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Journal articles on the topic 'Formal ontologies'

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Published: 10 March 2023

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1

Sanfilippo, Emilio M., Yoshinobu Kitamura, and Robert I. M. Young. "Formal ontologies in manufacturing." Applied Ontology 14, no. 2 (April 25, 2019): 119–25. http://dx.doi.org/10.3233/ao-190209.

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Otte, J. Neil, John Beverley, and Alan Ruttenberg. "BFO: Basic Formal Ontology1." Applied Ontology 17, no. 1 (March 15, 2022): 17–43. http://dx.doi.org/10.3233/ao-220262.

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Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-six classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using different top-level ontologies.
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Lumb, L. I., J. R. Freemantle, J. I. Lederman, and K. D. Aldridge. "Annotation modeling with formal ontologies: Implications for informal ontologies." Computers & Geosciences 35, no. 4 (April 2009): 855–61. http://dx.doi.org/10.1016/j.cageo.2008.03.009.

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Sanfilippo, Emilio, and Walter Terkaj. "Editorial: Formal Ontologies meet Industry." Procedia Manufacturing 28 (2019): 174–76. http://dx.doi.org/10.1016/j.promfg.2018.12.028.

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Abrusci, V. Michele, Christophe Fouqueré, and Marco Romano. "Formal Ontologies and Coherent Spaces." Journal of Applied Logic 12, no. 1 (March 2014): 67–74. http://dx.doi.org/10.1016/j.jal.2013.07.003.

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Lukashevich, N. V. "Concepts in formal and linguistic ontologies." Automatic Documentation and Mathematical Linguistics 45, no. 4 (August 2011): 155–62. http://dx.doi.org/10.3103/s0005105511040030.

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Shaked, Avi, and Oded Margalit. "Sustainable Risk Identification Using Formal Ontologies." Algorithms 15, no. 9 (September 2, 2022): 316. http://dx.doi.org/10.3390/a15090316.

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The cyber threat landscape is highly dynamic, posing a significant risk to the operations of systems and organisations. An organisation should, therefore, continuously monitor for new threats and properly contextualise them to identify and manage the resulting risks. Risk identification is typically performed manually, relying on the integration of information from various systems as well as subject matter expert knowledge. This manual risk identification hinders the systematic consideration of new, emerging threats. This paper describes a novel method to promote automated cyber risk identification: OnToRisk. This artificial intelligence method integrates information from various sources using formal ontology definitions, and then relies on these definitions to robustly frame cybersecurity threats and provide risk-related insights. We describe a successful case study implementation of the method to frame the threat from a newly disclosed vulnerability and identify its induced organisational risk. The case study is representative of common and widespread real-life challenges, and, therefore, showcases the feasibility of using OnToRisk to sustainably identify new risks. Further applications may contribute to establishing OnToRisk as a comprehensive, disciplined mechanism for risk identification.
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Jongeling, T. B., and P. P. Kirschenmann. "FORMAL AND HYPOTHETICAL OR HEURISTIC ONTOLOGIES." Grazer Philosophische studien 29, no. 1 (August 13, 1987): 217–23. http://dx.doi.org/10.1163/18756735-90000322.

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Jansen, L., and S. Schulz. "Formal Ontologies in Biomedical Knowledge Representation." Yearbook of Medical Informatics 22, no. 01 (August 2013): 132–46. http://dx.doi.org/10.1055/s-0038-1638845.

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Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.
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Müller, R., O. Mailahn, and R. Peifer. "Tool: Eine Sprachdomäne für die Montageplanung*/A domain specific language for assembly planning – Software-supported planning of human-robot cooperation based on ontologies." wt Werkstattstechnik online 108, no. 09 (2018): 606–10. http://dx.doi.org/10.37544/1436-4980-2018-09-42.

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Die Planung von Montagesystemen wird durch die Einführung von cyber-physischen Modulen und neuen Formen der Zusammenarbeit von Mensch und Roboter zunehmend komplexer. Ontologien können Planungswissen bezüglich Beziehungen und Restriktionen formal abbilden. Mit der hier beschriebenen Sprachdomäne werden Ontologien für Montageplaner zugänglich und anwendbar. Die Planung kann auf diese Weise beschleunigt und flexibilisiert werden.   The planning of assembly systems is becoming increasingly complex with the introduction of cyber-physical modules and new forms of human-robot cooperation. Ontologies can formally capture planning knowledge in terms of relationships and restrictions. The domain specific language described here makes ontologies accessible and usable for assembly planners. Thus, planning may be accelerated and designed more flexibly.
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Smaili, Fatima Zohra, Xin Gao, and Robert Hoehndorf. "Formal axioms in biomedical ontologies improve analysis and interpretation of associated data." Bioinformatics 36, no. 7 (December 10, 2019): 2229–36. http://dx.doi.org/10.1093/bioinformatics/btz920.

