Rule Induction and Reasoning over Knowledge Graphs

Stepanova, Daria; Gad-Elrab, Mohamed H.; Ho, Vinh Thinh

doi:10.1007/978-3-030-00338-8_6

Cited by 18 publications

(9 citation statements)

References 49 publications

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“…This approach, born in the field of data mining, is relevant for the task of building DL ontologies, as it can effectively find interesting relationships between concept and role names. However, it lacks support for mining CIs with existential quantifiers on the right-hand side [45].…”

Section: Building DL Ontologiesmentioning

confidence: 99%

Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

Ozaki

2020

Künstl Intell

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The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.

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Section: Building DL Ontologiesmentioning

confidence: 99%

Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

Ozaki

2020

Künstl Intell

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“…For example, if the weather forecast predicts rain and planned activities are likely to involve covering some distance on foot, then one conclusion may be that taking an umbrella is an appropriate course of action. Known variants of evidence-based inference include automatic rule induction (Stepanova et al 2018) and case-based reasoning (Kolodner 2014).…”

Section: Technology: Approaches To Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence – The Big Picture

Abele¹,

D’Onofrio

2020

Cognitive Computing

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This article presents an introduction to key concepts in the field of artificial intelligence and how they relate to each other. It offers a scheme to assess the maturity levels of current and future technologies or applications and lays out why today's solutions cannot be reasonably considered intelligent. At its heart, the article includes an overview of fundamental solution approaches, the resulting capabilities, required enablers and the critical aspects of robustness, transparency and trust of produced results. Using novel ways to organize existing knowledge, it aims to enable the reader to accurately separate different notions and to gain a broad understanding of the key concepts under consideration. Based on a more comprehensive framework, it is deliberately limited in scope to provide a well-rounded overview to help the reader identify topics on which more detailed information should be sought out.Vollständig neuer Beitrag.

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“…Classical ILP systems such as FOIL (Quinlan 1990) and Progol (Muggleton 1995) usually apply exhaustive algorithms to mine rules for the given data and either require false facts as counter-examples or assume a closed world (for an overview of classical ILP systems see Table 2 in (Stepanova, Gad-Elrab, and Ho 2018)). The closed-world assumption (CWA) states that all facts that are not explicitly given as true are assumed to be false.…”

Section: A Rule Learning Approachesmentioning

confidence: 99%

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools

Cornelio,

Thost

2019

Preprint

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Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise, and is further challenged by today's often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts, have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems.

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Rule Induction and Reasoning over Knowledge Graphs

Cited by 18 publications

References 49 publications

Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

Artificial Intelligence – The Big Picture

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools

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