The Value of Paraphrase for Knowledge Base Predicates
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Paraphrase, i.e., differing textual realizations of the same meaning, has proven useful for many natural language processing (NLP) applications. Collecting paraphrase for predicates in knowledge bases (KBs) is the key to comprehend the RDF triples in KBs. Existing works have published some paraphrase datasets automatically extracted from large corpora, but have too many redundant pairs or don't cover enough predicates, which cannot be improved by computer only and need the help of human beings. This paper shows a full process of collecting large-scale and high-quality paraphrase dictionaries for predicates in knowledge bases, which takes advantage of existing datasets and combines the technologies of machine mining and crowdsourcing. Our dataset comprises 2284 distinct predicates in DBpedia and 31130 paraphrase pairs in total, the quality of which is a great leap over previous works. Then it is demonstrated that such good paraphrase dictionaries can do great help to natural language processing tasks such as question answering and language generation. We also publish our own dictionary for further research.
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The paraphrase dictionary constructed by Xue et al. (2020) establishes a mapping relationship between natural language phrases and relations in the knowledge graph, which can be prior knowledge for implicit relation phrases with vague meanings.
“Considering the work (Xue et al., 2020) that constructs a dictionary of paraphrases and establishes a mapping relationship between NL phrases and relations in KG, which can be prior knowledge for implicit relation phrases with vague meanings”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Xue et al., 2020
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe Paraphrase dictionary maps natural language patterns to DBpedia relations and currently covers 2,284 relations and 31,130 paraphrase pairs.
“Paraphrase maps NL patterns to DBpedia relations, which currently covers 2,284 relations and 31,130 paraphrase pairs.”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Xue et al., 2020
Previous works pre-collect paraphrase pairs between relation phrases in natural language and KG relations through frequent item mining or crowdsourcing.
“The previous works (Nakashole et al., 2012;Xue et al., 2020) pre-collect some paraphrase pairs (e.g.,”
From Implicit Relation Linking for Question Answering over Knowledge Graph (2022) · cites this paper as Xue et al., 2020
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Cited by
Sorted by citations
Implicit Relation Linking for Question Answering over Knowledge Graph
Zhao, Y., Huang, J., Hu, W., Chen, Q., Qiu, X., Huo, C., & Ren, W. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.312 · Cited by 5 · see citing works
- Authors
- Xue, B., Hu, S., Zou, L., & Cheng, J.
- Affiliations
- Peking University (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 05
- Pages
- 9346-9353
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2997929338
- Cited by
- 4
- References
- 0
- Citations by year
- 2024: 1 · 2022: 2 · 2021: 1
- FWCI
- 0.29
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61532010, 61622201, 61932001, 61961130390)
- National Key Research and Development Program of China (2018YFB1003504)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- paraphrase, RDF triples, DBpedia, crowdsourcing, question answering, language generation
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