We propose an anytime bottom-up technique for learning logical rules from large knowledge graphs. We apply the learned rules to predict candidates in the context of knowledge graph completion. Our approach outperforms other rule-based approaches and it is competitive with current state of the art, which is based on latent representations. Besides, our approach is significantly faster, requires less computational resources, and yields an explanation in terms of the rules that propose a candidate.

Knowledge Graphs (KGs) are a popular means to represent knowledge on the Web, typically in the form of node/edge labelled directed graphs. We consider temporal KGs, in which edges are further annotated with time intervals, reflecting when the relationship between entities held in time. In this paper, we focus on the task of predicting time validity for unannotated edges. We introduce the problem as a variation of relational embedding. We adapt existing approaches, and explore the importance example selection and the incorporation of side information in the learning process. We present our experimental evaluation in details.

Abstract. The problem of SPARQL query containment is defined as determining if the result of one query is included in the result of another for any RDF graph. Query containment is important in many areas, including information integration, query optimization, and reasoning about Entity-Relationship diagrams. We encode this problem into an expressive logic called µ-calculus: where RDF graphs become transition systems, queries and schema axioms become formulas. Thus, the containment problem is reduced to formula satisfiability test. Beyond the logic's expressive power, satisfiability solvers are available for it. Hence, this study allows to exploit these advantages.

Abstract-Ontology matching is an important part of enabling the semantic web to reach its full potential. Most existing ontology matching methods are mainly based on linguistic information (label, name, title and comment) but from the results achieved it is realized that this information is not sufficient. The latest ontology matching research works are trying to deeply dig into the structural information of ontologies by using "similarityflooding" method. However, there are several innate issues in similarity-flooding methods that lead to wrong matching results. In this paper, we report the problems of similarity-flooding in ontology matching and propose a novel method to effectively leverage the structural information of the ontology. The evaluation is conducted on OAEI ontology matching benchmarks from 2011 to 2015. The result shows that the proposed approach performs comparatively well with other state of the art matching systems.

Knowledge graphs enriched with temporal information are becoming more and more common. As an example, the Wikidata KG contains millions of temporal facts associated with validity intervals (i.e., start and end time) covering a variety of domains. While these facts are interesting, computing temporal relations between their intervals allows to discover temporal relations holding between facts (e.g., "football players that get divorced after moving from a team to another"). In this paper we study the problem of computing different kinds of interval joins in temporal KGs. In principle, interval joins can be computed by resorting to query languages like SPARQL. However, this language is not optimized for such a task, which makes it hard to answer real-world queries. For instance, the query "find players that were married while being member of a team" times out on Wikidata. We present efficient algorithms to compute interval joins for the main Allen's relations (e.g., before, after, during, meets). We also address the problem of interval coalescing, which is used for merging contiguous or overlapping intervals of temporal facts, and propose an efficient algorithm. We integrate our interval joins and coalescing algorithms into a light SPARQL extension called iSPARQL. We evaluated the performance of our algorithms on real-world temporal kgs.