Enterprise information integration

Halevy, Alon; Ashish, Naveen; Bitton, Dina; Carey, Michael J.; Draper, Denise L.; Pollock, Jeff; Rosenthal, Arnon; Sikka, Vishal

doi:10.1145/1066157.1066246

Cited by 162 publications

(9 citation statements)

References 6 publications

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“…Semantic conflict is mainly caused by heterogeneous data sources in different table name, column name and data type [4]. For example, Use the 'author' to represent the author of a book in one data source, in another system, use 'writer' to represent homonymy heterogeneous caused by writer; Another example, The data type conflict in one data source 'datetime' to represent the date time with in another data source 'date' to represent date time.…”

Section: Ontology Semantic Conflictmentioning

confidence: 99%

Semantic Conflict Resolution Scheme Based on Ontology

Zhao¹

2017

dtetr

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In this paper, the ontology is made full use to describe the characteristics of the relation between concept and concept, adding semantics to XML Schema. This is the so-called semantic annotation technology. To solve the problem of semantic conflict. In the field of railway, there are many such semantic conflicts, by using this method can well solve the problem of semantic heterogeneity among them.

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Section: Ontology Semantic Conflictmentioning

confidence: 99%

Semantic Conflict Resolution Scheme Based on Ontology

Zhao¹

2017

dtetr

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“…The main aim is to provide a uniform view of these data so that users can query and access them in an integrated way. This problem is relevant in many real-world applications, both in commercial and scientific domains [42]. The problem of data integration is addressed by defining a global schema (i.e., a schema available to the user) and mappings between the schemas of the data sources and the global schema.…”

Section: Related Workmentioning

confidence: 99%

Knowledge base exchange: The case of OWL 2 QL

Arenas

Botoeva

Calvanese

et al. 2016

Artificial Intelligence

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In this article, we define and study the problem of exchanging knowledge between a source and a target knowledge base (KB), connected through mappings. Differently from the traditional database exchange setting, which considers only the exchange of data, we are interested in exchanging implicit knowledge. As representation formalism we use Description Logics (DLs), thus assuming that the source and target KBs are given as a DL TBox+ABox, while the mappings have the form of DL TBox assertions. We define a general framework of KB exchange, and study the problem of translating the knowledge in the source KB according to the mappings expressed in OWL 2 QL, the profile of the standard Web Ontology Language OWL 2 based on the description logic DL-Lite R . We develop novel gameand automata-theoretic techniques, and we provide complexity results that range from NLogSpace to ExpTime.

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“…Since the basic requirement for a database to function is data availability, the warehousing approach appears to be more tolerant to various data source conditions than its counterpart solutions which all require data sources to be interoperable and accessible. Warehousing is generally considered most suitable for historical data accumulation, quality data integration, and post-integration data curation and annotation (Halevy, Ashish et al 2005). In biomedical informatics, the warehousing approach is considered most suitable for personalized biomedical data integration (Louie, Mork et al 2007;Wang, Liu et al 2009).…”

Section: Data Warehousingmentioning

confidence: 99%