EntityLDA: A Topic Model for Entity Retrieval on Knowledge Graph

Abstract

Encoding great aof information, knowledge graph (KG) has become a popular data source for information retrieval, especially the entity retrieval task. However, many online KGs include both structured triples and unstructured texts, which makes it difficult to represent entities in a unified form. Moreover, there is also a vocabulary gap between queries given by users and triples contained in KG. To solve these problems, we propose EntityLDA, a topic model which jointly models structured and unstructured parts of KG in order to get complete descriptions of entities. It also bridges the vocabulary gap between users and KG by connecting related words with shared topics. We further propose a retrieval solution based on EntityLDA to retrieve entities under different circumstances. Experimental results show that EntityLDA outperforms baselines in both quantity and quality.