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Enhancing Information Management with Semantic Web Information Systems (SWIS)

The Semantic Web is increasingly complex, presenting both information overload and starvation. Semantic Web Information Systems (SWIS) leverage explicit, machine-processable semantics to dramatically improve Information Management (IDM). By focusing on scientific discovery, business processes, national defense, healthcare, and education, SWIS can address the challenges of handling vast and diverse datasets. Techniques such as query processing, constraints, and ontology representation are crucial for building effective SWIS, which aims to optimize performance and scalability in managing heterogeneous data.

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Enhancing Information Management with Semantic Web Information Systems (SWIS)

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  1. Workgroup Summary Semantic Web Information Systems (SWIS)

  2. Semantic Web Information Systems • Web is increasingly more complex, and simultaneously there is information overload and starvation. • SWIS uses explicit and machine processable semantics, which will allow orders of magnitude improvement in IDM. • SWIS can significantly enhance in activities such as scientific discovery, business processes, national defense, healthcare, and education.

  3. Why IDM for SWIS • IDM knows how to efficiently deal with very large amounts of diverse information (scale, heterogeneity). • Fundamental IDM concepts/techniques of query processing/views, constraints, modeling … are highly relevant to the realization of SWIS.

  4. SWIS Topics • Languages for representing and reasoning with ontologies-- more expressive, deal with XML and other formats • Constraints • more general/expressive formalisms • at different levels of the architecture • their processing • Semantic preserving/based schema/data transformation (mapping, evolution), integration, versioning

  5. SWIS Topics • Query languages for heterogeneous data (structured/semi-structured/unstructured data), metadata and knowledge bases • Logics for Semantic Web to support processing of uncertain, inexact, incomplete, temporal information • Workflow processes (discovery, negotiation, quality, trust) • User interfaces, interactivity • Optimization, scalability, performance

  6. Recommendation • Focused initiative within IDM • involve basic research and experimentation

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