SINA: Semantic interpretation of user queries for question answering on interlinked data

Shekarpour, Saeedeh; Marx, Edgard; Ngomo, Axel-Cyrille Ngonga; Auer, Sören

doi:10.1016/j.websem.2014.06.002

Cited by 92 publications

(41 citation statements)

References 33 publications

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“…Both SINA and NLIWOD did not employ a query ranking mechanism, i.e., their accuracy and 4 https://github.com/dice-group/NLIWOD (Shekarpour et al, 2015) 0.24 † 0.39 † NLIWOD 4 0.48 † 0.49 † SQG (Zafar et al, 2018) 0.75 † -CompQA (Luo et al, 2018) 0.772 ±0.014 0.511 ±0.043 SubQG (our approach) 0.846 ±0.016 0.624 ±0.030 † indicates results taken from Singh et al (2018) and SQG. coverage are limited by the rules and templates.…”

Section: End-to-end Resultsmentioning

confidence: 99%

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Ding¹,

Hu²,

Xu³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results.

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Section: End-to-end Resultsmentioning

confidence: 99%

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Ding¹,

Hu²,

Xu³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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show abstract

“…In CASIA , ambiguities are modeled as soft constraints in Markov logic networks. SINA employs a hidden Markov model (HMM) and utilizes the optimal path from the HMM for disambiguation. In our method, disambiguation is performed in the final step by ranking generated SPARQL queries.…”

Section: Related Workmentioning

confidence: 99%

Ontology‐lexicon–based question answering over linked data

2020

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Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD‐5 benchmark and exhibits promising results.

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“…QAKiS [5] is an agnostic QA system that matches fragments of the question with binary relations of the triple store to address the problem of question interpretation as a relation-based match. SINA [23] is a semantic search engine which obtains either keyword-based query or natural language query as input. It uses a Hidden Markov model for disambiguation of mapped resources and then applies forward chaining for generating formal queries.…”

Section: Related Workmentioning

confidence: 99%

“…The input query of a QA system can be of various types. For example it might be a query in natural language text (e.g., [30]), a keyword-based query (e.g., [24]), an audio stream (e.g., [16]), or a resource-driven input (e.g., [4]). In all these cases the parts of an input query need to be identifiable as a referable instance such that they can be annotated during the input query analysis.…”

Section: A Input Querymentioning

confidence: 99%

Towards a Message-Driven Vocabulary for Promoting the Interoperability of Question Answering Systems

Singh

Both²,

Diefenbach

et al. 2016

2016 IEEE Tenth International Conference on Semantic Computing (ICSC)

Self Cite

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Question answering (QA) is one of the biggest challenges for making sense out of data. The Web of Data has attracted the attention of the QA community and recently, a number of schema-aware QA systems have been introduced. While research achievements are individually significant, yet, integrating different approaches is not possible due to lack of a systematic approach for conceptually describing QA systems. In this paper, we present a message-driven vocabulary built upon an abstract level. This vocabulary is concluded from conceptual views of different QA systems. In this way, we are enabling researchers and industry to implement message-driven QA systems and to reuse and extend different approaches without interoperability and extension concerns

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SINA: Semantic interpretation of user queries for question answering on interlinked data

Cited by 92 publications

References 33 publications

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Leveraging Frequent Query Substructures to Generate Formal Queries for Complex Question Answering

Ontology‐lexicon–based question answering over linked data

Towards a Message-Driven Vocabulary for Promoting the Interoperability of Question Answering Systems

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