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Published in Paul Compton, Byeong Ho Kang, Ripple-Down Rules, 2021
The influence of Plato seems clear in modern work on ontologies. There is no doubt that it is useful to have a set of terms to be used consistently – and even better to have a formal structure for the concepts and relationships the terms express. But modern work on ontologies also includes the development of so-called upper ontologies which cover ideas like object, quality, attribute, property, quantity, processes, events, and the ultimate goal is a shared universal ontology enabling reasoning across the Semantic Web. Despite the importance of developing shared standard ontologies, it is not at all clear whether they will be able to be used by humans. The Unified Medical Language System (UMLS) (not actually an ontology) has over one million concepts, five million synonyms and hundreds of terminologies. Rosenbloom et al. have pointed out that the most important issue with the UMLS is not its completeness, but how end-users can be enabled to use it (Rosenbloom et al. 2006). A recent study of some biomedical ontologies in use found significant errors (Boeker et al. 2011). Along with errors in ontologies, a further issue is their multiplicity so that ontology merging is a major research issue, but the challenge is not simply that different terminologies have emerged but that people disagree even on ontology alignment (Tordai et al. 2011). On the other hand, Tordai et al. comment: “Humans rarely have problems with disambiguating the meaning of words in a discourse context”. In an earlier simpler example Shaw and Woodward carried out a study comparing different domain experts, as well as repeat studies with the same experts as a control (Shaw and Woodward 1988). They found experts construct knowledge with different terminologies and disagree with each other’s terminologies but that this is not a problem as they are well used to both working together and disagreeing with each other!
Matching heterogeneous ontologies with adaptive evolutionary algorithm
Published in Connection Science, 2022
Xingsi Xue, Haolin Wang, Xin Zhou, Guojun Mao, Hai Zhu
In general, ontology matching is aimed at finding the entity correspondences between two heterogeneous ontologies. By means of using the similarity measures, an ontology matching system can distinguish the heterogeneous entities and generate the ontology alignment (Xue & Chen, 2021). To be specific, similarity measure can be seen as a function that calculates what extent two entities are similar and outputs a real number from 0 to 1. The frequently-used similarity measures can be categorised into three categories, i.e. the syntax-based, semantic-based and structure-based measures (Rahm & Bernstein, 2001). Since a single similarity measure is not able to ensure the result's confidence, it is usually necessary to combine several measures. How to determine the aggregating weights to obtain the high-quality alignment is the so-called ontology meta-matching problem (Martinez-Gil & Aldana-Montes, 2011), which is a challenging problem in the ontology matching domain.
Ontology-based model-driven design of distributed control applications in manufacturing systems
Published in Journal of Engineering Design, 2019
Yue Cao, Yusheng Liu, Hongwei Wang, Jianjun Zhao, Xiaoping Ye
The core of ontology matching is ontology alignment which involves identifying the differences and similarities between a set of concepts from the two ontologies (Kovalenko et al. 2016). Based on the similarities between the DCO and FBO, the alignment between them can be identified. After that, the correspondences included in the alignment are specified to be applied in the transformation. Considering the correspondences between DCO and FBO are complex, the Expressive and Declarative Ontology Alignment Language (EDOAL) is adopted to specify them. It is a component of Alignment API (David et al. 2013) which is used to represent complex correspondences allowing to precisely describe relations between entities. From the correspondences described in EDOAL, executable transformations can be generated by various types of renderers provided by the Alignment API framework. For example, Figure 17 shows the correspondence between DataPort and Var and the SPARQL CONSTRUCT query generated from it. This query can retrieve instances from the DCO and populate corresponding instances into the FBO.
Agent-based distributed manufacturing scheduling: an ontological approach
Published in Cogent Engineering, 2019
Salman Saeidlou, Mozafar Saadat, Ebrahim Amini Sharifi, Guiovanni D. Jules
Using ontologies to store data enables data re-usability and maintenance, which is a massive advantage as compared to normal databases; it also provides the opportunity to merge it with available ontologies for knowledge enhancement. This ontology alignment gives structure that is essential for compatibility with domain specific applications. This research made use of “OWLSimpleJADEAbstractOntology.owl” in order to generate FIPA compliant ontology for the multi-agent platform JADE, which is compiled using the beangenerator tool. The “swrla.owl” and “sqwrl.owl” ontologies enable realisation of the knowledge base through the use of Semantic Web Rule Language. Figure 4 shows the main structure of the aforementioned ontologies.