Vadalog: A modern architecture for automated reasoning with large knowledge graphs

Bellomarini, Luigi; Benedetto, Davide; Gottlob, Georg; Sallinger, Emanuel

doi:10.1016/j.is.2020.101528

Cited by 28 publications

(23 citation statements)

References 21 publications

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“…If x occurs only in invaded positions, x is an attacked harmful variable. Without loss of generality (as more complex joins can be broken into steps (Bellomarini et al 2020)), we define protected harmful join rule as a rule of the form:…”

Section: Relationship Between Shy and Wardedmentioning

confidence: 99%

“…Specifically, Shy was introduced as part of the parsimonious class (Leone et al 2012) and is adopted in the system DLV ∃ (Leone et al 2017). Likewise, Warded was recently introduced as fragment of the Datalog ± family (Calì et al 2010) and is implemented as logic core of the reasoner Vadalog system (Bellomarini et al 2020). Both find many industrial applications in the financial, media intelligence, security, logistics, pricing domains, and more (Berger et al 2019;Adrian et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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On the Relationship between Shy and Warded Datalog+/-

Baldazzi¹,

Bellomarini²,

Favorito³

et al. 2022

Preprint

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Datalog ∃ is the extension of Datalog with existential quantification. While its high expressive power, underpinned by a simple syntax and the support for full recursion, renders it particularly suitable for modern applications on knowledge graphs, query answering (QA) over such language is known to be undecidable in general. For this reason, different fragments have emerged, introducing syntactic limitations to Datalog ∃ that strike a balance between its expressive power and the computational complexity of QA, to achieve decidability. In this short paper, we focus on two promising tractable candidates, namely Shy and Warded Datalog ± . Reacting to an explicit interest from the community, we shed light on the relationship between these fragments. Moreover, we carry out an experimental analysis of the systems implementing Shy and Warded, respectively DLV ∃ and Vadalog.

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Section: Relationship Between Shy and Wardedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Relationship between Shy and Warded Datalog+/-

Baldazzi¹,

Bellomarini²,

Favorito³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hay gran cantidad de investigaciones que aplican el GC para representar el conocimiento (Bellomarini et al, 2020;Jia et al, 2017;Krenn & Zeilinger, 2020;Lin et al, 2017;Zhu et al, 2019). La estructura del GC facilita la utilización de ponderaciones que permiten obtener nuevo conocimiento y conclusiones de datos existentes Li & Madden, 2019;.…”

Section: Revisión De La Literaturaunclassified

Knowledge Graph to determine the domain of learning in Higher Education

Almazán

Vega

Amaya

et al. 2021

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The representing of a student’s knowledge in an academic discipline plays an important role in boosting the student’s skills. To support stakeholders in the educational domain, it is necessary to provide them with robust assessment strategies that facilitate the teaching-learning process. Student´s mastery is determined by the degree of knowledge, which demonstrates objectively, on the topics included in the different areas that make up an academic discipline. Although there is a wide variety of techniques to represent knowledge, particularly Knowledge Graph technique is becoming relevant due to the structured approach and benefits it offers. This paper proposes a method that classifies and weights the nodes (topics) of a Knowledge Graph of a disciplinary area, which is analyzed through a case study. The method has two approaches: avoid exhaustive evaluation of the nodes and weight the nodes with adequate precision. Method´s application is illustrated by a case study. As results, a Knowledge Graph is obtained with its classified and weighted nodes through the application of the proposed method, in which 100% of the topics have been impacted through the objective evaluation of 20.8% representing 10 nodes. It is concluded that the proposed method has potential to be used in the representation and management of knowledge, being necessary to improve phases’ iteration to condition number of objective nodes.

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“…Nowadays, the terms ontology or ontology language in the context of knowledge representation is most likely to remind us about the Web Ontology Language (OWL) from the Semantic Web [24] or, for the younger ones among us, of Knowledge Graphs (KGs). Already these two terms evoque a large variety of languages, from the different formalisms used in modern KGs [4,14], to the existing OWL 2 profiles [54], to the many description logic [2] formalisms that are less expressive than SROIQ [40], the logic underlying OWL 2. Despite this variety, one can easily think of features which are not covered by any one of these languages.…”

Section: Ontologiesmentioning

confidence: 99%

“…Each of these situations is then treated as a classical ontology containing all axioms which are assumed to hold, with its usual semantics and entailment relation. 4 At this point, every consequence can be given a probability which is based on the probabilities of the subontologies where the entailment holds. The only problem is to identify such probabilities, which depend on the relationships between axioms, and might not be completely obvious.…”

Section: Use the Right Probabilitiesmentioning

confidence: 99%

Introduction to Probabilistic Ontologies

Peñaloza

2020

Reasoning Web. Declarative Artificial Intelligence

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There is no doubt about it; an accurate representation of a real knowledge domain must be able to capture uncertainty. As the best known formalism for handling uncertainty, probability theory is often called upon this task, giving rise to probabilistic ontologies. Unfortunately, things are not as simple as they might appear, and different choices made can deeply affect the semantics and computational properties of probabilistic ontology languages. In this tutorial, we explore the main design choices available, and the situations in which they may be meaningful or not. We then dive deeper into a specific family of probabilistic ontology languages which can express logical and probabilistic dependencies between axioms.

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Vadalog: A modern architecture for automated reasoning with large knowledge graphs

Cited by 28 publications

References 21 publications

On the Relationship between Shy and Warded Datalog+/-

On the Relationship between Shy and Warded Datalog+/-

Knowledge Graph to determine the domain of learning in Higher Education

Introduction to Probabilistic Ontologies

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