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{"work":{"id":49446636,"created_at":"2021-06-28T19:29:13.336-07:00","from_world_paper_id":170453651,"updated_at":"2024-11-23T20:12:37.415-08:00","_data":{"grobid_abstract":"We describe ongoing research using natural English text queries as an intelligent interface for inferring answers from structured data in a specific domain. Users can express queries whose answers need to be deduced from data in different databases, without knowing the structures of those databases nor even the existence of the sources used. Users can pose queries incrementally, elaborating on an initial query, and ask follow-up questions based on answers to earlier queries. Inference in an axiomatic theory of the subject domain links the natural form in which the question is posed to the way relevant data is represented in a database, and composes information obtained from several databases into an answer to a complex question. We describe the status of a prototype system, called Quadri, for answering questions about HIV treatment, using the Stanford HIV Drug Resistance Database [8] and European resources. We discuss some of the problems that need to be solved to make this approach work, and some of our solutions.","publication_date":"2011,,","publication_name":"Proceedings of the 15th international conference on Intelligent user interfaces - IUI '11","grobid_abstract_attachment_id":"67796231"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Deducing answers to english questions from structured data","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [119178855]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":67796231,"attachmentType":"pdf"}"><img alt="First page of “Deducing answers to english questions from structured data”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/67796231/mini_magick20210628-17170-asygtv.png?1624933911" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Deducing answers to english questions from structured data</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="119178855" href="https://independent.academia.edu/RichardWaldinger"><img alt="Profile image of Richard Waldinger" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Richard Waldinger</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2011, Proceedings of the 15th international conference on Intelligent user interfaces - IUI '11</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">4 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 49446636; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">We describe ongoing research using natural English text queries as an intelligent interface for inferring answers from structured data in a specific domain. Users can express queries whose answers need to be deduced from data in different databases, without knowing the structures of those databases nor even the existence of the sources used. Users can pose queries incrementally, elaborating on an initial query, and ask follow-up questions based on answers to earlier queries. Inference in an axiomatic theory of the subject domain links the natural form in which the question is posed to the way relevant data is represented in a database, and composes information obtained from several databases into an answer to a complex question. We describe the status of a prototype system, called Quadri, for answering questions about HIV treatment, using the Stanford HIV Drug Resistance Database [8] and European resources. We discuss some of the problems that need to be solved to make this approach work, and some of our solutions.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":67796231,"attachmentType":"pdf","workUrl":"https://www.academia.edu/49446636/Deducing_answers_to_english_questions_from_structured_data"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":67796231,"attachmentType":"pdf","workUrl":"https://www.academia.edu/49446636/Deducing_answers_to_english_questions_from_structured_data"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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In a deductive approach to this problem, language processing technology translates English queries into a first-order logical form, which is regarded as a conjecture to be established by a theorem prover. Subject domain knowledge is encoded in an axiomatic theory equipped with links to appropriate databases. Commonsense reasoning is necessary to disambiguate the query, to connect the query with relevant tables in the databases, to deal with logical relationships in the query, and to achieve interoperability between disparate databases. This is illustrated with examples from the Quest system, which deals with queries over business enterprise data. Motivation We are interested in a particular style of natural language question answering in which: • Questions are in ordinary English, not a sublanguage. • Answers are precise, not just references to Web pages or documents.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Natural Language Access to Data: It Takes Common Sense!","attachmentId":67796249,"attachmentType":"pdf","work_url":"https://www.academia.edu/49446679/Natural_Language_Access_to_Data_It_Takes_Common_Sense_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/49446679/Natural_Language_Access_to_Data_It_Takes_Common_Sense_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="62980834" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/62980834/Answering_Questions_about_HIV_Drug_Resistance_using_Natural_Language_Technology_and_Theorem_Proving">Answering Questions about HIV Drug Resistance using Natural Language Technology and Theorem Proving</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="119178855" href="https://independent.academia.edu/RichardWaldinger">Richard Waldinger</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2011</p><p class="ds-related-work--abstract ds2-5-body-sm">We present preliminary work on an intelligent interface for answering English language clinical queries. Although our approach is domain independent, we focus on the needs of clinical researchers who are identifying cohorts of patients based on HIV drug-resistance patterns. Such questions are transformed into an unambiguous logical form by natural language technology (Bridge), which is then sent to a theorem prover (SNARK) that operates over an axiomatic theory of the subject domain. Symbols in the theory are linked to relations in one or more knowledge resources, such as databases, and an answer is obtained from the proof. Answers may be deduced or computed if they are not represented explicitly in a resource. We describe the status of our prototype system, called Quadri. We discuss some of the challenges that need to be solved to make this approach work and present some of our solutions.