More Accurate Question Answering on Freebase

Bast, Hannah; Haußmann, Elmar

doi:10.1145/2806416.2806472

Cited by 217 publications

(159 citation statements)

References 17 publications

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“…As for an end-to-end experiment, we compare QKBfly against the state-of-the-art KB-QA system, namely AQQU [5]. AQQU achieves an F1 score of 10%.…”

Section: Methods Under Comparisonmentioning

confidence: 99%

“…Question answering over structured knowledge bases (KB-QA) [5,6,55] denotes the task of translating a natural language question into a structured query (e.g., using SPARQL for querying SPO triples), which is then executed over the underlying KB (e.g., Freebase [7]) to obtain answer entities. As an extrinsic use-case, we harness QKBfly for KB-QA.…”

Section: B Qa Setupmentioning

confidence: 99%

“…Compared to Open IE methods, arguments of facts are canonicalized, thus referring to unique entities with semantically typed predicates which are derived from precomputed clusters of phrases. Besides supporting analytical queries, QKBfly thus also facilitates the application of current question-answering (QA) frameworks [6,5,55], which increasingly rely on structured knowledge backends, to currently popular events and queries.…”

Section: Introductionmentioning

confidence: 99%

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Query-driven on-the-fly knowledge base construction

Nguyen¹,

Abujabal²,

Tran

et al. 2017

Proc. VLDB Endow.

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Today's openly available knowledge bases, such as DBpedia, Yago, Wikidata or Freebase, capture billions of facts about the world's entities. However, even the largest among these (i) are still limited in up-to-date coverage of what happens in the real world, and (ii) miss out on many relevant predicates that precisely capture the wide variety of relationships among entities. To overcome both of these limitations, we propose a novel approach to build on-the-fly knowledge bases in a query-driven manner. Our system, called QKBfly, supports analysts and journalists as well as question answering on emerging topics, by dynamically acquiring relevant facts as timely and comprehensively as possible. QKBfly is based on a semantic-graph representation of sentences, by which we perform three key IE tasks, namely named-entity disambiguation, co-reference resolution and relation extraction, in a light-weight and integrated manner. In contrast to Open IE, our output is canonicalized. In contrast to traditional IE, we capture more predicates, including ternary and higher-arity ones. Our experiments demonstrate that QKBfly can build high-quality, on-the-fly knowledge bases that can readily be deployed, e.g., for the task of ad-hoc question answering. PVLDB Reference Format:

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“…As for an end-to-end experiment, we compare QKBfly against the state-of-the-art KB-QA system, namely AQQU [5]. AQQU achieves an F1 score of 10%.…”

Section: Methods Under Comparisonmentioning

confidence: 99%

Section: B Qa Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Query-driven on-the-fly knowledge base construction

Nguyen¹,

Abujabal²,

Tran

et al. 2017

Proc. VLDB Endow.

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“…A learning-to-rank framework with a random forest classifier (Bast and Haussmann, 2015) is used to model a preference function for a pair of queries, given an input utterance.…”

Section: Training Phasementioning

confidence: 99%

QUINT: Interpretable Question Answering over Knowledge Bases

Abujabal¹,

Roy²,

Yahya³

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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We present QUINT, a live system for question answering over knowledge bases. QUINT automatically learns role-aligned utterance-query templates from user questions paired with their answers. When QUINT answers a question, it visualizes the complete derivation sequence from the natural language utterance to the final answer. The derivation provides an explanation of how the syntactic structure of the question was used to derive the structure of a SPARQL query, and how the phrases in the question were used to instantiate different parts of the query. When an answer seems unsatisfactory, the derivation provides valuable insights towards reformulating the question.

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“…Bordes et al (2014) uses a vector space embedding approach to measure the semantic similarity between question and answers. Yao and Van Durme (2014), Bast and Haussmann (2015) and exploit a graph centric approach where a grounded subgraph query is generated from question and then executed against a KB. In this work, we propose a neural answer type inference method that can be incorporated in existing grounded semantic parsers as a complementary feature to improve ranking of the candidate logical forms.…”

mentioning

confidence: 99%

Improving Semantic Parsing via Answer Type Inference

Yavuz¹,

Gür²,

Su³

et al. 2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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In this work, we show the possibility of inferring the answer type before solving a factoid question and leveraging the type information to improve semantic parsing. By replacing the topic entity in a question with its type, we are able to generate an abstract form of the question, whose answer corresponds to the answer type of the original question. A bidirectional LSTM model is built to train over the abstract form of questions and infer their answer types. It is also observed that if we convert a question into a statement form, our LSTM model achieves better accuracy. Using the predicted type information to rerank the logical forms returned by AgendaIL, one of the leading semantic parsers, we are able to improve the F1-score from 49.7% to 52.6% on the WE-BQUESTIONS data.

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More Accurate Question Answering on Freebase

Cited by 217 publications

References 17 publications

Query-driven on-the-fly knowledge base construction

Query-driven on-the-fly knowledge base construction

QUINT: Interpretable Question Answering over Knowledge Bases

Improving Semantic Parsing via Answer Type Inference

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