{"title":"Ontology for Semantic Data Integration in the Domain of IT Benchmarking.","authors":"Matthias Pfaff, Stefan Neubig, Helmut Krcmar","doi":"10.1007/s13740-017-0084-9","DOIUrl":null,"url":null,"abstract":"<p><p>A domain-specific ontology for IT benchmarking has been developed to bridge the gap between a systematic characterization of IT services and their data-based valuation. Since information is generally collected during a benchmark exercise using questionnaires on a broad range of topics, such as employee costs, software licensing costs, and quantities of hardware, it is commonly stored as natural language text; thus, this information is stored in an intrinsically unstructured form. Although these data form the basis for identifying potentials for IT cost reductions, neither a uniform description of any measured parameters nor the relationship between such parameters exists. Hence, this work proposes an ontology for the domain of IT benchmarking, available at https://w3id.org/bmontology. The design of this ontology is based on requirements mainly elicited from a domain analysis, which considers analyzing documents and interviews with representatives from Small- and Medium-Sized Enterprises and Information and Communications Technology companies over the last eight years. The development of the ontology and its main concepts is described in detail (i.e., the conceptualization of benchmarking events, questionnaires, IT services, indicators and their values) together with its alignment with the DOLCE-UltraLite foundational ontology.</p>","PeriodicalId":54029,"journal":{"name":"Journal on Data Semantics","volume":"7 1","pages":"29-46"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s13740-017-0084-9","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal on Data Semantics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13740-017-0084-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/11/13 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 11
Abstract
A domain-specific ontology for IT benchmarking has been developed to bridge the gap between a systematic characterization of IT services and their data-based valuation. Since information is generally collected during a benchmark exercise using questionnaires on a broad range of topics, such as employee costs, software licensing costs, and quantities of hardware, it is commonly stored as natural language text; thus, this information is stored in an intrinsically unstructured form. Although these data form the basis for identifying potentials for IT cost reductions, neither a uniform description of any measured parameters nor the relationship between such parameters exists. Hence, this work proposes an ontology for the domain of IT benchmarking, available at https://w3id.org/bmontology. The design of this ontology is based on requirements mainly elicited from a domain analysis, which considers analyzing documents and interviews with representatives from Small- and Medium-Sized Enterprises and Information and Communications Technology companies over the last eight years. The development of the ontology and its main concepts is described in detail (i.e., the conceptualization of benchmarking events, questionnaires, IT services, indicators and their values) together with its alignment with the DOLCE-UltraLite foundational ontology.
期刊介绍:
The Journal on Data Semantics (JoDS) provides an international high-quality publication venue for researchers whose themes cover issues related to information semantics. Its target domain ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge, thus covering work done on conceptual modeling, databases, Semantic Web, information systems, workflow and process modeling, ontologies, business intelligence, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, and artificial intelligence. Topics of relevance to this journal include (but are not limited to): Conceptualization, knowledge representation and reasoning, Conceptual data, process, workflow, and event modeling, Provenance, evolution and change management, Context and context-dependent representations and processing, Multi-model and multi-paradigm approaches, Mappings, transformations, reverse engineering and semantic elicitation, Semantic interoperability, semantic mediators and metadata management, Ontology models and languages, ontology-driven applications, Ontology, schema, data and process integration, reconciliation and alignment, Web semantics and semi-structured data, Integrity description and handling, Semantics in data mining and knowledge extraction, Semantics in business intelligence, analytics and data visualization, Spatial, temporal, multimedia and multimodal semantics, Semantic mobility data and services for mobile users, Supporting tools and applications of semantic-driven approaches.