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Answering Questions about HIV Drug Resistance using Natural Language Technology and Theorem Proving","attachmentId":75564876,"attachmentType":"pdf","work_url":"https://www.academia.edu/62980834/Answering_Questions_about_HIV_Drug_Resistance_using_Natural_Language_Technology_and_Theorem_Proving","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/62980834/Answering_Questions_about_HIV_Drug_Resistance_using_Natural_Language_Technology_and_Theorem_Proving"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="17701222" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/17701222/English_Access_to_Structured_Data">English Access to Structured Data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31101616" href="https://parc.academia.edu/DannyBobrow">Danny Bobrow</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2011 IEEE Fifth International Conference on Semantic Computing, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">We present work on using a domain model to guide text interpretation, in the context of a project that aims to interpret English questions as a sequence of queries to be answered from structured databases. We adapt a broad-coverage and ambiguity-enabled natural language processing (NLP) system to produce domain-specific logical forms, using knowledge of the domain to zero in on the appropriate interpretation. The vocabulary of the logical forms is drawn from a domain theory that constitutes a higher-level abstraction of the contents of a set of related databases. The meanings of the terms are encoded in an axiomatic domain theory. To retrieve information from the databases, the logical forms must be instantiated by values constructed from fields in the database. The axiomatic domain theory is interpreted by the first-order theorem prover SNARK to identify the groundings, and then retrieve the values through procedural attachments semantically linked to the database. SNARK attempts to prove the logical form as a theorem by reasoning over the theory that is linked to the database and returns the exemplars of the proof(s) back to the user as answers to the query. The focus of this paper is more on the language task; however, we discuss the interaction that must occur between linguistic analysis and reasoning for an endto-end natural language interface to databases. We illustrate the process using examples drawn from an HIV treatment domain, where the underlying databases are records of temporally bound treatments of individual patients.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"English Access to Structured Data","attachmentId":39664292,"attachmentType":"pdf","work_url":"https://www.academia.edu/17701222/English_Access_to_Structured_Data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/17701222/English_Access_to_Structured_Data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="2988150" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2988150/Natural_Language_Questions_for_the_Web_of_Data">Natural Language Questions for the Web of Data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="115301" href="https://mpi-inf-mpg.academia.edu/myahya">Mohammed Yahya</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The Linked Data initiative comprises structured databases in the Semantic-Web data model RDF. Exploring this heterogeneous data by structured query languages is tedious and error-prone even for skilled users. To ease the task, this paper presents a methodology for translating natural language questions into structured SPARQL queries over linked-data sources.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Natural Language Questions for the Web of Data","attachmentId":30941207,"attachmentType":"pdf","work_url":"https://www.academia.edu/2988150/Natural_Language_Questions_for_the_Web_of_Data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2988150/Natural_Language_Questions_for_the_Web_of_Data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="35916673" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/35916673/QUESTION_ANSWERING_SYSTEM_WITH_NATURAL_LANGUAGE_INTERFACE_TO_DATABASE">QUESTION ANSWERING SYSTEM WITH NATURAL LANGUAGE INTERFACE TO DATABASE</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3432150" href="https://independent.academia.edu/editorijmraus">Publisher ijmra.us UGC Approved</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Question Answering (QA) is an area of natural language processing research aimed at providing human users with a convenient and natural interface for accessing information. Nowadays, the need to develop accurate systems gains more importance due to available structured knowledge-bases and the continuous demand to access information rapidly and efficiently. The need to store data in an organized manner so that searching, retrieving and maintaining of data becomes easier. To efficiently operate these database, knowledge of Structures Query Language (SQL)becomes essential. But the usage of SQL restricts the access to databases from the users who don't have the knowledge of them. A need for interface comes into the picture to enable the access of these databases even to a non-expert users. This paper describes the design to develop Telugu language Question Answering system to database. This paper describes about question answering system using Natural Language Interface to a database. Here we use the rule based algorithm for train the systems question classifier to achieve a high accuracy ratio. Keywords —Natural Language Processing (NLP), Natural Language Interface To Database (NLIDB), Question Answering System(QAS), Structured Query Language(SQL).