Abstract

Abstract Knowledge Graphs (KGs) have become useful sources of structured data for information retrieval and data analytics tasks. Enabling complex analytics, however, requires entities in KGs to be represented in a way that is suitable for Machine Learning tasks. Several approaches have been recently proposed for obtaining vector representations of KGs based on identifying and extracting relevant graph substructures using both uniform and biased random walks. However, such approaches lead to representations comprising mostly popular, instead of relevant, entities in the KG. In KGs, in which different types of entities often exist (such as in Linked Open Data), a given target entity may have its own distinct set of most relevant nodes and edges. We propose specificity as an accurate measure of identifying most relevant, entity-specific, nodes and edges. We develop a scalable method based on bidirectional …

Date

2019

Authors

Mayank Kejriwal, Vanessa Lopez, Juan F Sequeda, Muhammad Rizwan Saeed, Charalampos Chelmis, Viktor K Prasanna

Journal

Semantic Web (1570-0844)

Volume

10

Issue

6