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Time-Aware Probabilistic Knowledge Graphs

Authors Melisachew Wudage Chekol, Heiner Stuckenschmidt

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Melisachew Wudage Chekol
  • Data and Web Science Group, University of Mannheim, Germany
Heiner Stuckenschmidt
  • Data and Web Science Group, University of Mannheim, Germany

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Melisachew Wudage Chekol and Heiner Stuckenschmidt. Time-Aware Probabilistic Knowledge Graphs. In 26th International Symposium on Temporal Representation and Reasoning (TIME 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 147, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.TIME.2019.8

Abstract

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.

Subject Classification

ACM Subject Classification
  • Information systems → Web Ontology Language (OWL)
  • Computing methodologies → Probabilistic reasoning
  • Computing methodologies → Temporal reasoning
Keywords
  • temporal
  • probabilistic
  • knowledge graph
  • OWL-RL

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