</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"QUESTION ANSWERING SYSTEM WITH NATURAL LANGUAGE INTERFACE TO DATABASE","attachmentId":55797835,"attachmentType":"pdf","work_url":"https://www.academia.edu/35916673/QUESTION_ANSWERING_SYSTEM_WITH_NATURAL_LANGUAGE_INTERFACE_TO_DATABASE","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/35916673/QUESTION_ANSWERING_SYSTEM_WITH_NATURAL_LANGUAGE_INTERFACE_TO_DATABASE"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="14911675" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/14911675/SINA_semantic_interpretation_of_user_queries_for_question_answering_on_interlinked_data_by_Saeedeh_Shekarpour_with_Prateek_Jain_as_coordinator">SINA: semantic interpretation of user queries for question answering on interlinked data" by Saeedeh Shekarpour with Prateek Jain as coordinator</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33901638" href="https://uni-leipzig.academia.edu/EdgardMarx">Edgard Marx</a></div><p class="ds-related-work--metadata ds2-5-body-xs">ACM SIGWEB Newsletter, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"SINA: semantic interpretation of user queries for question answering on interlinked data\" by Saeedeh Shekarpour with Prateek Jain as coordinator","attachmentId":43784336,"attachmentType":"pdf","work_url":"https://www.academia.edu/14911675/SINA_semantic_interpretation_of_user_queries_for_question_answering_on_interlinked_data_by_Saeedeh_Shekarpour_with_Prateek_Jain_as_coordinator","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14911675/SINA_semantic_interpretation_of_user_queries_for_question_answering_on_interlinked_data_by_Saeedeh_Shekarpour_with_Prateek_Jain_as_coordinator"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="73707715" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/73707715/Towards_a_Theory_of_Natural_Language_Interfaces_to_Databases">Towards a Theory of Natural Language Interfaces to Databases</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="129163519" href="https://independent.academia.edu/AnaMaria3758">Ana Maria</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 8th …, 2003</p><p class="ds-related-work--abstract ds2-5-body-sm">The need for Natural Language Interfaces to databases (NLIs) has become increasingly acute as more and more people access information through their web browsers, PDAs, and cell phones. Yet NLIs are only usable if they map natural language questions to SQL queries correctly. As Schneiderman and Norman have argued, people are unwilling to trade reliable and predictable user interfaces for intelligent but unreliable ones. In this paper, we introduce a theoretical framework for reliable NLIs, which is the foundation for the fully implemented Precise NLI. We prove that, for a broad class of semantically tractable natural language questions, Precise is guaranteed to map each question to the corresponding SQL query. We report on experiments testing Precise on several hundred questions drawn from user studies over three benchmark databases. We find that over 80% of the questions are semantically tractable questions, which Precise answers correctly. Precise automatically recognizes the 20% of questions that it cannot handle, and requests a paraphrase. Finally, we show that Precise compares favorably with Mooney's learning NLI and with Microsoft's English Query product.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Towards a Theory of Natural Language Interfaces to Databases","attachmentId":82123431,"attachmentType":"pdf","work_url":"https://www.academia.edu/73707715/Towards_a_Theory_of_Natural_Language_Interfaces_to_Databases","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/73707715/Towards_a_Theory_of_Natural_Language_Interfaces_to_Databases"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="2468887" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/2468887/Question_answering_from_structured_knowledge_sources">Question answering from structured knowledge sources</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2024004" href="https://independent.academia.edu/AnetteFrank">Anette Frank</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Applied Logic, 2007</p><p class="ds-related-work--abstract ds2-5-body-sm">We present an implemented approach for domain-restricted question answering from structured knowledge sources, based on robust semantic analysis in a hybrid NLP system architecture. We perform question interpretation and answer extraction in an architecture that builds on a lexical-conceptual structure for question interpretation, which is interfaced with domain-specific concepts and properties in a structured knowledge base. Question interpretation involves a limited amount of domain-specific inferences, and accounts for higher-level quantificational questions. Question interpretation and answer extraction are modular components that interact in clearly defined ways. We derive so-called proto queries from the linguistic representations, which provide partial constraints for answer extraction from the underlying knowledge sources. The search queries we construct from proto queries effectively compute minimal spanning trees from the underlying knowledge sources. Our approach naturally extends to multilingual question answering, and has been developed as a prototype system for two application domains: the domain of Nobel prize winners, and the domain of Language Technology, on the basis of the large ontology underlying the information portal LT WORLD.