Towards Combinational Relation Linking over Knowledge Graphs

Given a natural language phrase, relation linking aims to find a relation (predicate or property) from the underlying knowledge graph to match the phrase. It is very useful in many applications, such as natural language question answering, personalized recommendation and text summarization. However, the previous relation linking algorithms usually produce a single relation for the input phrase and pay little attention to a more general and challenging problem, i.e., combinational relation linking that extracts a subgraph pattern to match the compound phrase (e.g. In this paper, we focus on the task of combinational relation linking over knowledge graphs. To resolve the problem, we design a systematic method based on the data-driven relation assembly technique, which is performed under the guidance of meta patterns. We also introduce external knowledge to enhance the system understanding ability. Finally, we conduct extensive experiments over the real knowledge graph to study the performance of the proposed method. 1 Introduction Knowledge graphs have been important repositories to materialize a huge amount of structured information in the form of triples, where a triple consists of nullsubject, predicate, objectnull or null subject, property, value null. There have been many such knowledge graphs, e.g., DBpedia (Auer et al. 2007), Y ago (Suchanek, Kasneci, and Weikum 2007), and Freebase (Bollacker et al. 2008). In order to bridge the gap between unstructured text (including text documents and natural language questions) and structured knowledge, an important and interesting task is conducting relation linking over the knowledge graph, i.e., finding the specific predicates/properties from the knowledge graph that match the phrases detected in the sentence (also may be a question). Relation linking can power many downstream applications. As a friendly and intuitive approach to exploring knowledge graphs, using natural language questions to query the knowledge graph has attracted a lot of attentions in both academia and industrial communities (Berant et al. 2013; Bao et al. 2016; Das et al. 2017; Hu et al. 2018; Huang et al. 2019). Generally, the simple questions, e.g., who is the founder of Microsoft, are easy to answer since Figure 1: Example of combinational relations matching the compound phrase mother-in-law. it is straightforward to choose the predicate "founder" from the knowledge graph that matches the phrase "founder" in the input question.