6 research outputs found
GREASE: A Generative Model for Relevance Search over Knowledge Graphs
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.Comment: 9 pages, accepted to WSDM 202
Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs
Proximity search on heterogeneous graphs aims to measure the proximity between two nodes on a graph w.r.t. some semantic relation for ranking. Pioneer work often tries to measure such proximity by paths connecting the two nodes. However, paths as linear sequences have limited expressiveness for the complex network connections. In this paper, we explore a more expressive DAG (directed acyclic graph) data structure for modeling the connections between two nodes. Particularly, we are interested in learning a representation for the DAGs to encode the proximity between two nodes. We face two challenges to use DAGs, including how to efficiently generate DAGs and how to effectively learn DAG embedding for proximity search. We find distance-awareness as important for proximity search and the key to solve the above challenges. Thus we develop a novel Distance-aware DAG Embedding (D2AGE) model. We evaluate D2AGE on three benchmark data sets with six semantic relations, and we show that D2AGE outperforms the state-of-the-art baselines. We release the code on https://github.com/shuaiOKshuai