Leveraging Domain Context for Question Answering Over Knowledge Graph

By Peihao Tong, Junjie Yao, Linzi He, Liang Xu. 2019

43 citations recorded by the source corpus.

With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Haopeng Ren
2. Zhewei Wei
3. Yi Cai
4. Zengfeng Huang
5. Guohua Wang
6. Xiaofeng Chen
7. Mingguo He
8. Qing Li

Citations in the corpus

• A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification (2020, background): “Relation classification (RC) is one of the most important techniques in natural language processing (NLP) and has various applications such as information retrieval (Ercan et al., 2019), question answering (Tong et al., 2019) and dialogue systems (Ma et al., 2019).”
• BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation (2021, background): “Graph neural networks (GNNs) have received extensive attention from researchers due to their excellent performance on various graph learning tasks such as social analysis [24, 17, 29], drug discovery [12, 25], traffic forecasting [18, 3, 6], recommendation system [38, 32] and computer vision [39, 4]”