关键词: 知识图谱, Freebase, 查询扩展, 有效性, 稳定性

Abstract: This paper aims to construct a query expansion model based on query-related entities and their properties in Freebase, which are used to reconstruct the query for better expressing the user's needs. The relevance score between each property term and the query is measured by the risk-reward analysis in portfolio theory, which is expected to maximize the reward of the relevance scores of property terms and minimize the risk of query expansion failure using these property terms. This paper also proposes a method to integrate these entities and associated properties into the language modeling framework for query expansion. In the experiments, the retrieval effectiveness and stability of the query expansion model solely based on Freebase are evaluated on two Web collections, in comparison with the baseline language model LM and the traditional query expansion model based on pseudo relevance feedback RM3. The   experimental results show that the expansion model proposed in this paper outperforms baseline LM by 6%~15% in MAP (mean average precision), and it also performs more effectively and stably than RM3.

Key words: knowledge graph, Freebase, query expansion, effectiveness, stability