Attributed Description Logics: Reasoning on Knowledge Graphs

Krötzsch, Markus; Marx, Maximilian; Ozaki, Ana; Thost, Veronika

doi:10.24963/ijcai.2018/743

Cited by 25 publications

(11 citation statements)

References 12 publications

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“…Particularly interesting topics for knowledge graphs arise from the intersections of areas. In the intersection of data graphs and deductive knowledge, we emphasise emerging topics such as formal semantics for property graphs, with languages that can take into account the meaning of labels and property-value pairs on nodes and edges [74]; and reasoning and querying over contextual data, to derive conclusions and results valid in a particular setting [58,120,156]. In the intersection of data graphs and inductive knowledge, we highlight topics such as similarity-based query relaxation, allowing to find approximate answers to exact queries based on numerical representations (e.g., embeddings) [139]; shape induction, to extract and formalise inherent patterns in the knowledge graph as constraints [82]; and contextual knowledge graph embeddings that provide numeric representations of nodes and edges that vary with time, place, and so on [67,154].…”

Section: Discussionmentioning

confidence: 99%

Knowledge Graphs

et al. 2021

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In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

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Section: Discussionmentioning

confidence: 99%

Knowledge Graphs

et al. 2021

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“…Krötzsch et al [48] propose an approach for adding annotations in a logic-based reading of KGs. While that model formally introduces annotations to local description logic axioms and assertions, it does not provide definite criteria for knowledge propagation across contextual structures as in the KG-OLAP coverage hierarchy (cf.…”

Section: Contextualized Knowledge Graphsmentioning

confidence: 99%

Knowledge Graph OLAP

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Neumayr

et al. 2021

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A knowledge graph (KG) represents real-world entities and their relationships. The represented knowledge is often context-dependent, leading to the construction of contextualized KGs. The multidimensional and hierarchical nature of context invites comparison with the OLAP cube model from multidimensional data analysis. Traditional systems for online analytical processing (OLAP) employ multidimensional models to represent numeric values for further analysis using dedicated query operations. In this paper, along with an adaptation of the OLAP cube model for KGs, we introduce an adaptation of the traditional OLAP query operations for the purposes of performing analysis over KGs. In particular, we decompose the roll-up operation from traditional OLAP into a merge and an abstraction operation. The merge operation corresponds to the selection of knowledge from different contexts whereas abstraction replaces entities with more general entities. The result of such a query is a more abstract, high-level view – a management summary – of the knowledge.

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“…The third family is in a much more experimental stage than the previous families, and consists of attributed DLs [250,314]. Those have been designed to represent and reason with meta-knowledge, in the presence of knowledge graphs such as Wikidata [380] and DBpedia [256].…”

Section: Ontology Languagesmentioning

confidence: 99%