A fuzzy ontology based approach for mobilising industrial plant knowledge

Pakonen, Antti; Tommila, Teemu; Hirvonen, J.

doi:10.1109/etfa.2010.5641200

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…The authors developed a set of tools for engineers with little knowledge of semantic technology in the creation of ontology-based models and in populating them with data. In Pakonen et al [17], fuzzy ontologies were used in the industrial domain to improve information retrieval from the plant knowledge base consisting of reports obtained from different plant systems. With the emergence of the Industry 4.0 concept, several other ontological approaches have been created.…”

Section: Standards For Modeling Manufacturing Processesmentioning

confidence: 99%

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

2019

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The process industries rely on various software systems and use a wide range of technologies. Predictive modeling techniques are often applied to data obtained from these systems to build the predictive functions used to optimize the production processes. Therefore, there is a need to provide a proper representation of knowledge and data and to improve the communication between the data scientists who develop the predictive functions and domain experts who possess the expert knowledge of the domain. This can be achieved by developing a semantic model that focuses on cross-sectorial aspects rather than concepts for specific industries, and that specifies the meta-classes for the formal description of these specific concepts. This model should cover the most important areas including modeling the production processes, data analysis methods, and evaluation using the performance indicators. In this paper, our primary objective was to introduce the specifications of the Cross-sectorial domain model and to present a set of tools that support data analysts and domain experts in the creation of process models and predictive functions. The model and the tools were used to design a knowledge base that could support the development of predictive functions in the green anode production in the aluminum production domain.

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Section: Standards For Modeling Manufacturing Processesmentioning

confidence: 99%

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

2019

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“…Tamani et al (2013) developed a new approach for flexible querying of complex information systems that combines a reasoning mechanism (an ontology based on the fuzzy bipolar DLR-Lite) with a bipolar relational language of a high expressivity (Bipolar SQLf language). In detail, Pakonen et al (2010) and Tommila et al (2010) used the fuzzy ontologies in industrial knowledge retrieval, where how to build fuzzy ontologies for the process industry domain to enhance knowledge retrieval was introduced in detail, and a concrete benefit of fuzzy ontologies is the extension of information queries-allowing the search to also cover related results, and make the decisions about relatedness based on modeled domain knowledge. Other efforts on adaptation of fuzzy ontology for information retrieval can be found in Olivas et al (2003), Singh et al (2004), Garceś et al (2006), Sezer et al (2006), Gallova (2007) and Attia et al (2014).…”

Section: Information Retrievalmentioning

confidence: 99%

“…In the following, we first summarize the relevant applications about fuzzy ontologies, and then discuss other important issues of fuzzy ontologies. (Bobillo & Straccia, 2008) f-SHIF(D) √ √ Supporting that the degree of a fuzzy assertion is not only a constant, but also a variable DeLorean f-SROIQ(D) √ √ Reducing to crisp DLs to solve, and supporting fuzzy concrete domains D GURDL (Haarslev et al, 2007(Haarslev et al, , 2008 f-ALC √ Proposing some interesting techniques of optimization GERDS (Habiballa, 2007) f-ALC √ Adding role negation, top role, and bottom role to f-ALC FRESG (Wang et al, 2009) f-ALC(G) √ √ Supporting fuzzy data information with customized fuzzy data types G YADLR (Stasinos & Georgios, 2007) SLG algorithm √ Allowing to deal with unknown degrees of truth in the fuzzy assertions of the knowledge base SoftFacts (Straccia, 2009a) SoftFacts An ontology-mediated top-k information retrieval system over relational databases LiFR (Tsatsou et al, 2014) f-DLP A lightweight fuzzy DL reasoner that supports a subset of fuzzy DL Programs (f-DLP) (Pakonen et al, 2010;Tommila et al, 2010) Medical document retrieval (Parry, 2006b) Multimedia information retrieval (Wallace & Avrithis, 2004;Dasiopoulou et al, 2008;Elleuch et al, 2011) Semantic information retrieval (Olivas et al, 2003;Singh et al, 2004;Kang et al, 2005b;Baziz et al, 2006;Garceś et al, 2006;Parry, 2006a;Sezer et al, 2006;Gallova, 2007;Calegari & Sanchez, 2008;Lau et al, 2009;Pereira et al, 2009;Tamani et al, 2013;Attia et al, 2014;Rani et al, 2014) Personalized information retrieval (Vallet et al, 2006;Zhou et al, 2006;Mylonas et al, 2008)...…”

