A large-scale knowledge graph contains a huge number of path-based semantic features, which provides a flexible mechanism to assign and expand semantics/attributes to entities. A particular set of these semantic features can be exploited on the fly, to support particular entity-oriented semantic search tasks. In this paper, we use entity set expansion as an example to show how these path-based semantic features can be effectively utilized in a semantic search application. The entity set expansion problem is to expand a small set of seed entities to a more complete set of similar entities. Traditionally, people solve this problem by exploiting the statistical co-occurrence of entities in the web pages, where semantic correlation among the seed entities is not well exploited. We propose to address the entity set expansion problem using the path-based semantic features of knowledge graphs. Our method first discovers relevant semantic features of the seed entities, which can be treated as the common aspects of these seed entities, and then retrieves relevant entities based on the discovered semantic features. Probabilistic models are proposed to rank entities, as well as semantic features, by handling the incompleteness of knowledge graphs. Extensive experiments on a public knowledge graph (i.e., DBpedia V3.9) and three public test collections (i.e., CLEF-QALD 2–4, SemSearch-LS 2011, and INEX-XER 2009) show that our method significantly outperforms the state-of-the-art techniques.

大规模知识图包含大量基于路径的语义特征，这提供了一种灵活的机制来向实体分配和扩展语义/属性。可以即时利用这些语义特征的特定集合，以支持特定的面向实体的语义搜索任务。在本文中，我们以实体集扩展为例来说明如何在语义搜索应用程序中有效利用这些基于路径的语义特征。实体集扩展问题是将一小组种子实体扩展为一组更完整的相似实体。传统上，人们通过利用网页中实体的统计共现来解决此问题，而种子实体之间的语义相关性并未得到很好的利用。我们建议使用知识图的基于路径的语义特征来解决实体集扩展问题。我们的方法首先发现种子实体的相关语义特征，可以将其视为这些种子实体的共同方面，然后根据发现的语义特征检索相关实体。通过处理知识图的不完整性，提出了概率模型来对实体以及语义特征进行排序。在公共知识图谱（即DBpedia V3.9）和三个公共测试集（即CLEF-QALD 2-4，SemSearch-LS 2011和INEX-XER 2009）上进行的大量实验表明，我们的方法在性能上显着优于状态最先进的技术。可以视为这些种子实体的共同方面，然后根据发现的语义特征检索相关实体。通过处理知识图的不完整性，提出了概率模型来对实体以及语义特征进行排序。在公共知识图谱（即DBpedia V3.9）和三个公共测试集（即CLEF-QALD 2-4，SemSearch-LS 2011和INEX-XER 2009）上进行的大量实验表明，我们的方法在性能上显着优于状态最先进的技术。可以视为这些种子实体的共同方面，然后根据发现的语义特征检索相关实体。通过处理知识图的不完整性，提出了概率模型来对实体以及语义特征进行排序。在公共知识图谱（即DBpedia V3.9）和三个公共测试集（即CLEF-QALD 2-4，SemSearch-LS 2011和INEX-XER 2009）上进行的大量实验表明，我们的方法在性能上显着优于状态最先进的技术。