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Abstract Motivation Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns and encode domain background knowledge. The domain knowledge of biomedical ontologies may have also the potential to provide background knowledge for machine learning and predictive modelling. Results We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein–protein interactions and gene–disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies. Availability and implementation https://github.com/bio-ontology-research-group/tsoe. Supplementary information Supplementary data are available at Bioinformatics online.
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Husáková, Martina, and Vladimír Bureš. "Formal Ontologies in Information Systems Development: A Systematic Review." Information 11, no. 2 (January 27, 2020): 66. http://dx.doi.org/10.3390/info11020066.

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Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.
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Samoilov, D. E., V. A. Semenova, and S. V. Smirnov. "incomplete data analysis for building formal ontologies." Ontology of designing 6, no. 3 (2016): 317–39. http://dx.doi.org/10.18287/2223-9537-2016-6-3-317-339.

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Musen, M. A. "Domain Ontologies in Software Engineering: Use of Protégé with the EON Architecture." Methods of Information in Medicine 37, no. 04/05 (October 1998): 540–50. http://dx.doi.org/10.1055/s-0038-1634543.

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AbstractDomain ontologies are formal descriptions of the classes of concepts and the relationships among those concepts that describe an application area. The Protege software-engineering methodology provides a clear division between domain ontologies and domain-independent problemsolvers that, when mapped to domain ontologies, can solve application tasks. The Protege approach allows domain ontologies to inform the total software-engineering process, and for ontologies to be shared among a variety of problem-solving components. We illustrate the approach by describing the development of EON, a set of middleware components that automate various aspects of protocol-directed therapy. Our work illustrates the organizing effect that domain ontologies can have on the software-development process. Ontologies, like all formal representations, have limitations in their ability to capture the semantics of application areas. Nevertheless, the capability of ontologies to encode clinical distinctions not usually captured by controlled medical terminologies provides significant advantages for developers and maintainers of clinical software applications.
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An, Yoo Jung, Kuo-Chuan Huang, Soon Ae Chun, and James Geller. "A Formal Approach to Evaluating Medical Ontology Systems using Naturalness." International Journal of Computational Models and Algorithms in Medicine 1, no. 1 (January 2010): 1–18. http://dx.doi.org/10.4018/jcmam.2010072001.

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Ontologies, terminologies and vocabularies are popular repositories for collecting the terms used in a domain.It may be expected that in the future more such ontologies will be created for domain experts. However, there is increasing interest in making the language of experts understandable to casual users. For example, cancer patients often research their cases on the Web. The authors consider the problem of objectively evaluating the quality of ontologies (QoO). This article formalizes the notion of naturalness as a component of QoO and quantitatively measures naturalness for well-known ontologies (UMLS, WordNet, OpenCyc) based on their concepts, IS-A relationships and semantic relationships. To compute numeric values characterizing the naturalness of an ontology, this article defines appropriate metrics. As absolute numbers in such a pursuit are often meaningless, we concentrate on using relative naturalness metrics. That allows us to say that a certain ontology is relatively more natural than another one.
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Ma, Zongmin, Haitao Cheng, and Li Yan. "Automatic Construction of OWL Ontologies From Petri Nets." International Journal on Semantic Web and Information Systems 15, no. 1 (January 2019): 21–51. http://dx.doi.org/10.4018/ijswis.2019010102.

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Ontology, as a formal representation method of domain knowledge, plays a particular important key role in semantic web. How to construct ontologies has become a key technology in the semantic web, especially constructing ontologies from existing domain knowledge. Currently, Petri nets have been a mathematical modeling tool, and have been widely studied and successfully applied in modeling of software engineering, database and artificial intelligence. In particular, PNML (Petri Net Markup Language) language has been a part of ISO/IEC Petri nets standard for representing and exchanging data on Petri nets. Therefore, how to construct ontologies from PNML model of Petri nets needs to be investigated. In this article, the authors investigate a method for automatic construction of web ontology language (OWL) ontologies from PNML of Petri nets. Firstly, this paper gives a formal definition and the semantics of PNML models of Petri nets. On this basis, a formal approach for constructing OWL ontologies from PNML model of Petri nets is proposed, i.e., this paper transforms Petri nets (including PNML model and PNML document of the Petri nets) into OWL ontologies at both structure and instance levels. Furthermore, the correctness of the transformation is proved. Finally, a prototype construction tool called PN2OWL is developed to transform Petri nets models into OWL ontologies automatically.
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Nanda, Jyotirmaya, Timothy W. Simpson, Soundar R. T. Kumara, and Steven B. Shooter. "A Methodology for Product Family Ontology Development Using Formal Concept Analysis and Web Ontology Language." Journal of Computing and Information Science in Engineering 6, no. 2 (December 23, 2005): 103–13. http://dx.doi.org/10.1115/1.2190237.

