Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket The increasing scale of encyclopedic knowledge graphs (KGs) calls for summarization as a way to help users efficiently access and distill world knowledge. Motivated by the disparity between individualsu0027 limited information needs and the massive scale of KGs, in this paper we propose a new problem called personalized knowledge graph summarization. The goal is to construct compact personal summaries of KGs containing only the facts most relevant to individualsu0027 interests. Such summaries can be stored and utilized ondevice, allowing individuals private, anytime access to the information that interests them most. We formalize the problem as one of constructing a sparse graph, or summary, that maximizes a useru0027s inferred utility over a given KG, subject to a userand devicespecific constraint on the summaryu0027s size. To solve it, we propose GLIMPSE, a summarization framework that provides theoretical guarantees on the summaryu0027s utility and is linear in the number of edges in the KG. In an evaluation with real user queries to opensource, encyclopedic KGs of up to one billion triples, we show that GLIMPSE efficiently creates summaries that outperform strong baselines by up to 19% in query answering F1 score.

Knowledge Graph Fact Prediction Via KnowledgeEnriched Tensor Factorization Abstract   We present a family of novel methods for embedding knowledge graphs into realvalued tensors. These tensorbased embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a provablyconvergent, linear tensor factorization algorithm. We demonstrate the efficacy of our models for the task of predicting new facts across eight different knowledge graphs, achieving between 5% and 50% relative improvement over existing stateoftheart knowledge graph embedding techniques. Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraintbased models doing better on graphs with a small number of highly similar relations and regularizationbased models dominating for graphs with relations of varying degrees of similarity.