Logic on MARS: Ontologies for Generalised Property Graphs

Marx, Maximilian; Krötzsch, Markus; Thost, Veronika

doi:10.24963/ijcai.2017/165

Cited by 18 publications

(20 citation statements)

References 10 publications

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“…Possible further directions for future work include i) a practical study on the usefulness of integrating implicational knowledge into the editing process, ii) integrating with completeness [6] tools such as COOL-WD [7] to ensure that only counterexamples above a certain completeness threshold are considered, iii) extending the structured approach to include further incidence relations adapted to other aspects of the Wikidata data model, such as grouping properties for quantities by intervals of their values, and iv) extend the approach to incorpo-rate background knowledge given, e.g., in the form of the MARPL rules [20] that have been proposed for ontological reasoning on Wikidata [19].…”

Section: Discussionmentioning

confidence: 99%

Discovering Implicational Knowledge in Wikidata

Hanika

Marx

Stumme

2019

Formal Concept Analysis

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Knowledge graphs have recently become the state-of-the-art tool for representing the diverse and complex knowledge of the world. Examples include the proprietary knowledge graphs of companies such as Google, Facebook, IBM, or Microsoft, but also freely available ones such as YAGO, DBpedia, and Wikidata. A distinguishing feature of Wikidata is that the knowledge is collaboratively edited and curated. While this greatly enhances the scope of Wikidata, it also makes it impossible for a single individual to grasp complex connections between properties or understand the global impact of edits in the graph. We apply Formal Concept Analysis to efficiently identify comprehensible implications that are implicitly present in the data. Although the complex structure of data modelling in Wikidata is not amenable to a direct approach, we overcome this limitation by extracting contextual representations of parts of Wikidata in a systematic fashion. We demonstrate the practical feasibility of our approach through several experiments and show that the results may lead to the discovery of interesting implicational knowledge. Besides providing a method for obtaining large real-world data sets for FCA, we sketch potential applications in offering semantic assistance for editing and curating Wikidata.

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Section: Discussionmentioning

confidence: 99%

Discovering Implicational Knowledge in Wikidata

Hanika

Marx

Stumme

2019

Formal Concept Analysis

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“…data complexity (only w.r.t. the data size) for MARPL, an attributed logic based on Datalog (Marx, Krötzsch, and Thost 2017). Finally, we show lower complexity bounds in the case of ground ontologies.…”

Section: Reasoning In Dl-lite R @mentioning

confidence: 97%

Querying Attributed DL-Lite Ontologies Using Provenance Semirings

Bourgaux

Ozaki

2019

AAAI

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Attributed description logic is a recently proposed formalism, targeted for graph-based representation formats, which enriches description logic concepts and roles with finite sets of attribute-value pairs, called annotations. One of the most important uses of annotations is to record provenance information. In this work, we first investigate the complexity of satisfiability and query answering for attributed DL-LiteR ontologies. We then propose a new semantics, based on provenance semirings, for integrating provenance information with query answering. Finally, we establish complexity results for satisfiability and query answering under this semantics.

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“…Different other alternatives are surveyed in [4]. Newer approaches attach attributes to statements [37,47].…”

Section: Challenges In Knowledge Representationmentioning

confidence: 99%

Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases

Suchanek

Lajus

Boschin

et al. 2019

Lecture Notes in Computer Science

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Entity-centric knowledge bases are large collections of facts about entities of public interest, such as countries, politicians, or movies. They find applications in search engines, chatbots, and semantic data mining systems. In this paper, we first discuss the knowledge representation that has emerged as a pragmatic consensus in the research community of entity-centric knowledge bases. Then, we describe how these knowledge bases can be mined for logical rules. Finally, we discuss how entities can be represented alternatively as vectors in a vector space, by help of neural networks.

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Logic on MARS: Ontologies for Generalised Property Graphs

Cited by 18 publications

References 10 publications

Discovering Implicational Knowledge in Wikidata

Discovering Implicational Knowledge in Wikidata

Querying Attributed DL-Lite Ontologies Using Provenance Semirings

Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases

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