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Question answering from structured knowledge sources","attachmentId":30489425,"attachmentType":"pdf","work_url":"https://www.academia.edu/2468887/Question_answering_from_structured_knowledge_sources","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/2468887/Question_answering_from_structured_knowledge_sources"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="79847962" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79847962/An_Introduction_to_Question_Answering_over_Linked_Data">An Introduction to Question Answering over Linked Data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="186467570" href="https://independent.academia.edu/Andr%C3%A9Freitas94">André Freitas</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lecture Notes in Computer Science, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">While the amount of knowledge available as linked data grows, so does the need for providing end users with access to this knowledge. Especially question answering systems are receiving much interest, as they provide intuitive access to data via natural language and shield end users from technical aspects related to data modelling, vocabularies and query languages. This tutorial gives an introduction to the rapidly developing eld of question answering over linked data. It gives an overview of the main challenges involved in the interpretation of a user's information need expressed in natural language with respect to the data that is queried. The paper summarizes the main existing approaches and systems including available tools and resources, benchmarks and evaluation campaigns. Finally, it lists the open topics that will keep question answering over linked data an exciting area of research in the years to come.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"An Introduction to Question Answering over Linked Data","attachmentId":86424893,"attachmentType":"pdf","work_url":"https://www.academia.edu/79847962/An_Introduction_to_Question_Answering_over_Linked_Data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79847962/An_Introduction_to_Question_Answering_over_Linked_Data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="123475493" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/123475493/A_Joint_Reasoning_based_Disease_Q_and_A_System">A Joint-Reasoning based Disease Q&A System</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="12399033" href="https://smu-sg.academia.edu/prakashsukhwal">prakash sukhwal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">arXiv (Cornell University), 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">Medical question answer (QA) assistants respond to lay users' health-related queries by synthesizing information from multiple sources using natural language processing and related techniques. They can serve as vital tools to alleviate issues of misinformation, information overload, and complexity of medical language, thus addressing lay users' information needs while reducing the burden on healthcare professionals. QA systems, the engines of such assistants, have typically used either language models (LMs) or knowledge graphs (KG), though the approaches could be complementary. LM-based QA systems excel at understanding complex questions and providing well-formed answers, but are prone to factual mistakes. KG-based QA systems, which represent facts well, are mostly limited to answering short-answer questions with pre-created templates. While a few studies have jointly used LM and KG approaches for text-based QA, this was done to answer multiple-choice questions. Extant QA systems also have limitations in terms of automation and performance. We address these challenges by designing a novel, automated disease QA system which effectively utilizes both LM and KG techniques through a joint-reasoning approach to answer disease-related questions appropriate for lay users. Our evaluation of the system using a range of quality metrics demonstrates its efficacy over benchmark systems, including the popular ChatGPT.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Joint-Reasoning based Disease Q\u0026A System","attachmentId":117897319,"attachmentType":"pdf","work_url":"https://www.academia.edu/123475493/A_Joint_Reasoning_based_Disease_Q_and_A_System","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/123475493/A_Joint_Reasoning_based_Disease_Q_and_A_System"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":67796231,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":67796231,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_67796231" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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href="https://www.academia.edu/104432585/A_RADAR_for_information_reconciliation_in_Question_Answering_systems_over_Linked_Data1">A RADAR for information reconciliation in Question Answering systems over Linked Data1</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="60783149" href="https://independent.academia.edu/AlessioPalmeroAprosio">Alessio Palmero Aprosio</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Semantic Web, 2017</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A RADAR for information reconciliation in Question Answering systems over Linked 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Bruce Croft</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2002</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Quasm: A system for question answering using semi-structured data","attachmentId":30740449,"attachmentType":"pdf","work_url":"https://www.academia.edu/2787969/Quasm_A_system_for_question_answering_using_semi_structured_data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/2787969/Quasm_A_system_for_question_answering_using_semi_structured_data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 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Sattler</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Question Answering on OLAP-like Data Sources","attachmentId":100313984,"attachmentType":"pdf","work_url":"https://www.academia.edu/99150936/Question_Answering_on_OLAP_like_Data_Sources","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/99150936/Question_Answering_on_OLAP_like_Data_Sources"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div 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href="https://independent.academia.edu/AnetteFrank">Anette Frank</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2005</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Querying structured knowledge sources","attachmentId":30823193,"attachmentType":"pdf","work_url":"https://www.academia.edu/2891758/Querying_structured_knowledge_sources","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/2891758/Querying_structured_knowledge_sources"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" 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