2 Search Results for "Chekol, Melisachew Wudage"

Document

DOI: 10.4230/LIPIcs.TIME.2019.8

Time-Aware Probabilistic Knowledge Graphs

Authors: Melisachew Wudage Chekol and Heiner Stuckenschmidt

Published in: LIPIcs, Volume 147, 26th International Symposium on Temporal Representation and Reasoning (TIME 2019)

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.

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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)

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@InProceedings{chekol_et_al:LIPIcs.TIME.2019.8,
  author =	{Chekol, Melisachew Wudage and Stuckenschmidt, Heiner},
  title =	{{Time-Aware Probabilistic Knowledge Graphs}},
  booktitle =	{26th International Symposium on Temporal Representation and Reasoning (TIME 2019)},
  pages =	{8:1--8:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-127-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{147},
  editor =	{Gamper, Johann and Pinchinat, Sophie and Sciavicco, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2019.8},
  URN =		{urn:nbn:de:0030-drops-113662},
  doi =		{10.4230/LIPIcs.TIME.2019.8},
  annote =	{Keywords: temporal, probabilistic, knowledge graph, OWL-RL}
}

Document

DOI: 10.4230/OASIcs.ICLP.2017.4

Rule Based Temporal Inference

Authors: Melisachew Wudage Chekol and Heiner Stuckenschmidt

Published in: OASIcs, Volume 58, Technical Communications of the 33rd International Conference on Logic Programming (ICLP 2017)

Abstract

Time-wise knowledge is relevant in knowledge graphs as the majority facts are true in some time period, for instance, (Barack Obama, president of, USA, 2009, 2017). Consequently, temporal information extraction and temporal scoping of facts in knowledge graphs have been a focus of recent research. Due to this, a number of temporal knowledge graphs have become available such as YAGO and Wikidata. In addition, since the temporal facts are obtained from open text, they can be weighted, i.e., the extraction tools assign each fact with a confidence score indicating how likely that fact is to be true. Temporal facts coupled with confidence scores result in a probabilistic temporal knowledge graph. In such a graph, probabilistic query evaluation (marginal inference) and computing most probable explanations (MPE inference) are fundamental problems. In addition, in these problems temporal coalescing, an important research in temporal databases, is very challenging. In this work, we study these problems by using probabilistic programming. We report experimental results comparing the efficiency of several state of the art systems.

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Melisachew Wudage Chekol and Heiner Stuckenschmidt. Rule Based Temporal Inference. In Technical Communications of the 33rd International Conference on Logic Programming (ICLP 2017). Open Access Series in Informatics (OASIcs), Volume 58, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{chekol_et_al:OASIcs.ICLP.2017.4,
  author =	{Chekol, Melisachew Wudage and Stuckenschmidt, Heiner},
  title =	{{Rule Based Temporal Inference}},
  booktitle =	{Technical Communications of the 33rd International Conference on Logic Programming (ICLP 2017)},
  pages =	{4:1--4:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-058-3},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{58},
  editor =	{Rocha, Ricardo and Son, Tran Cao and Mears, Christopher and Saeedloei, Neda},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICLP.2017.4},
  URN =		{urn:nbn:de:0030-drops-84612},
  doi =		{10.4230/OASIcs.ICLP.2017.4},
  annote =	{Keywords: temporal inference, temporal knowledge graphs, probabilistic temporal reasoning}
}

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2 Search Results for "Chekol, Melisachew Wudage"

Time-Aware Probabilistic Knowledge Graphs

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Rule Based Temporal Inference

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