Date of Award
12-14-2022
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
yanqing zhang
Second Advisor
Hemanth Demakethepalli Venkateswara
Third Advisor
Yi Ding
Abstract
Building query graphs from questions is an important step in complex question answering over knowledge graph (Complex KGQA). In general, a question can be correctly answered if its query graph is built correctly and the right answer is then retrieved by issuing the query graph against the KG. Therefore, this paper focuses on query graph generation from natural language questions. Existing approaches for query graph generation ignore the semantic structure of a question, resulting in a large number of noisy query graph candidates that undermine prediction accuracies. In this paper, we define six semantic structures from common questions in KGQA and develop a novel Structure-BERT to predict the semantic structure of a question, and then rank the remaining candidates with a BERT-based ranking model. Extensive experiments on two popular benchmarks MetaQA and WebQuestionsSP demonstrate the effectiveness of our method as compared to state-of-the-arts.
DOI
https://doi.org/10.57709/32601833
Recommended Citation
Li, Mingchen, "Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph." Thesis, Georgia State University, 2022.
doi: https://doi.org/10.57709/32601833
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