Retrieving Entities from Knowledge Graphs without Knowing Much: On Learning Generalizable Patterns and Indexing
Yuting Xie, Tingjian Ge, Cindy Chen · 2020
Querying and extracting potentially a large number of entities that are the user's intention is a challenging problem for knowledge graphs. The conventional query mechanism of subgraph pattern matching would not work well as the user in general does not know the graph pattern to search for. Moreover, there may not be a single subgraph pattern that fits all the intended entities. Using keywords also may not be a viable approach, as it is very difficult to come up with the right set of keywords, and the results are often very diverse and overwhelming. We propose a novel approach that does not require users to know much about the knowledge graph but only simple keywords about the desired entities. We retrieve a sample of matches and learn the entity context patterns as what we call the subgraph sketch signatures. We provide clustered patterns for the user to prune. Moreover, we devise a novel index to finally perform efficient entity retrieval over the whole knowledge graph.