Path-Enhanced Multi-Relational Question Answering with Knowledge Graph
Embeddings
- URL: http://arxiv.org/abs/2110.15622v1
- Date: Fri, 29 Oct 2021 08:37:46 GMT
- Title: Path-Enhanced Multi-Relational Question Answering with Knowledge Graph
Embeddings
- Authors: Guanglin Niu, Yang Li, Chengguang Tang, Zhongkai Hu, Shibin Yang, Peng
Li, Chengyu Wang, Hao Wang, Jian Sun
- Abstract summary: We propose a Path and Knowledge Embedding-Enhanced multi-relational Question Answering model (PKEEQA)
We show that PKEEQA improves KBQA models' performance for multi-relational question answering with explainability to some extent derived from paths.
- Score: 16.21156041758793
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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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