Not all Embeddings are created Equal: Extracting Entity-specific Substructures for RDF Graph Embedding

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 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 random walks to compute specificity. Our experimental evaluation results show that specificity-based biased random walks extract more "meaningful" (in terms of size and relevance) substructures compared to the state-of-the-art and the graph embedding learned from the extracted substructures outperform existing techniques in the chosen task of entity recommendation.