Learning to Retrieve Related Resources in a Bibliographic Information Network

摘要

With the advancement in semantic technologies, structured data in different domains is being modelled as knowledge graphs. Bibliographic information has proven rather acquiescent to being formalized as knowledge graphs or information networks. In this paper, we address the problem of retrieving related resources in a bibliographic information network like SciGraph. Discovery of path patterns representing a set of example pairs of related resources forms the basis of our solution. We have adopted a bi-directional search strategy to accommodate state-of-the-art similarity measure (viz. HeteSim), relevant to Heterogeneous Information Networks (HIN). The proposed method has been evaluated based on precision and execution time. On experimenting with different datasets (YAGO and Springer Nature SciGraph), we observe an improvement in performance, compared to forward search algorithm, which is based on Path Constrained Random Walk similarity measure.