随着大规模知识图谱的出现以及企业高效管理领域知识图谱的需求,知识图谱中的自组织实体检索成为研究热点。给定知识图谱以及用户查询,实体检索的目标在于从给定的知识图谱中返回实体的排序列表。从匹配的角度来看,传统的实体检索模型大都将用户查询和实体统一映射到词的特征空间。这样做具有明显的缺点,例如,将同属于一个实体的两个词视为独立的。为此,该文提出将用户查询和实体同时映射到实体与词两个特征空间方法,称为双特征空间的排序学习。首先将实体抽象成若干个域。之后从词空间和实体空间两个维度分别抽取排序特征,最终应用于排序学习算法中。实验结果表明,在标准数据集上,双特征空间的实体排序学习模型性能显著优于当前先进的实体检索模型。
Abstract
Entity retrieval from knowledge graph is of substantial significance as the large scale knowledge graphs appear, and the industry demand on effectively managing the domain knowledge graphs. Given a certain knowledge graph and a user query, entity retrieval aims at obtaining a ranking list of entities from the knowledge graph accor-ding to its relevance to the query. Being treated as the matching between the query and entities, traditional entity retrieval models map both user queries and entities into the word feature space. However, it does not work when two words in the name of an entity are assumed to be independent. In this paper, we propose to project both user queries and entities into a dual feature space, namely entity-word feature space. First, we represent entities as multiple domains and extract ranking features from them. Then, learning to rank models are employed to train a ranking model from this dual feature space. Experimental results on benchmark datasets show that our proposed method outperform state-of-the-art baselines significantly.
关键词
知识图谱 /
实体检索 /
双特征空间
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Key words
knowledge graph /
entity retrieval /
dual features space
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脚注
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基金
国家自然科学基金(61602451);山东省重大科技创新工程项目(2018CXGC1215);山东大学基本科研业务费资助项目(26010177611018)
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