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Corporate Semantic Search

An effective knowledge management of corporate information resources is a cornerstone for a long-lasting competitive advantage. Semantic Web Technologies offer new possibilities for enhanced integration of heterogeneous business data, information discovery as well as advanced automation of sophisticated tasks. The foundation of the Semantic Web is build upon ontologies which formally and unambiguously describe concepts of an application domain as well as relationships among them, thus making data machine interpretable. Utilizing them in corporate portals, as means for representing business objects, could substantially improve both content navigation as well as search results.

As a business oriented research group concentrating on the Semantic Web applied within corporate structures, the Corporate Semantic Web (CSW) project covers a wide spectrum of innovative scientific and application oriented solutions for research problems in a corporate context. One of the pillars of CSW research is represented by Corporate Semantic Search which unites standard search methods with semantic technologies. The systematic integration of business information allows the semantic analysis of the available data and information sources making semantic search an enhancement to the classical search solutions. Corporate Semantic Search, in this context, refers to the different forms of semantic search in corporate and business applications.


Search in Non-semantic Data

The key objective of this research area is the development of a semantic based learning method for trend recognition in hybrid information systems, i.e. systems consisting of both qualitative and quantitative data. Using the financial domain as an example of hybrid information systems and having regard to the multimodal financial data, an adequate trend recognition method will be developed for the recognition of temporal changing patterns in textual information sources relevant to the financial market domain. The focus of this research will be put on developing a solution relevant to the trend mining problem which combines a Data Mining approach with adequate Semantic Web technologies.

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Search Personalization

Searching for business objects like products, projects, employee profiles, etc. is a nontrivial and often occurring task. For complex objects having multiple properties a perfect match is rarely found. Therefore, the user is, in most cases, also interested in a ranking of objects by their semantic similarity values with respect to the personalized query. The focus of this work package lies on utilizing Semantic Web technologies in order to enhance the query capabilities of corporate portals. The research in this area ranges from adaptive and adaptable representation of search profiles, over evaluation of semantically represented business objects with respect to user preferences based on semantic similarity of concepts, to providing the user with requirement based, personalized views on corporate data.

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Search Contextualiztion and Recommender

The application of personalization and context-aware search techniques provides the greatest benefits in environments characterized by user diversity with respect to their preferences, knowledge, goals, environmental context, etc. Such conditions can clearly be observed within business enterprises where personalization and contextualization can be targeted at internal (employees) and external (customers or business partners) users. From the business perspective, the most relevant kind of adaptive systems, providing personalized information access, are recommender systems, due to their prevalence in e-commerce applications like online-shops. Recommender systems address the problem of information overload by reducing the search space to items or resources of interest to the user. Consequently, the focus of this work package lies on the application of Semantic Web technologies to the recommendation task.

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Multimedia Search

Multimedia content has become one of the most important type of resources available on the World Wide Web, however our understanding of it is severely limited due to its non-textual nature. In order to overcome this problem, large Web 2.0 sites allow their users to assign free-text tags multimedia content such as images respectively videos, in order to better index and retrieve it. However, due to its arbitrary nature this approach is limited in it's applicability in scenarios that require machine processing of annotations. The main drawbacks are the lack of a consensual controlled vocabulary for tagging, lack of a standardized mechanism for granular annotation of multimedia content and lack of reusability and lack of support mechanisms based on machine learning in order to support the user in the task of manual annotation. In order to be able to better understand the semantics of multimedia and thus be able to better retrieve and monetize this content, we observe the need innovative and intuitive annotation tools. In our work we propose and develop a framework for the annotation and retrieval of multimedia content that makes use of Semantic Web technologies such as Ontologies and Linked Data as well as croudsourcing and machine learning.

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2012-12-15 10:33

Abschlussveranstaltung des Corporate Semantic Web Projekts

5 Jahre Corporate Semantic Web Abschlussveranstaltung am 16.1.2013

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2012-12-07 18:20

CSW active in OMG API4KB Standardization

API4KB is an initiative within OMG that aims at defining a standard programming interface for knowledge bases

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© 2008 FU Berlin | Feedback
This work has been partially supported by the  InnoProfile-Corporate Semantic Web project funded by the German Federal Ministry of Education and Research (BMBF) and the BMBF Innovation Initiative for the New German Länder - Entrepreneurial Regions.
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