Reusability ontology in business processes with similarity matching

The working technology will provide information and knowledge. Information and technology can be developed in various ways, by reusing the technologies. In this study modeled the ontology of SOPs using protégé. Ontology will be matched between ontology A and B to obtain similarity and reuse ontology to create a more optimal ontology. Matching is a matching process between both ontologies to get the same value from both ontologies. Jaro-Winkler distance is used to find commonality between ontology. The result of the Jaro-Winkler distance has a value of 0 and 1, in matching will be obtained value close to 0 or 1. On matching ontology obtained two tests using 40% SPARQL query. In the test it uses Jaro-Winkler distance with a value of 0.67. This research yields matching value between ontology A and ontology B which is the same so that reuse ontology can be done for better ontolog

Synchronizing physical and digital factory: benefits and technical challenges

Abstract The Digital Twin is a representation of characteristics and behavior of a factory according to various levels of detail and the scope it addresses. Its full range of capabilities can be exploited when it is synchronized with the real world. Indeed, in this case, it can be used to mirror the real operating conditions for simulating the real-time behavior, and thus forecasting factory performances. However, we are still far from its large-scale diffusion. The purpose of this work is to analyze both the major challenges that still have to be faced and some potential solutions for each of the identified challenges

Research in progress: report on the ICAIL 2017 doctoral consortium

This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences

The evaluation of ontologies: quality, reuse and social factors

Finding a “good” or the “right” ontology is a growing challenge in the ontology domain, where one of the main aims is to share and reuse existing semantics and knowledge. Before reusing an ontology, knowledge engineers not only have to find a set of appropriate ontologies for their search query, but they should also be able to evaluate those ontologies according to different internal and external criteria. Therefore, ontology evaluation is at the heart of ontology selection and has received a considerable amount of attention in the literature.Despite the importance of ontology evaluation and selection and the widespread research on these topics, there are still many unanswered questions and challenges when it comes to evaluating and selecting ontologies for reuse. Most of the evaluation metrics and frameworks in the literature are mainly based on a limited set of internal characteristics, e.g., content and structure of ontologies and ignore how they are used and evaluated by communities. This thesis aimed to investigate the notion of quality and reusability in the ontology domain and to explore and identify the set of metrics that can affect the process of ontology evaluation and selection for reuse. [Continues.

Building high-quality merged ontologies from multiple sources with requirements customization

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a knowledge graph fully representing a domain of interest. Existing approaches scale rather poorly to the merging of multiple ontologies due to using a binary merge strategy. Thus, we aim to investigate the extent to which the n-ary strategy can solve the scalability problem. This thesis contributes to the following important aspects: 1. Our n-ary merge strategy takes as input a set of source ontologies and their mappings and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. 2. We take a step towards parameterizable merge methods. We have identified a set of Generic Merge Requirements (GMRs) that merged ontologies might be expected to meet. We have investigated and developed compatibilities of the GMRs by a graph-based method. 3. When multiple ontologies are merged, inconsistencies can occur due to different world views encoded in the source ontologies To this end, we propose a novel Subjective Logic-based method to handling the inconsistency occurring while merging ontologies. We apply this logic to rank and estimate the trustworthiness of conflicting axioms that cause inconsistencies within a merged ontology. 4. To assess the quality of the merged ontologies systematically, we provide a comprehensive set of criteria in an evaluation framework. The proposed criteria cover a variety of characteristics of each individual aspect of the merged ontology in structural, functional, and usability dimensions. 5. The final contribution of this research is the development of the CoMerger tool that implements all aforementioned aspects accessible via a unified interface

Uma Abordagem Orientada a Objetivos para Desenvolvimento de Ontologias baseado em Integração

Reúso tem sido apontado como uma abordagem promissora para a Engenharia de Ontologias. Reutilização permite acelerar o processo de desenvolvimento, além de melhorar a qualidade das ontologias resultantes, uma vez que promove a aplicação de boas práticas. No âmbito da Engenharia de Ontologias, uma das formas de reúso envolve a integração de ontologias existentes. Integração de ontologias pode ser definida como a junção (integração) de ontologias fonte em uma ontologia integrada, na qual ainda podem ser acrescidos conceitos e relações além dos encontrados nas ontologias fontes. A integração depende de encontrar ontologias que satisfaçam os requisitos da ontologia a ser desenvolvida. Porém, muitas vezes, as ontologias disponíveis não têm seu design rationale explícito, o que dificulta o entendimento das ontologias fonte e, consequentemente, a integração entre elas. Explicitar o design rationale da ontologia a ser desenvolvida a partir da integração também é importante para auxiliar na busca por ontologias fonte que atendam os requisitos da ontologia integrada. Embora haja abordagens de desenvolvimento de ontologias que reconheçam a importância da integração nesse contexto e também haja abordagens que tratem especificamente do processo de integração, há carência de abordagens que guiem o engenheiro de ontologias em um processo de desenvolvimento de ontologias baseado em integração e que se preocupem em tornar explícito o design rationale da ontologia sendo construída. Modelagem de objetivos tem sido apontada como uma forma de apoiar o levantamento de requisitos de ontologias. Nesse sentido, a capacidade de os modelos de objetivos representarem aspectos motivacionais do desenvolvimento de ontologias pode ser explorada para explicitar o design rationale por trás de uma ontologia. Assim, neste trabalho é proposta Integra, uma abordagem orientada a objetivos para desenvolvimento de ontologias baseado em integração. Para avaliar Integra, ela foi utilizada em uma prova de conceito e em um estudo de caso

Технология комплексной поддержки жизненного цикла семантически совместимых интеллектуальных компьютерных систем нового поколения

В издании представлено описание текущей версии открытой технологии онтологического проектирования, производства и эксплуатации семантически совместимых гибридных интеллектуальных компьютерных систем (Технологии OSTIS). Предложена стандартизация интеллектуальных компьютерных систем, а также стандартизация методов и средств их проектирования, что является важнейшим фактором, обеспечивающим семантическую совместимость интеллектуальных компьютерных систем и их компонентов, что существенное снижение трудоемкости разработки таких систем. Книга предназначена всем, кто интересуется проблемами искусственного интеллекта, а также специалистам в области интеллектуальных компьютерных систем и инженерии знаний. Может быть использована студентами, магистрантами и аспирантами специальности «Искусственный интеллект». Табл. 8. Ил. 223. Библиогр.: 665 назв