Relevance Search Over Schema-rich Knowledge Graphs

Relevance search over a knowledge graph(KG)has gained much research atten-tion.Given a query entity in a KG,the problem is to find its most relevant entities.However,the relevance function is hidden and dynamic.Different users for different queries may consider relevance from different angles of semantics.The ambiguity in a query is more noticeable in the presence of thousands of types of entities and relations in a schema-rich KG,which has challenged the effectiveness and scalability of existing methods.To meet the challenge,our approach called RelSUE requests a user to pro-vide a small number of answer entities as examples,and then automatically learns the most likely relevance function from these examples.Specifically,we assume the in-tent of a query can be characterized by a set of meta-paths at the schema level.RelSUE searches a KG for diversified significant meta-paths that best characterize the relevance of the user-provided examples to the query entity.It reduces the large search space of a schema-rich KG using distance and degree-based heuristics,and performs reasoning to deduplicate meta-paths that represent equivalent query-specific semantics.Finally,a linear model is learned to predict meta-path based relevance.Extensive experiments demonstrate that RelSUE outperforms several state-of-the-art methods.