An ontology for enterprise and information systems modelling

Opdahl, Andreas L.; Berio, Giuseppe; Harzallah, Mounira; Matulevičius, Raimundas

doi:10.3233/ao-2011-0101

Cited by 24 publications

(10 citation statements)

References 71 publications

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“…Additionally, several works define or reuse a core ontology to identify and further define the domain concepts by specialization. For instance, on the one hand, almost all OBOs (Open Biomedical Ontologies) have been originated by importing the BFO (Basic Formal Ontology) and the RO (Relation Ontology); Opdhal et al [39] used BWW (Bunge Wand Weber) ontology to build the UEML ontology; Chulyadyo et al [7] improved the ontology flatness by inferring hypernym relation between extracted terms and core concepts. On the other hand, some works map a core ontology to a given domain ontology, so as to better define the concepts of the domain and superimpose a structure of one domain ontology.…”

Section: Core 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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Section: Core 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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“…The mediator's role is to serve as the basis to which all the modelling languages are mapped, and to ensure the consistency between the models. In UEML, the unified 'ontology' (Opdahl et al, 2012) plays this role. However, it requires the languages to be (re)defined in accordance with the specific grammar, as well as in full formal precision, given that the approach targets the semantic interoperability of tools and associated languages.…”

Section: Model Integrationmentioning

confidence: 99%

Challenges of Modelling LandscapesPragmatics Swept Under the Carpet?

Proper

Bjekovic

2013

Proceedings of the Third International Symposium on Proceedings of the Third International Symposium on Business Modeling and S

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In enterprise modelling, a wide range of models and languages is used to support different purposes. If left uncontrolled, this can easily result in a fragmented perspective on the enterprise, its processes and IT support. On its turn, this negatively affects traceability, the ability to do crosscutting analysis, and the overall coherence of models. Different strategies are suggested to achieve model integration. They mainly address syntactic-semantics aspects of models/languages, and only to a limited extent their pragmatics. In actual use, the 'standardising' and 'integrating' effects of traditional approaches (e.g. UML, ArchiMate) erodes. This is typically manifested by the emergence of local 'dialects', 'light weight versions', as well as extensions of the standard to cover 'missing aspects'. This paper aims to create more awareness of the factors that are at play when creating integrated modelling landscapes. Relying on our ongoing research, we develop a fundamental understanding of the driving forces and challenges related to modelling and linguitic variety within modelling landscapes. In particular, the paper discusses the effect of a priori fixed languages in modelling and model integration efforts, and argues that they bring about the risk of neglecting the pragmatic richness needed across practical modelling situations.

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“…The ontology and modeling of entities and processes/events has been well-studied, and we point to Unified Enterprise Modelling Ontology (UEMO) [21] for (among others) a rich OWL axiomatization of commonly useful constructs.…”

Section: Fig 2 Bim Concepts and Relationshipsmentioning

confidence: 99%

“…The UEML (Unified Enterprise Modelling Language) project has led to, among others, the creation of an ontology UEMO (Unified Enterprise Modelling Ontology) [21], which is philosophically founded in Bunge's work [25] and has precise semantics. The purpose of UEML/UEMO is to help align modeling languages (for which a special methodology is provided).…”

Section: Table 3 Coverage Of Concepts In Existing Business Languages Using Bim Conceptsmentioning

confidence: 99%

Making Data Meaningful: The Business Intelligence Model and Its Formal Semantics in Description Logics

Horkoff

Borgida

Mylopoulos

et al. 2012

On the Move to Meaningful Internet Systems: OTM 2012

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Business Intelligence (BI) offers great opportunities for strategic analysis of current and future business operations; however, existing BI tools typically provide data-oriented responses to queries, difficult to understand in terms of business objectives and strategies. To make BI data meaningful, we need a conceptual modeling language whose primitive concepts represent business objectives, processes, opportunities and threats. We have previously introduced such a language, the Business Intelligence Model (BIM). In this paper we consolidate and rationalize earlier work on BIM, giving a precise syntax, reducing the number of fundamental concepts by using meta-attributes, and introducing the novel notion of "pursuit". Significantly, we also provide a formal semantics of BIM using a subset of the OWL Description Logic (DL). Using this semantics as a translation, DL reasoners can be exploited to (1) propagate evidence and goal pursuit in support of "what if?" reasoning, (2) allow extensions to the BIM language, (3) detect inconsistencies in specific BIM models, and (4) automatically classify defined concepts relative to existing concepts, organizing the model.

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An ontology for enterprise and information systems modelling

Cited by 24 publications

References 71 publications

Towards a Term Clustering Framework for Modular Ontology Learning

Towards a Term Clustering Framework for Modular Ontology Learning

Challenges of Modelling LandscapesPragmatics Swept Under the Carpet?

Making Data Meaningful: The Business Intelligence Model and Its Formal Semantics in Description Logics

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