorithm whose objective is to reduce the sample size in order to quicken the process and it does so by treating the array as a perfect arithmetic progression series and thereby deducing the key element’s position. We look to highlight some of the key drawbacks of interpolation search, which are accounted for in the Pattern Recognition Search. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=array" title="array">array</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=index" title=" index"> index</a>, <a href="https://publications.waset.org/abstracts/search?q=sorting" title=" sorting"> sorting</a>, <a href="https://publications.waset.org/abstracts/search?q=space" title=" space"> space</a>, <a href="https://publications.waset.org/abstracts/search?q=time" title=" time"> time</a> </p> <a href="https://publications.waset.org/abstracts/142819/pattern-recognition-search-an-advancement-over-interpolation-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142819.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">244</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">3158</span> Ontology-Based Representation of Islamic Rules to Perform Salah</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamza%20Zafar">Hamza Zafar</a>, <a href="https://publications.waset.org/abstracts/search?q=Quratulain%20Rajput"> Quratulain Rajput</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Salah (نماز ) is one of five pillars of Islam and obligatory for every Muslims. However, due to the lack of Islamic knowledge it might be very difficult for a layperson to perform it correctly. This paper presents an ontology based representation of Islamic rules to perform Salah. The Salah ontology has been built under the guidance of domain expert in light of Quran and Hadith. The ontology consists of basic concepts as well as relationship among concepts and constraints on them. The basic concepts include cleanness, body cover, Salah timing and steps to perform Salah. The SWRL rule language has been used to represent rule to determine whether the Salah performed correctly or it should be repeated. Finally, we evaluate the use of the Salat ontology through user’s example queries using SPARQL queries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prayer" title="prayer">prayer</a>, <a href="https://publications.waset.org/abstracts/search?q=salah" title=" salah"> salah</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=SPARQL%20queries" title=" SPARQL queries"> SPARQL queries</a>, <a href="https://publications.waset.org/abstracts/search?q=reasoning" title=" reasoning"> reasoning</a> </p> <a href="https://publications.waset.org/abstracts/49552/ontology-based-representation-of-islamic-rules-to-perform-salah" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49552.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">418</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">3157</span> Resource Creation Using Natural Language Processing Techniques for Malay Translated Qur'an</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nor%20Diana%20Ahmad">Nor Diana Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Eric%20Atwell"> Eric Atwell</a>, <a href="https://publications.waset.org/abstracts/search?q=Brandon%20Bennett"> Brandon Bennett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing techniques for English have been developed for several decades. But for the Malay language, text processing methods are still far behind. Moreover, there are limited resources, tools for computational linguistic analysis available for the Malay language. Therefore, this research presents the use of natural language processing (NLP) in processing Malay translated Qur’an text. As the result, a new language resource for Malay translated Qur’an was created. This resource will help other researchers to build the necessary processing tools for the Malay language. This research also develops a simple question-answer prototype to demonstrate the use of the Malay Qur’an resource for text processing. This prototype has been developed using Python. The prototype pre-processes the Malay Qur’an and an input query using a stemming algorithm and then searches for occurrences of the query word stem. The result produced shows improved matching likelihood between user query and its answer. A POS-tagging algorithm has also been produced. The stemming and tagging algorithms can be used as tools for research related to other Malay texts and can be used to support applications such as information retrieval, question answering systems, ontology-based search and other text analysis tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language%20resource" title="language resource">language resource</a>, <a href="https://publications.waset.org/abstracts/search?q=Malay%20translated%20Qur%27an" title=" Malay translated Qur'an"> Malay translated Qur'an</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing%20%28NLP%29" title=" natural language processing (NLP)"> natural language processing (NLP)</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20processing" title=" text processing"> text processing</a> </p> <a href="https://publications.waset.org/abstracts/92441/resource-creation-using-natural-language-processing-techniques-for-malay-translated-quran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92441.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">318</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">3156</span> A Similarity Measure for Classification and Clustering in Image Based Medical and Text Based Banking Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Sandesh">K. P. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Suman"> M. H. Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20classification" title="document classification">document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20clustering" title=" document clustering"> document clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22708/a-similarity-measure-for-classification-and-clustering-in-image-based-medical-and-text-based-banking-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22708.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">518</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">3155</span> OCR/ICR Text Recognition Using ABBYY FineReader as an Example Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Bagirzade">A. R. Bagirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sh.%20Najafova"> A. Sh. Najafova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yessirkepova"> S. M. Yessirkepova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20S.%20Albert"> E. S. Albert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABBYY%20FineReader%20system" title="ABBYY FineReader system">ABBYY FineReader system</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20symbol%20recognition" title=" algorithm symbol recognition"> algorithm symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR%2FICR%20techniques" title=" OCR/ICR techniques"> OCR/ICR techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20technologies" title=" recognition technologies"> recognition technologies</a> </p> <a href="https://publications.waset.org/abstracts/130255/ocricr-text-recognition-using-abbyy-finereader-as-an-example-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130255.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">168</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">3154</span> Functional and Efficient Query Interpreters: Principle, Application and Performances’ Comparison</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laurent%20Thiry">Laurent Thiry</a>, <a href="https://publications.waset.org/abstracts/search?q=Michel%20Hassenforder"> Michel Hassenforder</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a general approach to implement efficient queries’ interpreters in a functional programming language. Indeed, most of the standard tools actually available use an imperative and/or object-oriented language for the implementation (e.g. Java for Jena-Fuseki) but other paradigms are possible with, maybe, better performances. To proceed, the paper first explains how to model data structures and queries in a functional point of view. Then, it proposes a general methodology to get performances (i.e. number of computation steps to answer a query) then it explains how to integrate some optimization techniques (short-cut fusion and, more important, data transformations). It then compares the functional server proposed to a standard tool (Fuseki) demonstrating that the first one can be twice to ten times faster to answer queries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20transformation" title="data transformation">data transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20programming" title=" functional programming"> functional programming</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20server" title=" information server"> information server</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/104283/functional-and-efficient-query-interpreters-principle-application-and-performances-comparison" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104283.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">158</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">3153</span> Enterprise Information Portal Features: Results of Content Analysis Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michal%20Kr%C4%8D%C3%A1l">Michal Krčál</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since their introduction in 1990’s, Enterprise Information Portals (EIPs) were investigated from different perspectives (e.g. project management, technology acceptance, IS success). However, no systematic literature review was produced to systematize both the research efforts and the technology itself. This paper reports first results of an extent systematic literature review study focused on research of EIPs and its categorization, specifically it reports a conceptual model of EIP features. The previous attempt to categorize EIP features was published in 2002. For the purpose of the literature review, content of 89 articles was analyzed in order to identify and categorize features of EIPs. The methodology of the literature review was as follows. Firstly, search queries in major indexing databases (Web of Science and SCOPUS) were used. The results of queries were analyzed according to their usability for the goal of the study. Then, full-texts were coded in Atlas.ti according to previously established coding scheme. The codes were categorized and the conceptual model of EIP features was created. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=enterprise%20information%20portal" title="enterprise information portal">enterprise information portal</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20analysis" title=" content analysis"> content analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20literature%20review" title=" systematic literature review"> systematic literature review</a> </p> <a href="https://publications.waset.org/abstracts/59660/enterprise-information-portal-features-results-of-content-analysis-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59660.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> 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