Improving Question Answering over Knowledge Graphs with a Chunked Learning Network
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Published:2023-08-06
Issue:15
Volume:12
Page:3363
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Zuo Zicheng1ORCID, Zhu Zhenfang1ORCID, Wu Wenqing2, Wang Wenling3, Qi Jiangtao1, Zhong Linghui1
Affiliation:
1. School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250104, China 2. School of Economic and Management, Nanjing University of Science and Technology, Nanjing 210094, China 3. Chinese Lexicography Research Center, Lu Dong University, Yantai 264025, China
Abstract
The objective of knowledge graph question answering is to assist users in answering questions by utilizing the information stored within the graph. Users are not required to comprehend the underlying data structure. This is a difficult task because, on the one hand, correctly understanding the semantics of a problem is difficult for machines. On the other hand, the growing knowledge graph will inevitably lead to information retrieval errors. Specifically, the question-answering task has three difficulties: word abbreviation, object complement, and entity ambiguity. An object complement means that different entities share the same predicate, and entity ambiguity means that words have different meanings in different contexts. To solve these problems, we propose a novel method named the Chunked Learning Network. It uses different models according to different scenarios to obtain a vector representation of the topic entity and relation in the question. The answer entity representation that yields the closest fact triplet, according to a joint distance metric, is returned as the answer. For sentences with an object complement, we use dependency parsing to construct dependency relationships between words to obtain more accurate vector representations. Experiments demonstrate the effectiveness of our method.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference43 articles.
1. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. (2008, January 9–12). Freebase: A collaboratively created graph database for structuring human knowledge. Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada. 2. Dbpedia—A large-scale, multilingual knowledge base extracted from wikipedia;Lehmann;Semantic Web.,2015 3. Fabian, M., Gjergji, K., and Gerhard, W. (2007, January 8–12). Yago: A core of semantic knowledge unifying wordnet and wikipedia. Proceedings of the 16th International World Wide Web Conference, Banff, AL, Canada. 4. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E.R., and Mitchell, T.M. (2010, January 11–15). Toward an architecture for never-ending language learning. Proceedings of the 24th AAAI Conference on Artificial Intelligence, Atlanta, GA, USA. 5. A relational algebra for SPARQL;Cyganiak;Digit. Media Syst. Lab. Lab. Bristol,2005
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