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The use of ontologies for information sharing is well documented in the literature, but the lack of a comprehensive and systematic methodology for constructing product ontologies has limited the process of developing ontologies for design artifacts. In this paper we introduce the Product Family Ontology Development Methodology (PFODM), a novel methodology to develop formal product ontologies using the Semantic Web paradigm. Within PFODM, Formal Concept Analysis (FCA) is used first to identify similarities among a finite set of design artifacts based on their properties and then to develop and refine a product family ontology using Web Ontology Language (OWL). A family of seven one-time-use cameras is used to demonstrate the steps of the PFODM to construct such an ontology. The benefit of PFODM lies in providing a systematic and consistent methodology for constructing ontologies to support product family design. The resulting ontologies provide a hierarchical conceptual clustering of related design artifacts, which is particularly advantageous for product family design where parts, processes, and most important, information is intentionally shared and reused to reduce complexity, lead-time, and development costs. Potential uses of the resulting ontologies and FCA representations within product family design are also discussed.
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Song, Hua Zhu, Cong Xiao, and Lu Xu. "Ontology-Based Semantic Similarity Measure with Concept Lattice." Applied Mechanics and Materials 411-414 (September 2013): 177–81. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.177.

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Semantic similarity measure has always been one of the important contents in artificial intelligence. This paper puts the ontology as the research object, and measure the semantic similarity between two ontologies in view of concept lattice. Firstly, concept lattice is introduced to similarity measure, and the thought of the ontology-based semantic similarity measure with concept lattice was given. Next, the solution of the measure was described, which includes generating a formal context of heterogeneous ontologies, constructing the corresponding formal context of formal context to fetch the formal context in concept lattice, and using irreducible infimum theory to calculate the similarity value of heterogeneous ontology concept. Finally, we employed a sample to verify the measure method. The results showed the method can effectively compute the semantic similarity between the ontologies, and the method proposed is valid and feasible.
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Vita, Randi J., James A. Overton, Kei-Hoi Cheung, Patrick Dunn, Julie Burel, Syed Ahmad Chan, Alexander D. Diehl, Steven H. Kleinstein, Alessandro Sette, and Bjoern Peters. "Formal representation of immunology related data with ontologies." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 130.26. http://dx.doi.org/10.4049/jimmunol.202.supp.130.26.

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Abstract The Human Immunology Project Consortium (HIPC) is a multicenter collaboration between research centers performing large-scale human immunology studies that focus on profiling the human immune response to natural infection and vaccination. “Immune exposures” are events such as natural infection and vaccination whereby the immune system may or may not respond to the exposure. Many of the HIPC studies investigate the response of specific cell populations after a variety of immune exposures. In order to cross-compare results from the many different centers and projects, we established a standardized representation of immune exposures and cell descriptions that simplifies data collection. By standardizing how this data is collected and stored, the vast amount of data collected by these diverse projects is made significantly more useful and interoperable. The data collected by the HIPC projects is stored in the National Institute of Health, Division of Allergy, Immunology and Transplantation funded resource, the Immunology Database and Analysis Portal (ImmPort). ImmPort was modified to provide the necessary structured data fields to capture our standardized representation of immune exposures and studied cell populations with a set of data fields that primarily utilize formal ontology terms. We will discuss the process of modeling immune exposures and cell populations via ontology terms, including real life scenarios from HIPC projects, as well as collaborations with existing ontology projects in order to meet the specific needs of immunologists.
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de Rozario, Richard. "Matching a Trope Ontology to the Basic Formal Ontology." Philosophies 4, no. 3 (July 18, 2019): 40. http://dx.doi.org/10.3390/philosophies4030040.

