Fast Pathfinding in Knowledge Graphs Using Word Embeddings

Knowledge graphs, which model relationships between entities, provide a rich and structured source of information. Currently, search engines aim to enrich their search results by structured summaries, e.g., obtained from knowledge graphs, that provide further information on the entity of interest. While single entity summaries are available already, summaries on the relations between multiple entities have not been studied in detail so far. Such queries can be understood as a pathfinding problem. However, the large size of public knowledge graphs, such as Wikidata, as well as the large indegree of its major entities, and the problem of concept drift impose major challenges for standard search algorithms in this context. In this paper, we propose a bidirectional pathfinding approach for directed knowledge graphs that uses the semantic distance between entity labels, which is approximated using word vectors, as a search heuristics in a parameterized A*-like evaluation function in order to find meaningful paths between two entities fast. We evaluate our approach using different parameters against a set of selected within- and cross-domain queries. The results indicate that our approach generally needs to explore fewer entities compared to its uninformed counterpart and qualitatively yields more meaningful paths.