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knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第8期15卷 636-650页

作者： Zhi-qiang FENG Cun-gen LIU Hu HUANG Maritime College Qinzhou University State Key Laboratory of Ocean Engineering Shanghai Jiao Tong University

knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.

关键词： knowledge modeling Interval-valued fuzzy rough set Similarity-based inference Welding distortion prediction

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An ontology model to represent aquaponics 4.0 system’s knowledge

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Information Processing in Agriculture 2022年第4期9卷 514-532页

作者： Rabiya Abbasi Pablo Martinez Rafiq Ahmad Aquaponics 4.0 Learning Factory(AllFactory) Department of Mechanical EngineeringUniversity of Alberta9211116 St.EdmontonAB T6G 2G8Canada

Aquaponics,one of the vertical farming methods,is a combination of aquaculture and *** enhance the production capabilities of the aquaponics system and maxi-mize crop yield on a commercial level,integration of Industry 4.0 technologies is *** 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics,internet of things,robotics,cloud computing,and artificial *** realization of aquaponics 4.0,however,requires an efficient flow and inte-gration of data due to the presence of complex biological processes.A key challenge in this essence is to deal with the semantic heterogeneity of multiple data *** ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing,extracting,and sharing the domains’*** the field of agriculture,several ontologies are developed for the soil-based farming methods,but so far,no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology ***,this study proposes a unified ontology model,AquaONT,to rep-resent and store the essential knowledge of an aquaponics 4.0 *** ontology pro-vides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation *** is built from indoor vertical farming termi-nologies and is validated and implemented by considering experimental test cases related to environmental parameters,design configuration,and product *** proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production,product quality,and facility layout of the aquaponics *** future work,a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.

关键词： Aquaponics 4.0 Industry 4.0 Ontology modeling knowledge modeling Decision support system

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Dictionary,Thesaurus or Ontology? Disentangling Our Choices in the Semantic Web Jungle

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Journal of Integrative Agriculture 2012年第5期11卷 710-719页

作者： Armando Stellato Department of Enterprise Engineering University of Tor VergataRome 00133Italy

The Semantic Web seems finally close to maintaining its promise about a real world-wide graph of interconnected resources. The SPARQL query language and protocols and the Linked Open Data initiative have laid the way for endless data endpoints sparse around the globe. However, for the Semantic Web to really happen, it does not suffice to get billions of triples out there： these must be shareable, interlinked and conform to widely accepted vocabularies. While more and more data are converted from already available large knowledge repositories of companies and organizations, the question whether these should be carefully converted to semantically consistent ontology vocabularies or find other shallow representations for their content naturally arises. The danger is to come up with massive amounts of useless data, a boomerang which could result to be contradictory for the success of the web of data. In this paper, I provide some insights on common problems which may arise when porting huge amount of existing data or conceptual schemes （very common in the agriculture domain） to resource description framwork （RDF）, and will address different modeling choices, by discussing in particular the relationship between the two main modeling vocabularies offered by W3C： OWL and SKOS.

关键词： ontologies thesauri knowledge modeling linked open data

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The Semantic Data Dictionary-An Approach for Describing and Annotating Data

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Data Intelligence 2020年第4期2卷 443-486页

作者： Sabbir M.Rashid James P.McCusker Paulo Pinheiro Marcello P.Bax Henrique O.Santos Jeanette A.Stingone Amar K.Das Deborah L.McGuinness Tetherless World Constellation Rensselaer Polytechnic InstituteTroy NY 12180USA School of Information Science Federal University of Minas GeraisBelo Horizonte 31270-901MGBrazil Mailman School of Public Health Columbia UniversityNew York NY 10032USA IBM Research Cambridge MA 02142USA

It is common practice for data providers to include text descriptions for each column when publishing data sets in the form of data *** these documents are useful in helping an end-user properly interpret the meaning of a column in a data set,existing data dictionaries typically are not machine-readable and do not follow a common specification *** introduce the Semantic Data Dictionary,a specification that formalizes the assignment of a semantic representation of data,enabling standardization and harmonization across diverse data *** this paper,we present our Semantic Data Dictionary work in the context of our work with biomedical data;however,the approach can and has been used in a wide range of *** rendition of data in this form helps promote improved discovery,interoperability,reuse,traceability,and *** present the associated research and describe how the Semantic Data Dictionary can help address existing limitations in the related *** discuss our approach,present an example by annotating portions of the publicly available National Health and Nutrition Examination Survey data set,present modeling challenges,and describe the use of this approach in sponsored research,including our work on a large National Institutes of Health(NIH)-funded exposure and health data portal and in the RPI-IBM collaborative Health Empowerment by Analytics,Learning,and Semantics project.

关键词： Semantic Data Dictionary Dictionary mapping Codebook knowledge modeling Data integration Data dictionary Mapping language Metadata standard Semantic Web Semantic ETL FAIR Data

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