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Applied ontology, at the foundational level, is as much philosophy as engineering and as such provides a different aspect of contemporary natural philosophy. A prominent foundational ontology in this field is the Basic Formal Ontology (BFO). It is important for lesser known ontologies, like the trope ontology of interest here, to match to BFO because BFO acts like the glue between many disparate ontologies. Moreover, such matchings provide philosophical insight into ontologies. As such, the core research question here is how we can match a trope ontology to BFO (which is based on universals) and what insights such a matching provides for foundational ontology. This article provides a logical matching, starting with BFO’s top entities (continuants and occurrences) and identifies key ontological issues that arise, such as whether universals and mereological sums are equivalent. This article concludes with general observations about the matching, including that matching to universals is generally straightforward, but not so much the matching between relations. In particular, the treatment of occurrences as causal chains is different in the trope ontology, compared to BFO’s use of time arguments.
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Lord, Phillip, and Robert Stevens. "ISMB 2003 Bio-ontologies SIG and Sixth Annual Bio-ontologies Meeting Report." Comparative and Functional Genomics 4, no. 6 (2003): 663–66. http://dx.doi.org/10.1002/cfg.339.

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The Annual Bio-Ontologies meeting (http://www.cs.man.ac.uk/˜stevens/meeting03/) has now been running for 6 consecutive years, as a special interest group (SIG) of the much larger ISMB conference. It met in Brisbane, Australia, this summer, the first time it was held outside North America or Europe. The bio-ontologies meeting is 1 day long and normally has around 100 attendees. This year there were many fewer, no doubt a result of the distance, global politics and SARS. The meeting consisted of a series of 30 min talks with no formal peer review or publication. Talks ranged in style from fairly formal and complete pieces of work, through works in progress, to the very informal and discursive. Each year's meeting has a theme and this year it was ‘ontologies, and text processing’. There is a tendency for those submitting talks to ignore the theme completely, but this year's theme obviously struck a chord, as half the programme was about ontologies and text analysis (http://www.cs.man.ac.uk/˜stevensr/meeting03/programme.html). Despite the smaller size of the meeting, the programme was particularly strong this year, meaning that the tension between allowing time for the many excellent talks, discussion and questions from the floor was particular keenly felt. A happy problem to have!
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Ozaki, Ana. "Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?" KI - Künstliche Intelligenz 34, no. 3 (April 22, 2020): 317–27. http://dx.doi.org/10.1007/s13218-020-00656-9.

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Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.
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Daradkeh, Yousef Ibrahim, and Iryna Tvoroshenko. "Application of an Improved Formal Model of the Hybrid Development of Ontologies in Complex Information Systems." Applied Sciences 10, no. 19 (September 27, 2020): 6777. http://dx.doi.org/10.3390/app10196777.

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Ontologies in artificial intelligence systems are an effective way to represent and integrate knowledge and data. The property of such structures is that any subject area is accurately described in formal language. There is a problem in the research and determination of the adequacy of ontologies under development. The perspective directions are model construction for the development of fuzzy ontologies and also the creation of methods for evaluating adequacy. The achieved results allow one to implement the processes of supporting the development and integration of ontologies of complex systems on the basis of intelligent approaches. The method is proposed to solve the problem of alternative representation and the integration of knowledge and data in artificial intelligence systems. The methodology of improving the model of the hybrid development of fuzzy ontologies is described here; it provides the preliminary modification of models of extensive and intensive progress of ontologies in space and time. The identified features of fuzzy ontology processing allow us to create a procedure for finding and eliminating inadequacies. The software implementation of the application for the integration and presentation of heterogeneous data is carried out. The consumption of Random Access Memory (RAM) for the proposed models is analyzed. The further perspectives of the proposed research are determined in accordance with the principles of classification.
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Szostak, Rick. "Advances in Classification Research Online 2013 Classification, Ontology, and the Semantic Web." Advances in Classification Research Online 24, no. 1 (January 9, 2014): 30. http://dx.doi.org/10.7152/acro.v24i1.14674.

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The Semantic Web is developing slowly, but arguably surely. Two inter-related sources of delay are network effects and ontologies. The Semantic Web has come over time to rely onformal ontologies but there are many of these and they are each hard to master. The ability to link databases is compromised by the use of incompatible ontologies. But the RDF triplet format at the centre of the Semantic Web insists only on triplets of the form (object) (predicate orproperty) (subject). This paper explores the potential for a classification system that contains these three types of hierarchies (things, predicates, properties), plus a minimal set of rules on how they can be combined, to serve the needsof the Semantic Web. To this end, it surveys theroles (both the intended roles and side-effects) that formal ontologies play within the Semantic Web. The paper also briefly reviews the challenges faced in applying existing classification systems or thesauri to the Semantic Web.<br />
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Uschold, Mike, and Michael Gruninger. "Ontologies: principles, methods and applications." Knowledge Engineering Review 11, no. 2 (June 1996): 93–136. http://dx.doi.org/10.1017/s0269888900007797.

