152,391 research outputs found
TEQUILA: Temporal Question Answering over Knowledge Bases
Question answering over knowledge bases (KB-QA) poses challenges in handling complex questions that need to be decomposed into sub-questions. An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled. We present TEQUILA, an enabler method for temporal QA that can run on top of any KB-QA engine. TEQUILA has four stages. It detects if a question has temporal intent. It decomposes and rewrites the question into non-temporal sub-questions and temporal constraints. Answers to sub-questions are then retrieved from the underlying KB-QA engine. Finally, TEQUILA uses constraint reasoning on temporal intervals to compute final answers to the full question. Comparisons against state-of-the-art baselines show the viability of our method
Systematic review of question answering over knowledge bases
Over the years, a growing number of semantic data repositories have been made available on the web. However, this has created new challenges in exploiting these resources efficiently. Querying services require knowledge beyond the typical user’s expertise, which is a critical issue in adopting semantic information solutions. Several proposals to overcome this dif- ficulty have suggested using question answering (QA) systems to provide user‐friendly interfaces and allow natural language use. Because question answering over knowledge bases (KBQAs) is a very active research topic, a comprehensive view of the field is essential. The purpose of this study was to conduct a systematic review of methods and systems for KBQAs to identify their main advantages and limitations. The inclusion criteria rationale was English full‐text articles published since 2015 on methods and systems for KBQAs.info:eu-repo/semantics/publishedVersio
Question Decomposition Tree for Answering Complex Questions over Knowledge Bases
Knowledge base question answering (KBQA) has attracted a lot of interest in
recent years, especially for complex questions which require multiple facts to
answer. Question decomposition is a promising way to answer complex questions.
Existing decomposition methods split the question into sub-questions according
to a single compositionality type, which is not sufficient for questions
involving multiple compositionality types. In this paper, we propose Question
Decomposition Tree (QDT) to represent the structure of complex questions.
Inspired by recent advances in natural language generation (NLG), we present a
two-staged method called Clue-Decipher to generate QDT. It can leverage the
strong ability of NLG model and simultaneously preserve the original questions.
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA
system called QDTQA. Extensive experiments show that QDTQA outperforms previous
state-of-the-art methods on ComplexWebQuestions dataset. Besides, our
decomposition method improves an existing KBQA system by 12% and sets a new
state-of-the-art on LC-QuAD 1.0.Comment: Accepted by AAAI202
Ontology-based question answering systems over knowledge bases: a survey
Searching relevant, specific information in big data volumes is quite a challenging task. Despite the numerous
strategies in the literature to tackle this problem, this task is usually carried out by resorting to a Question
Answering (QA) systems. There are many ways to build a QA system, such as heuristic approaches, machine
learning, and ontologies. Recent research focused their efforts on ontology-based methods since the resulting
QA systems can benefit from knowledge modeling. In this paper, we present a systematic literature survey on
ontology-based QA systems regarding any questions. We also detail the evaluation process carried out in these
systems and discuss how each approach differs from the others in terms of the challenges faced and strategies
employed. Finally, we present the most prominent research issues still open in the field
Semantic Question Answering System over Linked Data using Relational Patterns
Hakimov S, Tunc H, Akimaliev M, Dogdu E. Semantic Question Answering System over Linked Data using Relational Patterns. In: EDBT/ICDT LWDM 2013. 2013.Question answering is the task of answering questions in naturallanguage. Linked Data project and Semantic Web communitymade it possible for us to query structured knowledge bases likeDBpedia and YAGO. Only expert users, however, with theknowledge of RDF and ontology definitions can build correctSPARQL queries for querying knowledge bases formally. In thispaper, we present a method for mapping natural languagequestions to ontology-based structured queries to retrieve directanswers from open knowledge bases (linked data). Our tool isbased on translating natural language questions into RDF triplepatterns using the dependency tree of the question text. Inaddition, our method uses relational patterns extracted from theWeb. We tested our tool using questions from QALD-2, QuestionAnswering over Linked Data challenge track and found promisingpreliminary results
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