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Knowledge Augmented Expert finding framework via knowledge graph embedding for Community Question Answering
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AI
DOI：10.1016/j.engappai.2024.109891.png)
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Abstract
En 中文
Expert Finding in Community Question Answering aims to recommend appropriate experts to answer posted questions. However, existing approaches focus on exploiting semantic extraction and authority analysis techniques, failing to realize the latent knowledge-aspect connections between questions and experts. Therefore, the experts recommended for posted questions are limited to text-to-text approximation or domain-independent authority. In this study, we propose a Knowledge Augmented Expert finding framework (KAExpert) that introduces knowledge level information into semantic-based and authority-based expert finding method. A community knowledge graph is firstly constructed by acquiring ternary relations from public databases based on question tags and knowledge-related phrases. Then a flexible knowledge graph embedding is designed to extract the matching relationship between questions and experts at the knowledge level. Along this line, the knowledge- level authority is calculated based on the knowledge graph embedding to optimize the results of domain matching. According to knowledge graph embedding and knowledge-level authority, KAExpert is constructed to optimize the results of domain matching so that the found experts set consists of high-level expert with semantic matching and knowledge matching. Finally, experimental results on two real-world datasets collected from two major commercial question answering web sites show that KAExpert can outperform baseline methods with a significant margin.
Keywords:
Expert finding
Community question answering
Knowledge graph
Knowledge graph embedding
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