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AbstractThis paper is intended to serve as a comprehensive introduction to the emerging field concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools and techniques are a major barrier to effective communication among people, organisations and/or software understanding (i.e. an “ontology”) in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purpose they serve. We outline a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing definitions. We then consider the benefits and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the specification, implementation and evalution of ontologies. Finally, we review the state of the art and practice in this emerging field, considering various case studies, software tools for ontology development, key research issues and future prospects.
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Ofiсerov, V. P., and S. V. Smirnov. "FUZZY FORMAL CONCEPT ANALYSIS IN THE CONSTRUCTION OF ONTOLOGIES." Ontology of Designing 26, no. 7 (December 29, 2017): 487–95. http://dx.doi.org/10.18287/2223-9537-2017-7-4-487-495.

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Schwitter, Rolf. "CREATING AND QUERYING FORMAL ONTOLOGIES VIA CONTROLLED NATURAL LANGUAGE." Applied Artificial Intelligence 24, no. 1-2 (February 2, 2010): 149–74. http://dx.doi.org/10.1080/08839510903448700.

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Schulz, Stefan, and Udo Hahn. "Part-whole representation and reasoning in formal biomedical ontologies." Artificial Intelligence in Medicine 34, no. 3 (July 2005): 179–200. http://dx.doi.org/10.1016/j.artmed.2004.11.005.

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Priya, M., and Aswani Kumar Ch. "A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure." Library Hi Tech 38, no. 2 (July 2, 2019): 399–419. http://dx.doi.org/10.1108/lht-02-2019-0035.

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Purpose The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure. Design/methodology/approach The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part. Findings The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain. Practical implications In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers. Originality/value The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.
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Alruqimi, Mohammed, and Noura Aknin. "Enabling social WEB for IoT inducing ontologies from social tagging." International Journal of Informatics and Communication Technology (IJ-ICT) 8, no. 1 (April 1, 2019): 19. http://dx.doi.org/10.11591/ijict.v8i1.pp19-24.

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<span>Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Semantic domain ontologies are increasingly seen as the key for enabling interoperability across heterogeneous systems and sensor-based applications. The ontologies deployed in these systems and applications are developed by restricted groups of domain experts and not by semantic web experts. Lately, folksonomies are increasingly exploited in developing ontologies. The “collective intelligence”, which emerge from collaborative tagging can be seen as an alternative for the current effort at semantic web ontologies. However, the uncontrolled nature of social tagging systems leads to many kinds of noisy annotations, such as misspellings, imprecision and ambiguity. Thus, the construction of formal ontologies from social tagging data remains a real challenge. Most of researches have focused on how to discover relatedness between tags rather than producing ontologies, much less domain ontologies. This paper proposed an algorithm that utilises tags in social tagging systems to automatically generate up-to-date specific-domain ontologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed algorithm introduced a simple and effective method for disambiguating tags.</span>
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MARTÍNEZ-CARRERAS, M. ANTONIA, ANDRÉS MUÑOZ, JUAN BOTÍA, and ANTONIO F. GÓMEZ-SKARMETA. "CREATING CONTEXT-AWARE COLLABORATIVE WORKING ENVIRONMENTS." International Journal on Artificial Intelligence Tools 20, no. 01 (February 2011): 195–207. http://dx.doi.org/10.1142/s0218213011000085.

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Context-aware systems are intended for providing services adapted to the needs of people, by taking into account their state and the information related to their environment. One alternative to represent this context information resides in the use of Semantic Web ontologies. They provide a formal vocabulary which allows to easily express and share knowledge. Additionally, several types of automatic knowledge manipulation and reasoning processes become available thanks to the formal features of such ontologies. The inclusion of context information through ontologies in Collaborative Working Environments (CWEs) may bring important benefits to team work inside an organization, such as an automatic selection between different collaborative services according to the team members' preferences and their current state. This paper describes the design and implementation of a context-reasoning system which has been integrated into a CWE architecture to take advantage of context-awareness.
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Boeker, M., H. Stenzhorn, J. Niggemann, and S. Schulz. "Granularity Issues in the Alignment of Upper Ontologies." Methods of Information in Medicine 48, no. 02 (2009): 184–89. http://dx.doi.org/10.3414/me9221.

