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Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings

arXiv 2110.15622 ↗First posted 29 Oct 2021cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer. Recent approaches attempt to introduce the knowledge graph embedding (KGE) technique to handle the KG incompleteness but only consider the triple facts and neglect the significant semantic correlation between paths and multi-relational questions. In this paper, we propose a Path and Knowledge Embedding-Enhanced multi-relational Question Answering model (PKEEQA), which leverages multi-hop paths between entities in the KG to evaluate the ambipolar correlation between a path embedding and a multi-relational question embedding via a customizable path representation mechanism, benefiting for achieving more accurate answers from the perspective of both the triple facts and the extra paths. Experimental results illustrate that PKEEQA improves KBQA models' performance for multi-relational question answering with explainability to some extent derived from paths.

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References · 11

  1. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  2. 2016Complex Embeddings for Simple Link Predictioncited by 897
  3. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  4. 2016Key-Value Memory Networks for Directly Reading Documentscited by 223
  5. 2019KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoningcited by 216
  6. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 182
  7. 2015Holographic Embeddings of Knowledge Graphscited by 181
  8. 2017Variational Reasoning for Question Answering with Knowledge Graphcited by 170
  9. 2018Open Domain Question Answering Using Early Fusion of Knowledge Bases and Textcited by 167
  10. 2018An Interpretable Reasoning Network for Multi-Relation Question Answeringcited by 47
  11. 2019Bidirectional Attentive Memory Networks for Question Answering over Knowledge Basescited by 30

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