Section: Applications and Other Issues Of Fuzzy Ontologiesmentioning

confidence: 99%

A survey on fuzzy ontologies for the Semantic Web

Zhang¹,

Cheng²,

Ma³

2016

The Knowledge Engineering Review

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Ontology, 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, we first introduce fuzzy ontologies from the most common aspects such as representation (including categories, formal definitions, representation languages, and tools of fuzzy ontologies), reasoning (including reasoning techniques and reasoners), and applications (the most relevant applications about fuzzy ontologies). Then, the other important issues on fuzzy ontologies, such as construction, mapping, integration, query, storage, evaluation, extension, and directions for future research, are also discussed in detail. Also, we make some comparisons and analyses in our whole review.ontology definition languages, such as RDF(S), SHOE, OIL, DAML, DAML + OIL, and web ontology language (OWL), have been developed over the past years (Horrocks et al., 2003). The OWL (Smith et al., 2004) and its successor OWL 2 (Cuenca Grau et al., 2008) is the W3C recommended ontology representation language. The theoretical underpinnings of OWL and ontologies are strongly based on Description Logics (DLs), a subset of first-order logic especially suitable for representing structured knowledge (Horrocks & Sattler, 2001;Baader et al., 2003;Horrocks et al., 2003).Although ontology is a quite expressive formalism, it features limitations, mainly with what can be said about fuzzy information. Nowadays, in ontology-based and many applications information is often vague and imprecise. This is a well-known problem especially for semantics-based applications of the Semantic Web, such as knowledge management, e-commerce, and web portals. For example, a task like a 'doctor appointment' could look like: 'Make me an appointment with a doctor close to my home not too early and of good references' (Stoilos et al., 2010). The conceptual formalism supported by typical ontology may not be sufficient to represent such information and knowledge. Therefore, many proposals have attempted to apply different formalisms...

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“…FO is a generalization of crisp ontology (CO) where fuzzy relations exist between crisp concepts. FO have been successfully implemented in several application areas such as news summarization, diet recommendation, flight booking, information retrieval, reputation management, collision avoidance, and knowledge mobilization …”

Section: Introductionmentioning

confidence: 99%

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Safaeipour

Zarandi

Bastani

2017

Int. J. Intell. Syst.

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Assessing semantic similarity is a fundamental requirement for many AI applications. Crisp ontology (CO) is one of the knowledge representation tools that can be used for this purpose. Thanks to the development of semantic web, CO‐based similarity assessment has become a popular approach in recent years. However, in the presence of vague information, CO cannot consider uncertainty of relations between concepts. On the other hand, fuzzy ontology (FO) can effectively process uncertainty of concepts and their relations. This paper aims at proposing an approach for assessing concept similarity based on FO. The proposed approach incorporates fuzzy relation composition in combination with an edge counting approach to assess the similarity. Accordingly, proposed measure relies on taxonomical features of an ontology in combination with statistical features of concepts. Furthermore, an evaluation approach for the FO‐based similarity measure named as FOSE is proposed. Considering social network data, proposed similarity measure is evaluated using FOSE. The evaluation results prove the dominance of proposed approach over its respective CO‐based measure.

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A fuzzy ontology based approach for mobilising industrial plant knowledge

Cited by 6 publications

References 16 publications

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

A survey on fuzzy ontologies for the Semantic Web

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

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