[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-197858-105":53,"doc-detail-197858-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","a-guided-template-based-question-answering-system-over-knowledge-graphs-abstract-and-evaluation","A Guided Template-Based Question Answering System over Knowledge Graphs - Abstract and Evaluation","","Question answering systems over RDF and other structured knowledge graphs often fail when users ask queries that the system cannot interpret, reducing user experience and trust. The work introduces a guided QA approach that guarantees every entered question is mapped to a corresponding answer. It uses template-based generation of natural-language questions from RDF datasets, indexes question/answer pairs for auto-completion, and validates the architecture with preliminary evaluation results.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/a-guided-template-based-question-answering-system-over-knowledge-graphs-abstract-and-evaluation/197858/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/a-guided-template-based-question-answering-system-over-knowledge-graphs-abstract-and-evaluation/197858.png","ImageObject",442,249,{"name":88,"@type":89},"Ivy","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-11","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Why do question answering systems over linked data often disappoint users?","Question",{"text":108,"@type":109},"They are brittle: the system may fail to parse and execute a user’s question correctly, harming user experience and trust.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How does the proposed guided QA approach ensure that every entered question returns an answer?",{"text":113,"@type":109},"It uses template-based generation to create possible natural-language questions from an RDF dataset, evaluates SPARQL queries, and indexes resulting question/answer pairs for guided querying.",{"name":115,"@type":106,"acceptedAnswer":116},"What were the key limitations identified during evaluation?",{"text":117,"@type":109},"Due to lexicon coverage, the system could answer only a subset of properties, and it struggles with questions requiring multiple conditions (e.g., combining two constraints).","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},197858,1788471438,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":135,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":79,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":73},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","CEUR-WS°org/Vol-2262/ekaw-demo-20.pdf  \n# A Guided Template-Based Question AnsweringSystem over Knowledge Graphs\n\nLukas Biermann¹,2,Sebastian Walter¹,and Philipp Cimiano¹,2  \n¹Knowledge Engineering Team,Semalytix GmbH2 Semantic Computing Group,Bielefeld University  \nAbstract.Question answering systems provide easy access to struc-tured data,in particular RDF data.However,the user experience isoften negatively affected by questions that are not interpreted correctly.To remedy this,we present a new guided approach to QA that ensuresthat all questions that can be entered into the system also return a corre-sponding answer.For this,a template-based approach is used to generateall possible questions from a given RDF dataset using a number of tem-plates.The question/answer pairs can then be indexed to provide auto-completion functionality at querying time.We describe the architectureand approach and present preliminary evaluation results.  \nKeywords:Template based QA·QALD·Knowledge Graph  \n## 1 Introduction\n\nQuestion answering systems over linked data(QALD)often suffer from theirbrittleness,that is the relatively high probability of not being able to parse andexecute a question correctly.This is problematic as it negatively affects the userexperience and trust in a system.We hypothesize that user experience would besignificantly increased if all questions that can be entered into a query interfacewould also produce an answer.Building on this hypothesis,we have developed aQALD system that provides a controlled interface such that every question en-tered is interpretable and returns and appropriate answer.This is accomplishedby a template-based approach that matches basic graph patterns over the dataand,using a lexicon,generates different variants of asking the same question.Thequestion and corresponding answer are stored in an index and used to providequery writing support using auto-completion to propose possible continuationsof a query.In this paper we present our approach,which has been implementedin Python and relies on Hbase as database for indexing question/answer pairs.We illustrate the workings of the system using DBpedia as knowledge graph.Alive demo of the system will be shown during the demonstration session,whichis available at:  \nhttps://qa.semalytix.de  \n## 2 Approach\n\nOur overall approach is visualized in figure 1.In an offline process,a set of pre-defined templates are used to generate natural language questions and SPARQLqueries automatically.The SPARQL queries are evaluated and the resultingquestion/answer pairs are stored and indexed in HBase³.On the basis of thisindex,questions are retrieved while the user is typing a question and possiblecompletions are proposed in an auto-completion functionality.  \nFig.1:System Overview  \nThe retrieval of the data and the generation of the questions is displayed inmore detail in figure 2.The templates currently supported by the system aregiven in the table below:  \n\n| Template   | Example question   |\n| --- | --- |\n| Basic Triple(Noun/Verb)  \u003Cbr>Verb with Prepositional Phras  \u003Cbr>Participle Construction  \u003Cbr>Numeric Question  \u003Cbr>MaxCount  \u003Cbr>Numeric Aggregation  \u003Cbr>Numeric Filter  \u003Cbr>Ordering  \u003Cbr>Geographic   | Who is the wife of Barack Obama?  \u003Cbr>e|Which team Walter Payton played for?  \u003Cbr>Basic In which programming language is gimp written?  \u003Cbr>What is the frequency of BBC Radio Sheffield?  \u003Cbr>Who has the most alma maters?  \u003Cbr>Which Educational Institution has the lowest faculty size?  \u003Cbr>Give me all ships where the draft is less than \u003Cnumber>?  \u003Cbr>Give me all american football players ordered by birth place?  \u003Cbr>Give me all west german movies?   |\n\nIn order to illustrate the behaviour of the system,we focus on the Basic Triple(Noun/Verb)and the Verb with Prepositional Phrase pattern.We will assumethat for the property dbo:spouse the following lexical variants are in the lexicon:spouse,marry,wife,husband.In these settings,the template-based questiongeneration woul","cbCaiuuJbxtFuQEv","https://ap.wps.com/l/cbCaiuuJbxtFuQEv","pdf",448458,8,"English","# 1 Introduction\n## QALD brittleness and controlled interface\n# 2 Approach\n## Offline template generation and SPARQL evaluation\n## Question retrieval and auto-completion\n## Lexicon-driven lexical variants and example patterns\n# 3 Evaluation\n## Datasets and question/property coverage\n## Automatic and manual evaluation findings\n# 4 Conclusion\n## Guided QA via indexed question/answer pairs","[{\"question\":\"Why do question answering systems over linked data often disappoint users?\",\"answer\":\"They are brittle: the system may fail to parse and execute a user’s question correctly, harming user experience and trust.\"},{\"question\":\"How does the proposed guided QA approach ensure that every entered question returns an answer?\",\"answer\":\"It uses template-based generation to create possible natural-language questions from an RDF dataset, evaluates SPARQL queries, and indexes resulting question/answer pairs for guided querying.\"},{\"question\":\"What were the key limitations identified during evaluation?\",\"answer\":\"Due to lexicon coverage, the system could answer only a subset of properties, and it struggles with questions requiring multiple conditions (e.g., combining two constraints).\"}]","A Guided Template-Based Question Answering System over Knowledge Graphs - Abstract and Evaluation | PDF"]