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Semantic Enhancement vs. Integration Data-Model DSC Solution

Semantic Enhancement vs. Integration Data-Model DSC Solution. Example. Single Ontology. Multiple Data models. Person. Person. PersonName NetworkSkill ProgrammingSkill. Skill. Person Name. Is-a Bearer-of. Computer Skill. PersonSkill.

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Semantic Enhancement vs. Integration Data-Model DSC Solution

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  1. Semantic Enhancement vs. Integration Data-ModelDSC Solution Example

  2. Single Ontology Multiple Data models Person Person PersonNameNetworkSkillProgrammingSkill Skill Person Name Is-a Bearer-of Computer Skill PersonSkill Last NameFirst NameSkill First Name Last Name Skill Programming Skill Network Skill Person NameComputer Skill Ontology vs. Data-Model • Ontology provides a comprehensive hierarchical view of a domain as opposed to a flat and partial representation of a data-model

  3. Sources • Source database Db1, with tables Db1.Person and Db1.Skill, containing person data and data pertaining to skills of different kinds, respectively. • Source database Db2.Person, containing data about IT personnel and their skills: • Source database Db3.ProgrSkill, containing data about programmers’ skills:

  4. Representation in the Dataspace Representation of data-models, SE and SE annotations as Concepts and ConceptAssociations Blue – SE annotations Red – SE hierarchies Native representation of data and data-models as Terms and Statements

  5. Index Entities Based on SE • Dynamic model-driven definition of entities based on user preferences, e.g. users want to deal with Persons’ data, including data about Skills • Index entities based on the SE and native (blue) vocabularies • Leverages syntactic integration provided by DRIF, semantic integration provided by the SE vocabulary and annotations of native sources, and rich semantics provided by ontologiesin general • Entering Skill = Java (which will be re-written at run time as: Skill = Java OR ComputerSkill = Java OR ProgrammingSkill = Java OR NetworkSkill = Java) will return: persons 111 and 444 • Entering ComputerSkill = Java OR ComputerSkill = SQL will return: persons 333 and 444 • entering ProgrammingSkill = Java will return: person 444 • entering Description = Programming will return: person 111 • Allows to query/search and manipulate native representations • Additional querying richness can be achieved by combining pre-materialization with query re-write • Light-weight non-intrusive approach that can be improved and refined without impacting the Dataspace

  6. … and without SE Index entities based on native vocabularies • However much manual effort the analyst is able to apply in performing search supported by the Index entries, the information he will gain will still be meager in comparison with what is made available through the Index based on the SE. Even if an analyst is familiar with the labels used in Db1, for example, and is thus in a position to enter Name = Java, his query will still return only: person 111. Directly salient Db4 information will thus be missed.

  7. Ontology and Data-Model Education Skill Technical Education ComputerSkill ProgrammingSkill SQL Java C++ Amazing semantic enrichment of data without any change to data; enrichment that can grow and change as our understanding of the reality changes For this richness to be leveraged by different communities, persons, and applications it needs to be constructed in accordance with the principles of the SE

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