MontoloStats - Ontology Modeling Statistics

Lieber, Sven; Meester, Ben De; Dimou, Anastasia; Verborgh, Ruben

doi:10.1145/3360901.3364433

Cited by 3 publications

(8 citation statements)

References 10 publications

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“…-RDF datasets instantiating expressions of this pattern (under the inferential closure) with smaller or bigger numbers of Abox statements. -Existing ontology axioms in whose right-hand sides 12 (RHS) an expression matching the given CE pattern appear less or more often. 11 As we exemplified (on 'cars with an accident') in Section 2.5, the presence of (trustworthy) Abox data should overrule the assumptions made based on the specific Tbox information (within w I (FC D, FC)) or by the CE pattern (within w P (pattern(D))).…”

Section: Weight Of Individual Category Vs Of Ce Patternmentioning

confidence: 99%

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Focused categorization power of ontologies: General framework and study on simple existential concept expressions

Svátek

Zamazal

Nguyen

et al. 2023

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When reusing existing ontologies for publishing a dataset in RDF (or developing a new ontology), preference may be given to those providing extensive subcategorization for important classes (denoted as focus classes). The subcategories may consist not only of named classes but also of compound class expressions. We define the notion of focused categorization power of a given ontology, with respect to a focus class and a concept expression language, as the (estimated) weighted count of the categories that can be built from the ontology’s signature, conform to the language, and are subsumed by the focus class. For the sake of tractable initial experiments we then formulate a restricted concept expression language based on existential restrictions, and heuristically map it to syntactic patterns over ontology axioms (so-called FCE patterns). The characteristics of the chosen concept expression language and associated FCE patterns are investigated using three different empirical sources derived from ontology collections: first, the concept expression pattern frequency in class definitions; second, the occurrence of FCE patterns in the Tbox of ontologies; and last, for class expressions generated from the Tbox of ontologies (through the FCE patterns); their ‘meaningfulness’ was assessed by different groups of users, yielding a ‘quality ordering’ of the concept expression patterns. The complementary analyses are then compared and summarized. To allow for further experimentation, a web-based prototype was also implemented, which covers the whole process of ontology reuse from keyword-based ontology search through the FCP computation to the selection of ontologies and their enrichment with new concepts built from compound expressions.

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Section: Weight Of Individual Category Vs Of Ce Patternmentioning

confidence: 99%

“…( 3)), we may afford such a simplification. 12 By the RHS we mean the syntactic RHS as indicated in the code. For the overwhelming majority of axioms inside the OWL code available in ontology documents, the syntactic LHS is a named class.…”

Section: Weight Of Individual Category Vs Of Ce Patternmentioning

confidence: 99%

Focused categorization power of ontologies: General framework and study on simple existential concept expressions

Svátek

Zamazal

Nguyen

et al. 2023

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“…More than eighty constraint types were identified [7] from which a subset is used as axioms in ontologies [36] and a subset motivated the creation of SHACL [60]. Existing approaches to generate RDF constraints use UML diagrams or ontologies as source but usually cover only a limited subset of SHACL core constraint types due to an incomplete mapping.…”

Section: Creating Constraintsmentioning

confidence: 99%

“…Manually created RDF constraints are theoretically not limited by any mapping as a user potentially can use all constraint types of a specification. However, similar to ontology axioms [36] only a subset seems to find common use. In our previous work [37] and later updated and extended statistics, 5 we investigated the use of constraint types in SHACL shapes.…”

Section: Creating Constraintsmentioning

confidence: 99%

“…Certain constraint types are represented using the visual variables border, edge and position but to reduce graphic complexity most of the 31 constraint types are represented textually within note-elements. However, constraint types may also be accompanied by an icon which we provide for commonly used constraint types [36] (see Fig. 1) not visualized using other visual variables such as position (see next section).…”

Section: Semiotic Claritymentioning

confidence: 99%

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Visual notations for viewing RDF constraints with UnSHACLed

Lieber

Meester

Heyvaert

et al. 2022

Self Cite

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The quality of knowledge graphs can be assessed by a validation against specified constraints, typically use-case specific and modeled by human users in a manual fashion. Visualizations can improve the modeling process as they are specifically designed for human information processing, possibly leading to more accurate constraints, and in turn higher quality knowledge graphs. However, it is currently unknown how such visualizations support users when viewing RDF constraints as no scientific evidence for the visualizations’ effectiveness is provided. Furthermore, some of the existing tools are likely suboptimal, as they lack support for edit operations or common constraints types. To establish a baseline, we have defined visual notations to represent RDF constraints and implemented them in UnSHACLed, a tool that is independent of a concrete RDF constraint language. In this paper, we (i) present two visual notations that support all SHACL core constraints, built upon the commonly used visualizations VOWL and UML, (ii) analyze both notations based on cognitive effective design principles, (iii) perform a comparative user study between both visual notations, and (iv) present our open source tool UnSHACLed incorporating our efforts. Users were presented RDF constraints in both visual notations and had to answer questions based on visualization task taxonomies. Although no statistical significant difference in mean error rates was observed, all study participants preferred ShapeVOWL in a self assessment to answer RDF constraint-related questions. Furthermore, ShapeVOWL adheres to more cognitive effective design principles according to our performed comparison. Study participants argued that the increased visual features of ShapeVOWL made it easier to spot constraints, but a list of constraints – as in ShapeUML – is easier to read. However, also that more deviations from the strict UML specification and introduction of more visual features can improve ShapeUML. From these findings we conclude that ShapeVOWL has a higher potential to represent RDF constraints more effective compared to ShapeUML. But also that the clear and efficient text encoding of ShapeUML can be improved with visual features. A one-size-fits-all approach to RDF constraint visualization and editing will be insufficient. Therefore, to support different audiences and use cases, user interfaces of RDF constraint editors need to support different visual notations.

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Ontology Modelling for Metallurgy as a Domain and Retrieval Using Particle Swarm Optimization

Deepak

Kumar

Santhanavijayan

et al. 2021

Machine Learning Approaches for Improvising Modern Learning Systems

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Ontologies are the entities that are built to gather knowledge from the chosen domain and make it available to both machines and humans. In this chapter, an ontology that structures all the processes involved in the metallurgical extraction of the metals has been modelled. Ontology Development 101 methodology with minor modifications has been adopted to model the ontologies for metallurgy as the principal domain, and the processes involved in the various fields of metallurgy have been modelled into an ontology by defining the classes and the relationship between them. The model is implemented using Protégé and quantitatively evaluated using the semiotics approach. To comprehensively demonstrate the applicability of the developed ontology, an algorithm for retrieval of entities from the web incorporating particle swarm optimization and that makes use of the modelled ontology has been proposed. Finally, the quality of the modelled ontology and as well as the performance efficiency of the entity retrieval has been quantitatively evaluated.

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MontoloStats - Ontology Modeling Statistics

Cited by 3 publications

References 10 publications

Focused categorization power of ontologies: General framework and study on simple existential concept expressions

Focused categorization power of ontologies: General framework and study on simple existential concept expressions

Visual notations for viewing RDF constraints with UnSHACLed

Ontology Modelling for Metallurgy as a Domain and Retrieval Using Particle Swarm Optimization

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