1. Shared Ontology for Knowledge Management Atanas Kiryakov, Borislav Popov, Ilian Kitchukov, and Krasimir Angelov Meher Shaikh

2. Overview The authors present an approach for semantic searching on the web. Indexing schema based on entity occurrence. Demonstrate scalable implementation of the indexing. KIM platform: allows semantic annotation, indexing of documents with respect to named entities (NE). CORE module: based on co-occurrence of entities. User Interface: CORE Search and Timelines

3. Objective The contemporary search engines uses ranking to provide the relevant information based on string tokens. Involves semantic analysis of the data on the web. Example query: “telecom company in Europe” “John Smith” director Information need: A telecom company in Europe, a person called John Smith, and a management position. A document containing the following sentence would not be returned using conventional search techniques. The search engine needs to be able to consider several semantic relation and inference rule to return this above document.

4. Traditional IR Vector-Space Model (VSM): The documents are characterized by the token appearing in them. The model evaluates the similarity between the query tokens and the tokens appearing in documents to retrieve and rank the documents.

5. Shared Ontology Approach Combines the advantages of the semantic repository and the raw power of relational databases. Semantic repository allows inferring and querying on top of formal knowledge. The relational databases can handle large volumes of data efficiently.

6. KIM  The platform provides infrastructure for automatically extracting named entities (semantic annotations) from the unstructured text. This includes attributes and relations. The extracted information is presented in a knowledge base called the semantic repository. The semantic annotations are then used for indexing of the documents.

7. Semantic repository: repository of entities and their relations Example: The semantic repository infers that London is part of UK

8. Partial Architecture of KIM Platform

9. CORE module Extension of KIM platform with advanced UI Based on robust open source platforms specialized in ontology management, text mining and IR. Focuses on co-occurrence of entities.

10. CORE module cont.. Maintains bi-directional relations between entity and documents. This allows retrieval of entities by documents in addition to retrieval of documents by entities.  Provides incremental searching, ranking, and tracking and popularity timelines of these entities.

11. User Interface: CORE Search

12. User Interface: Timelines Timelines interface allows trends to be calculated and conveniently viewed and navigated through.

14. Conclusion More Meaningful content extracted using semantic analysis and inference. The KIM Platform and its CORE module currently achieve real-time retrieval from about a million documents and a million of entity descriptions. Work is ongoing to deal with the large amount of web resources Synchronization techniques between database and the semantic repository yet to mature.