Extraction of Common Conceptual Components from Multiple Ontologies

Asprino, Luigi; Carriero, Valentina Anita; Presutti, Valentina

doi:10.1145/3460210.3493542

Cited by 4 publications

(8 citation statements)

References 10 publications

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“…axioms) as statements that can be annotated. Moreover, we also use the OPLaX ontology 12 [2], which is a language explicitly designed for annotating ontology design patterns. As a result, each OWL pattern will be included in a file containing all its probabilistic axioms.…”

Section: Select Relevant Classes From the Domain Subgraphmentioning

confidence: 99%

Empirical ontology design patterns and shapes from Wikidata

Carriero,

Groth,

Presutti

2024

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The ontology underlying the Wikidata knowledge graph (KG) has not been formalized. Instead, its semantics emerges bottom-up from the use of its classes and properties. Flexible guidelines and rules have been defined by the Wikidata project for the use of its ontology, however, it is still often difficult to reuse the ontology’s constructs. Based on the assumption that identifying ontology design patterns from a knowledge graph contributes to making its (possibly) implicit ontology emerge, in this paper we present a method for extracting what we term empirical ontology design patterns (EODPs) from a knowledge graph. This method takes as input a knowledge graph and extracts EODPs as sets of axioms/constraints involving the classes instantiated in the KG. These EODPs include data about the probability of such axioms/constraints happening. We apply our method on two domain-specific portions of Wikidata, addressing the music and art, architecture, and archaeology domains, and we compare the empirical ontology design patterns we extract with the current support present in Wikidata. We show how these patterns can provide guidance for the use of the Wikidata ontology and its potential improvement, and can give insight into the content of (domain-specific portions of) the Wikidata knowledge graph.

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Section: Select Relevant Classes From the Domain Subgraphmentioning

confidence: 99%

Empirical ontology design patterns and shapes from Wikidata

Carriero,

Groth,

Presutti

2024

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“…strings), and edges represent semantic relations (properties) between nodes. While the property ex:livesIn has been invented for this example, the rdf:type property is part of the RDF built-in vocabulary 3 and states that the subject is an instance of a class. RDF Schema (RDFS) 4 vocabulary is an extension of the basic RDF vocabulary, and allows for a better structuring of resources by e.g.…”

Section: The Semantic Web: Principles and Standardsmentioning

confidence: 99%

“…While the property ex:livesIn has been invented for this example, the rdf:type property is part of the RDF built-in vocabulary 3 and states that the subject is an instance of a class. RDF Schema (RDFS) 4 vocabulary is an extension of the basic RDF vocabulary, and allows for a better structuring of resources by e.g. supporting the definition of hierarchies of both classes and properties (rdfs:subClassOf, rdfs:subPropertyOf).…”

Section: The Semantic Web: Principles and Standardsmentioning

confidence: 99%

Empirical Ontology Design Patterns

Carriero

2024

Studies on the Semantic Web

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“…A technique for extracting common conceptual components (CC) from a corpus of ontologies is proposed in [24]. CCs are general concepts such as membership, participation, authorship, etc.…”

Section: ) Observing Patterns and Modelling Style In Lodmentioning

confidence: 99%

“…90% of examples for training, 10% for testing), six multi-label classification algorithms: Random Forest, K-nearest neighbours, Extra Trees, XGBoost, Ada Boost and Multi-layer Perceptron. We refer to the scikit-learn 24 and XGBoost 25 libraries for the implementation of the algorithms. All the algorithms are initialised with default hyperparameters.…”

Section: A Preprocessingmentioning

confidence: 99%

Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

Asprino

Presutti

2023

IEEE Access

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Linked Open Data (LOD) is the largest, collaborative, distributed, and publicly-accessible Knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character.INDEX TERMS Intelligent web services and semantic web, knowledge engineering methodologies, knowledge and data engineering tools and techniques.

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Extraction of Common Conceptual Components from Multiple Ontologies

Cited by 4 publications

References 10 publications

Empirical ontology design patterns and shapes from Wikidata

Empirical ontology design patterns and shapes from Wikidata

Empirical Ontology Design Patterns

Observing LOD: Its Knowledge Domains and the Varying Behavior of Ontologies Across Them

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