Question Answering on Freebase via Relation Extraction and Textual Evidence

Xu, Kun; Reddy, Siva; Feng, Yansong; Huang, Songfang; Zhao, Dongyan

doi:10.18653/v1/p16-1220

Cited by 255 publications

(182 citation statements)

References 37 publications

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“…A triple e 1 , r, e 2 is called a relation instance and we refer to the relation of the target entity pair as target relation. Relation extraction is a fundamental task that enables a wide range of semantic applications from question answering (Xu et al, 2016) to fact checking (Vlachos and Riedel, 2014).…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Representations for Knowledge Base Relation Extraction

Sorokin¹,

Gurevych²

2017

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

123

124

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We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction.We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24% on a held-out set of relations.The code and the dataset to replicate the experiments are made available at https://github.com/ukplab.

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Section: Introductionmentioning

confidence: 99%

Context-Aware Representations for Knowledge Base Relation Extraction

Sorokin¹,

Gurevych²

2017

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

123

124

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“…We use the detected topic entity mentions to obtain candidate matching entities in the KB using Freebase Search API. We use top-Model F1 (Berant et al, 2013) 35.7 (Yao and Van Durme, 2014) 33.0 (Berant and Liang, 2014) 39.9 (Bao et al, 2014) 37.5 (Bordes et al, 2014) 39.2 (Yang et al, 2014) 41.3 (Dong et al, 2015b) 40.8 (Yao, 2015) 44.3 (Berant and Liang, 2015) 49.7 52.5 50.3 (Xu et al, 2016) 53 3 entities returned for the pruning step of Question Abstraction on the test examples. Answer Type Prediction.…”

Section: Methodsmentioning

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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“…By working against a knowledge graph, crisp entities can be returned as answers. By exploiting the structure provided by the knowledge graph and extracting relationship between entities, one can also answer complex questions that require multiple joins, corresponding to paths in the knowledge graph [6,13,14]. In these cases, a knowledge graph can be used to return answers that are proper tuples of entities, rather than singletons.…”

Section: Related Workmentioning

confidence: 99%

Answering Who/When, What, How, Why through Constructing Data Graph, Information Graph, Knowledge Graph and Wisdom Graph

Shao

Duan

Sun

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. Natural language questions are the most intuitive way of formulating an information need. People can formulate questions to express their information needs. Natural language questions as a query language present an ideal compromise between keyword and structured querying. Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics. Knowledge graph has abundant natural semantics and can contain various and more complete information. Its expression mechanism is closer to natural language. We propose to clarify the expression of knowledge graph as a whole. We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why. We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

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Question Answering on Freebase via Relation Extraction and Textual Evidence

Cited by 255 publications

References 37 publications

Context-Aware Representations for Knowledge Base Relation Extraction

Context-Aware Representations for Knowledge Base Relation Extraction

Improving Semantic Parsing via Answer Type Inference

Answering Who/When, What, How, Why through Constructing Data Graph, Information Graph, Knowledge Graph and Wisdom Graph

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