Application of Ontology Modularization for Building a Criminal Domain Ontology

Ghosh, Mirna El; Naja, Hala; Abdulrab, Habib; Khalil, Mohamad

doi:10.1007/978-3-030-00178-0_27

Cited by 3 publications

(5 citation statements)

References 42 publications

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“…Large ontologies can incorporate concerns from different generality levels ranging from domain-specific concerns (e.g., what kinds of elements are involved in a software service), to concerns that crosscut different domains (e.g., what is a service), to domain-independent ones (e.g., how to differentiate and characterize objects and events). Some approaches (e.g., [15]) use an understanding of ontologies at these different levels (i.e., foundational, core, and domain ontologies) as a strategy for model breakdown. Another example that takes a similar level-based approach for modularizing ontology networks is proposed in [41].…”

Section: Complexity Management Of Conceptual Modelsmentioning

confidence: 99%

“…None of the aforementioned approaches is fully automated. Approaches such as [14] assume the existence of additional supporting models (enterprise architecture models) and require an understanding of the mapping between these models and the model to be modularized; approaches such as [15] require a human interpretation of the nonconsensual classification of ontologies in different generality levels. Finally, approaches for ontology module extraction and forgetting rely on the user input of seed signatures, which can be a problem in the case of large models.…”

Section: Complexity Management Of Conceptual Modelsmentioning

confidence: 99%

“…-"Coherence of the unifying concept-relators" (subject 4, who reported to have no knowledge of OntoUML); -"ALT-A is the best one. It is organized by the events, 14 what made me better understand the different model topics" (subject 9); -"Association classes (although not represented as they should, imho 15 ), should not be broken down by modularization. Inheritance hierarchies should also be kept together, whenever possible."…”

Section: Analysis Of the Motivations For The Rankingsmentioning

confidence: 99%

“…And for example employee is clearly 14 As discussed in [23,29], relators and events form a duality that is often manifest in terms of systematic polysemy in language (e.g., "marriage as a relator bundling mutual rights and obligations of the spouses" versus "marriage as a process aggregating the actions of the spouses qua players of these roles"; "service as a contractual agreement" versus "service as a service delivery event"). 15 Association classes were introduced in UML to address one of the functions of relators (namely, association reification) but with a poorer semantics [31]. It is natural that UML users interpret relators as association classes and, in doing so, resist to what they interpret as "change in notation".…”

Section: Analysis Of the Motivations For The Rankingsmentioning

confidence: 99%

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Automated conceptual model clustering: a relator-centric approach

et al. 2021

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In recent years, there has been a growing interest in the use of reference conceptual models to capture information about complex and sensitive business domains (e.g., finance, healthcare, space). These models play a fundamental role in different types of critical semantic interoperability tasks. Therefore, domain experts must be able to understand and reason with their content. In other words, these models need to be cognitively tractable . This paper contributes to this goal by proposing a model clustering technique that leverages on the rich semantics of ontology-driven conceptual models (ODCM). In particular, we propose a formal notion of Relational Context to guide the automated clusterization (or modular breakdown) of conceptual models. Such Relational Contexts capture all the information needed for understanding entities “qua players of roles” in the scope of an objectified (reified) relationship (relator). The paper also presents computational support for automating the identification of Relational Contexts and this modular breakdown procedure. Finally, we report the results of an empirical study assessing the cognitive effectiveness of this approach.

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Section: Complexity Management Of Conceptual Modelsmentioning

confidence: 99%

Section: Complexity Management Of Conceptual Modelsmentioning

confidence: 99%

Section: Analysis Of the Motivations For The Rankingsmentioning

confidence: 99%

Section: Analysis Of the Motivations For The Rankingsmentioning

confidence: 99%

See 2 more Smart Citations

Automated conceptual model clustering: a relator-centric approach

et al. 2021

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“…Moreover, the modular representations are easier to understand, reason with, extend and reuse [24]. Therefore, using these representations tends to reduce the complexity of designing and to facilitate ontology reasoning, development, and integration [13].…”

Section: Modular Ontologymentioning

confidence: 99%

Towards a Term Clustering Framework for Modular Ontology Learning

Harzallah

Guillet

et al. 2020

Communications in Computer and Information Science

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This paper aims to analyze and adopt the term clustering method for building a modular ontology according to its core ontology. The acquisition of semantic knowledge focuses on noun phrase appearing with the same syntactic roles in relation to a verb or its preposition combination in a sentence. The construction of this co-occurrence matrix from context helps to build feature space of noun phrases, which is then transformed to several encoding representations including feature selection and dimensionality reduction. In addition, word embedding techniques are also presented as feature representation. These representations are clustered respectively with K-Means, K-Medoids, Affinity Propagation, DBscan and co-clustering algorithms. The feature representation and clustering methods constitute the major sections of term clustering frameworks. Due to the randomness of clustering approaches, iteration efforts are adopted to find the optimal parameter and provide convinced value for evaluation. The DBscan and affinity propagation show their outstanding effectiveness for term clustering and NMF encoding technique and word embedding representation are salient by its promising facilities in feature compression.

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The Application of ODCM for Building Well-Founded Legal Domain Ontologies

Ghosh

Abdulrab

2019

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law

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Application of Ontology Modularization for Building a Criminal Domain Ontology

Cited by 3 publications

References 42 publications

Automated conceptual model clustering: a relator-centric approach

Automated conceptual model clustering: a relator-centric approach

Towards a Term Clustering Framework for Modular Ontology Learning

The Application of ODCM for Building Well-Founded Legal Domain Ontologies

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