Shihao Ji


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Semantic Structure Based Query Graph Prediction for Question Answering over Knowledge Graph
Mingchen Li | Shihao Ji
Proceedings of the 29th International Conference on Computational Linguistics

Building query graphs from natural language 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. By doing so, we can first filter out noisy candidate query graphs by the predicted semantic structures, and then rank the remaining candidates with a BERT-based ranking model. Extensive experiments on two popular benchmarks MetaQA and WebQuestionsSP (WSP) demonstrate the effectiveness of our method as compared to state-of-the-arts.


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WordRank: Learning Word Embeddings via Robust Ranking
Shihao Ji | Hyokun Yun | Pinar Yanardag | Shin Matsushima | S. V. N. Vishwanathan
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


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Empirical Exploitation of Click Data for Task Specific Ranking
Anlei Dong | Yi Chang | Shihao Ji | Ciya Liao | Xin Li | Zhaohui Zheng
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing