Shared Ontology for Knowledge Shared Ontology for Knowledge ManagementManagement

Atanas Kiryakov, Borislav Popov, Ilian Kitchukov, Atanas Kiryakov, Borislav Popov, Ilian Kitchukov, and Krasimir Angelovand Krasimir Angelov

Meher Shaikh

OverviewOverview• 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

ObjectiveObjective

• 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” directorInformation 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.

Traditional IRTraditional 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.

Shared Ontology ApproachShared 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.

KIMKIM 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.

Semantic repository: repository of entities Semantic repository: repository of entities

and their relationsand their relations

Example: The semantic repository infers that London is part of UK

Partial ArchitecturePartial Architecture of KIM Platform of KIM Platform

CORE moduleCORE 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.

CORE module cont..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.

User Interface: CORE SearchUser Interface: CORE Search

User Interface: TimelinesUser Interface: Timelines

•Timelines interface allows trends to be calculated and conveniently viewed andnavigated through.

ConclusionConclusion

• 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.