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Summary Objectives: The application of upper ontologies has been repeatedly advocated for to support the interoperability between different domain ontologies for facilitating the shared use of data within and across disciplines. BioTop is an upper domain ontology that aims at aligning more specialized biomolecular and biomedical ontologies. The integration of BioTop and the upper ontology Basic Formal Ontology (BFO) is the objective of this study. Methods: BFO was manually integrated into BioTop, observing both its free text and formal definitions. BioTop classes were attached to BFO classes as children and BFO classes were reused in the formal definitions of BioTop classes. A description logics reasoner was used to check the logical consistency of this integration. The domain adequacy was checked manually by domain experts. Results: Logical inconsistencies were found by the reasoner when applying the BFO classes for fiat and aggregated objects in some of the BioTop class definitions. We discovered that the definition of those particular classes in BFO was dependent on the notion of physical connectedness. Hence we suggest ignoring a BFO subbranch in order not to hinder cross-granularity integration. Conclusion: Without introducing a more sophisticated theory of granularity, the described problems cannot be properly dealt with. Whereas we argue that an upper ontology should be granularity-independent, we illustrate how granularity-dependent domain ontologies can still be embedded into the framework of BioTop in combination with BFO.
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Guizzardi, Giancarlo. "Ontology, Ontologies and the “I” of FAIR." Data Intelligence 2, no. 1-2 (January 2020): 181–91. http://dx.doi.org/10.1162/dint_a_00040.

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According to the FAIR guiding principles, one of the central attributes for maximizing the added value of information artifacts is interoperability. In this paper, I discuss the importance, and propose a characterization of the notion of Semantic Interoperability. Moreover, I show that a direct consequence of this view is that Semantic Interoperability cannot be achieved without the support of, on one hand, (i) ontologies, as meaning contracts capturing the conceptualizations represented in information artifacts and, on the other hand, of (ii) Ontology, as a discipline proposing formal meth- ods and theories for clarifying these conceptualizations and articulating their representations. In particular, I discuss the fundamental role of formal ontological theories (in the latter sense) to properly ground the construction of representation languages, as well as methodological and computational tools for supporting the engineering of ontologies (in the former sense) in the context of FAIR.
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H. Alkhammash, Eman. "Graphical Transformation of OWL Ontologies to Event-B Formal Models." Computers, Materials & Continua 70, no. 2 (2022): 3733–50. http://dx.doi.org/10.32604/cmc.2022.015987.

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Sicilia, Miguel-Ángel, and Elena García Barriocanal. "On the Convergence of Formal Ontologies and Standardized E-Learning." International Journal of Distance Education Technologies 3, no. 2 (April 2005): 13–29. http://dx.doi.org/10.4018/jdet.2005040102.

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Hagedorn, Thomas J., Barry Smith, Sundar Krishnamurty, and Ian Grosse. "Interoperability of disparate engineering domain ontologies using basic formal ontology." Journal of Engineering Design 30, no. 10-12 (June 14, 2019): 625–54. http://dx.doi.org/10.1080/09544828.2019.1630805.

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Dotsika, Fefie. "Uniting formal and informal descriptive power: Reconciling ontologies with folksonomies." International Journal of Information Management 29, no. 5 (October 2009): 407–15. http://dx.doi.org/10.1016/j.ijinfomgt.2009.02.002.

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Batres, Rafael, and Suriati Akmal. "A Formal Concept Analysis-Based Method for Developing Process Ontologies." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 46, no. 6 (2013): 396–406. http://dx.doi.org/10.1252/jcej.12we278.

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Hacherouf, Mokhtaria, Safia Nait-Bahloul, and Christophe Cruz. "Transforming XML schemas into OWL ontologies using formal concept analysis." Software & Systems Modeling 18, no. 3 (January 25, 2018): 2093–110. http://dx.doi.org/10.1007/s10270-017-0651-4.

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Kumar, Ch Aswani, and M. Priya. "A novel approach for merging ontologies using formal concept analysis." International Journal of Cloud Computing 9, no. 2/3 (2020): 189. http://dx.doi.org/10.1504/ijcc.2020.10031535.

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Priya, M., and Ch Aswani Kumar. "A novel approach for merging ontologies using formal concept analysis." International Journal of Cloud Computing 9, no. 2/3 (2020): 189. http://dx.doi.org/10.1504/ijcc.2020.109370.

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Gkoutos, Georgios V., Robert Hoehndorf, Loukia Tsaprouni, and Paul N. Schofield. "Best behaviour? Ontologies and the formal description of animal behaviour." Mammalian Genome 26, no. 9-10 (July 28, 2015): 540–47. http://dx.doi.org/10.1007/s00335-015-9590-y.

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Rodríguez-González, Alejandro, Ángel García-Crespo, Ricardo Colomo-Palacios, Juan Miguel Gómez-Berbís, and Enrique Jiménez-Domingo. "Using Ontologies in Drug Prescription." International Journal of Knowledge-Based Organizations 1, no. 4 (October 2011): 1–15. http://dx.doi.org/10.4018/ijkbo.2011100101.

