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

NETMAT: A knowledge-based grid system analysis tool

The increasing expansion of electric power systems renders the power system operator's task increasingly complex. The integration into energy management systems of further analytical algorithms implies that more data has to be analysed by the control engineer. For these reasons and many others, more sophisticated tools are required by power engineers to ease the pressure under which they perform their task. The advent of knowledge-based systems has led to a new approach to the problem. The combination of expert systems and numerical algorithms can be advantageously exploited to assist the power system engineer in operating the system. This paper presents the development of a knowledge-based tool for grid system analysis. The tool, NETMAT (NETwork Modelling AssistanT) , is to be used to analyse the impact of grid system maintenance and modification procedures and of new generating plants on power utilities, and in particular on their ability to generate and sell electricity. NE TMAT consists of a number of numerical applications interfaced to an expert system shell through specific problem domain knowledge bases. Results are presented based on the use of the IEEE-30 busbar network as a test network