GREASE: A Generative Model for Relevance Search over Knowledge Graphs

TL;DR

GREASE is a novel generative model designed for relevance search over knowledge graphs, effectively capturing diverse semantics and constraints, and demonstrating superior performance on large-scale datasets.

Contribution

It introduces a new generative approach for relevance search in knowledge graphs that handles multiple semantics and property constraints, outperforming existing methods.

Findings

GREASE outperforms state-of-the-art methods in effectiveness.

It demonstrates high expressiveness in capturing diverse relevance semantics.

The model is efficient on large-scale knowledge graphs.

Abstract

Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBpedia which supports a wide range of different semantics of relevance based on numerous types of relations and attributes. As users may lack the expertise to formalize the desired semantics, supervised methods have emerged to learn the hidden user-defined relevance from user-provided examples. Along this line, in this paper we propose a novel generative model over KGs for relevance search, named GREASE. The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities. It is also extended to support properties that constrain answer entities. Extensive experiments on two large-scale KGs demonstrate that GREASE has advanced the state of the art in effectiveness, expressiveness, and efficiency.