Meta-Path Constrained Random Walk Inference for Large-Scale Heterogeneous Information Networks
This addresses the impracticality of manual meta-path specification in large-scale networks, improving inference accuracy for applications like knowledge graphs and bibliographic databases.
The paper tackles the problem of requiring user-provided meta-paths or biased examples for inference in large-scale heterogeneous information networks, proposing a framework that learns inference patterns via a tree structure and performs unbiased random walk inference with minimal guidance, achieving state-of-the-art performance on YAGO2 and DBLP datasets.
Heterogeneous information network (HIN) has shown its power of modeling real world data as a multi-typed entity-relation graph. Meta-path is the key contributor to this power since it enables inference by capturing the proximities between entities via rich semantic links. Previous HIN studies ask users to provide either 1) the meta-path(s) directly or 2) biased examples to generate the meta-path(s). However, lots of HINs (e.g., YAGO2 and Freebase) have rich schema consisting of a sophisticated and large number of types of entities and relations. It is impractical for users to provide the meta-path(s) to support the large scale inference, and biased examples will result in incorrect meta-path based inference, thus limit the power of the meta-path. In this paper, we propose a meta-path constrained inference framework to further release the ability of the meta-path, by efficiently learning the HIN inference patterns via a carefully designed tree structure; and performing unbiased random walk inference with little user guidance. The experiment results on YAGO2 and DBLP datasets show the state-of-the-art performance of the meta-path constrained inference framework.