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Medical prescription has been touted as following an accurate approach to addressing particular health problems. However, the importance of the process might demand considering a formal knowledge-driven procedure to ensure its correctness which can be achieved through Medical Decision Support Systems (MDSS). Semantic Technologies have emerged as a potential silver bullet to become the backbone of those particular Information Systems since it provides seamless integration and an underlying logical formalism. This paper sheds light into using ontologies for drug prescription through the SemMed model, architecture and proof-of-concept implementation, being able to face challenges in these areas and solve day-to-day problems of health professionals in terms of drug prescription
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Zhang, Fu, Jingwei Cheng, and Zongmin Ma. "A survey on fuzzy ontologies for the Semantic Web." Knowledge Engineering Review 31, no. 3 (April 22, 2016): 278–321. http://dx.doi.org/10.1017/s0269888916000059.

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AbstractOntology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge strongly characterizing some real-world applications. Thus, a requirement of extending ontologies naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web. In order to provide the necessary means to handle such vague and imprecise information there are today many proposals for fuzzy extensions to ontologies, and until now the literature on fuzzy ontologies has been flourishing. To investigate fuzzy ontologies and more importantly serve as helping readers grasp the main ideas and results of fuzzy ontologies, and to highlight an ongoing research on fuzzy approaches for knowledge semantic representation based on ontologies, as well as their applications on various domains,in this paper,we provide a comprehensive overview of fuzzy ontologies. In detail, wefirstintroduce fuzzy ontologies from the most common aspects such asrepresentation(including categories, formal definitions, representation languages, and tools of fuzzy ontologies),reasoning(including reasoning techniques and reasoners), andapplications(the most relevant applications about fuzzy ontologies). Then,the other important issueson fuzzy ontologies, such asconstruction,mapping,integration,query,storage,evaluation,extension, anddirections for future research, are also discussed in detail. Also, we make somecomparisons and analysesin our whole review.
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Sinha, Prasant Kumar, and Biswanath Dutta. "A Systematic Analysis of Flood Ontologies: A Parametric Approach." KNOWLEDGE ORGANIZATION 47, no. 2 (2020): 138–59. http://dx.doi.org/10.5771/0943-7444-2020-2-138.

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The article identifies the core literature available on flood ontologies and presents a review on these ontologies from various perspectives like its purpose, type, design methodologies, ontologies (re)used, and also their focus on specific flood disaster phases. The study was conducted in two stages: i) literature identification, where the systematic literature review methodology was employed; and, ii) ontological review, where the parametric approach was applied. The study resulted in a set of fourteen papers discussing the flood ontology (FO). The ontological review revealed that most of the flood ontologies were task ontologies, formal, modular, and used web ontology language (OWL) for their representation. The most (re)used ontologies were SWEET, SSN, Time, and Space. METHONTOLOGY was the preferred design methodology, and for evaluation, application-based or data-based approaches were preferred. The majority of the ontologies were built around the response phase of the disaster. The unavailability of the full ontologies somewhat restricted the current study as the structural ontology metrics are missing. But the scientific community, the developers, of flood disaster management systems can refer to this work for their research to see what is available in the literature on flood ontology and the other major domains essential in building the FO.
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Saripalle, Rishi Kanth, Steven A. Demurjian, Alberto De la Rosa Algarín, and Michael Blechner. "A Software Modeling Approach to Ontology Design via Extensions to ODM and OWL." International Journal on Semantic Web and Information Systems 9, no. 2 (April 2013): 62–97. http://dx.doi.org/10.4018/jswis.2013040103.

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Ontologies are built to establish standard terminologies representing a semantic agreement between humans and knowledge systems via representational frameworks (e.g., KIF, DAML+OIL, OWL, etc.) that have been proposed in the research community, with limited adoption in industry. One possible reason is a lack of a formal model and associated process to more precisely and accurately design and develop ontologies. The authors’ prior work explored UML, entity-relationship diagrams, and XML as compared to RDF and OWL, identifying modeling capabilities lacking in ontologies. In all three approaches, design precedes instantiation which contrasts with ontology developers who build ontologies at the application level targeted to a specific domain. The paper proposes design-level modeling enhancements to ontologies by extending the OMG Ontology Definition Model (ODM) and OWL grammar with capabilities from the three aforementioned approaches, promoting a software engineering-based process. As a result, this work provides a more software engineering-oriented process to ontology design and development.
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GROSSE, IAN R., JOHN M. MILTON–BENOIT, and JACK C. WILEDEN. "Ontologies for supporting engineering analysis models." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, no. 1 (February 2005): 1–18. http://dx.doi.org/10.1017/s0890060405050018.

