Shape Expressions Schemas

We present Shape Expressions (ShEx), an expressive schema language for RDF designed to provide a high-level, user friendly syntax with intuitive semantics. ShEx allows to describe the vocabulary and the structure of an RDF graph, and to constrain the allowed values for the properties of a node. It includes an algebraic grouping operator, a choice operator, cardinalitiy constraints for the number of allowed occurrences of a property, and negation. We define the semantics of the language and illustrate it with examples. We then present a validation algorithm that, given a node in an RDF graph and a constraint defined by the ShEx schema, allows to check whether the node satisfies that constraint. The algorithm outputs a proof that contains trivially verifiable associations of nodes and the constraints that they satisfy. The structure can be used for complex post-processing tasks, such as transforming the RDF graph to other graph or tree structures, verifying more complex constraints, or debugging (w.r.t. the schema). We also show the inherent difficulty of error identification of ShEx

Time-Aware Probabilistic Knowledge Graphs

The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model