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.
We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.
Fact checking has captured the attention of the media and the public alike; it has also recently received strong attention from the computer science community, in particular from data and knowledge management, natural language processing and information retrieval; we denote these together under the term "content management". In this paper, we identify the fact checking tasks which can be performed with the help of content management technologies, and survey the recent research works in this area, before laying out some perspectives for the future. We hope our work will provide interested researchers, journalists and fact checkers with an entry point in the existing literature as well as help develop a roadmap for future research and development work.
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