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In this paper we lay the foundations for exchanging, adapting, and interoperating engineering analysis models (EAMs). Our primary foundation is based upon the concept that engineering analysis models are knowledge-based abstractions of physical systems, and therefore knowledge sharing is the key to exchanging, adapting, and interoperating EAMs within or across organizations. To enable robust knowledge sharing, we propose a formal set of ontologies for classifying analysis modeling knowledge. To this end, the fundamental concepts that form the basis of all engineering analysis models are identified, described, and typed for implementation into a computational environment. This generic engineering analysis modeling ontology is extended to include distinct analysis subclasses. We discuss extension of the generic engineering analysis modeling class for two common analysis subclasses: continuum-based finite element models and lumped parameter or discrete analysis models. To illustrate how formal ontologies of engineering analysis modeling knowledge might facilitate knowledge exchange and improve reuse, adaptability, and interoperability of analysis models, we have developed a prototype engineering analysis modeling knowledge base, called ON-TEAM, based on our proposed ontologies. An industrial application is used to instantiate the ON-TEAM knowledge base and illustrate how such a system might improve the ability of organizations to efficiently exchange, adapt, and interoperate analysis models within a computer-based engineering environment. We have chosen Java as our implementation language for ON-TEAM so that we can fully exploit object-oriented technology, such as object inspection and the use of metaclasses and metaobjects, to operate on the knowledge base to perform a variety of tasks, such as knowledge inspection, editing, maintenance, model diagnosis, customized report generation of analysis models, model selection, automated customization of the knowledge interface based on the user expertise level, and interoperability assessment of distinct analysis models.
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Garanina, Natalia O., Igor S. Anureev, Olesya I. Borovikova, and Vladimir E. Zyubin. "Methods for Domain Specification of Verification-Oriented Process Ontology." Modeling and Analysis of Information Systems 26, no. 4 (December 27, 2019): 534–49. http://dx.doi.org/10.18255/1818-1015-2019-4-534-549.

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User-friendly formal specifications and verification of parallel and distributed systems from various subject fields, such as automatic control, telecommunications, business processes, are active research topics due to its practical significance. In this paper, we present methods for the development of verification-oriented domain-specific process ontologies which are used to describe parallel and distributed systems of subject fields. One of the advantages of such ontologies is their formal semantics which make possible formal verification of the described systems. Our method is based on the abstract verification-oriented process ontology. We use two methods of specialization of the abstract process ontology. The declarative method uses the specialization of the classes of the original ontology, introduction of new declarative classes, as well as use of new axioms system, which restrict the classes and relations of the abstract ontology. The constructive method uses semantic markup and pattern matching techniques to link sublect fields with classes of the abstract process ontology. We provide detailed ontological specifications for these techniques. Our methods preserve the formal semantics of the original process ontology and, therefore, the possibility of applying formal verification methods to the specialized process ontologies. We show that the constructive method is a refinement of the declarative method. The construction of ontology of the typical elements of automatic control systems illustrates our methods: we develop a declarative description of the classes and restrictions for the specialized ontology in the Prot´eg´e system in the OWL language using the deriving rules written in the SWRL language and we construct the system of semantic markup templates which implements typical elements of automatic control systems.
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49

Dahlem, Nikolai. "OntoClippy." International Journal of Intelligent Information Technologies 7, no. 1 (January 2011): 15–32. http://dx.doi.org/10.4018/jiit.2011010102.

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In this article, the author describes OntoClippy, a tool-supported methodology for the user-friendly design and creation of ontologies. Existing ontology design methodologies and tools are targeted at experts and not suitable for users without a background in formal logic. Therefore, this research develops a methodology and a supporting tool to facilitate the acceptance of ontologies by a wider audience. In this article, the author positions the approach with respect to the current state of the art, formulates the basic principles of the methodology, presents its formal grounding, and describes its phases in detail. To demonstrate the viability of our approach, the author performs a comparative evaluation. The experiment is described, as well as real-world applications of the approach.
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Goldstein, Anat, Lior Fink, and Gilad Ravid. "A Framework for Evaluating Agricultural Ontologies." Sustainability 13, no. 11 (June 4, 2021): 6387. http://dx.doi.org/10.3390/su13116387.

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An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision-support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic-aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such evaluation methods are applied during the ontology development process. Our work contributes to the literature on agricultural ontologies by presenting a framework that guides the selection of suitable evaluation methods, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology’s purpose.
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