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Project Presentation On-To-Knowledge: Content-driven Knowledge-Management Tools through Evolving Ontologies

• Contract number: not yet known
• Project acronym: On-To-Knowledge
• Project name: On-To-Knowledge: Content-driven Knowledge-Management Tools through Evolving Ontologies
• Key Action and Action line: IST Programme 1.1.2., 1.1.2-4.3.3
• Project logo
• List of participants
• Total cost: 2.5 Million Euro
• Commission funding: 1.34 Million Euro
• Project Duration: January 2000 until October 2002
• Project main goal(s)
• Key issues
• Technical approach
• Expected achievements/impact
• Co-ordinator contact details

Project logo

List of participants

• Vrije Universiteit Amsterdam (VU), the Netherlands, (co-ordinator);

• AIdministrator, the Netherlands;

• the Institute AIFB, University of Karlsruhe, Germany;

• British Telecom Laboratories, UK;

• Swiss Life, Switzerland;

• CognIT, Norway; and

• Enersearch, Sweden.

• OntoText Lab.,Sirma AI Lab, Bulgaria.

The AIAI institute is subcontractor for the Vrije Universiteit Amsterdam (VU).

Project main goal(s)

The On-To-Knowledge contribution: ontologies. The On-To-Knowledge project applies ontologies to electronically available information to improve the quality of knowledge management in large and distributed organisations. Ontologies are formal theories supporting knowledge sharing and reuse (cf., CYC, KACTUS, KIF and Ontolingua). They can be used to explicitly represent semantics of semi-structured information. This enables sophisticated automatic support for acquiring, maintaining, and accessing information. For this we will develop a methodology and tools for intelligent access to large volumes of semistructured and textual information sources in intra-, extra-, and internet-based environments to employ the full power of ontologies in supporting knowledge management from two perspectives:

1. The Information Client. Access to knowledge must be simple and effective. The costs and barriers in accessing knowledge has to be lowered and the user needs to be made aware of existing knowledge sources. Existing keyword-based retrieval techniques clearly fail on these requirements. Improving access to information sources is the first main goal of the On-To-Knowledge.

2. The Information Provider: Providing and maintaining large bodies of textual and semistructured information sources with current techniques is a labour-intensive and costly activity. Lowering these costs is the second main goal of the project.

The goal of the On-To-Knowledge project is to support efficient and effective knowledge management. We will focus on acquiring, maintaining, and accessing weakly-structured online information sources:

• Acquiring: Text mining and extraction techniques are applied to extract semantic information from textual information (i.e., to acquire information).
• Maintaining: RDF and XML are used for describing syntax and semantics of semi-structured information sources. Toolsupport enables automatic maintenance and view definitions on this knowledge.
• Accessing: Pushservices and agent techology support users in accessing the information.

A methodology will provides guidelines for introducing knowledge management concepts and tools into enterprises, helping knowledge providers to present their knowledge efficiently and effectively. Three large case studies will help to develop the technology according to the actual needs of large and/or virtual organisations and will provide an ideal testbed for evaluating methods and tools.

Key issues

The approach of the project is the use of ontologies for each of these three processes. To achieve these advanced goals, we will develop an ontology-based tool and environment. We will develop a three-layered tool environment to achieve these goals. At the lowest level (the information level), weakly-structured information sources are processed to extract machine-processable meta-information from them. The intermediate level (the representation level) uses this meta-information to provide automatic access, creation, and maintenance of these information sources. The highest level (called access level) uses advanced push ad pull techniques to lower the thresholds for accessing this information. Agent-based techniques as well as state-of the art querying and visualization techniques can fully employ the formal annotations to guide user access of information. At all levels, ontologies are the key asset in achieving the described functionality. The methodology will complement the tool helping to bridge the gap between information needs and information sources.

Technical approach

The following are the key innovations of the On-To-Knowledge project w.r.t.the state of the art:

• going beyond key-word based search,
• enabling automated information extraction,
• exploiting ontologies, and
• supporting information maintenance.

Beyond key-word based search

There are numerous approaches on information retrieval, text extraction or agent-based information access. However, nearly all of them work at the keyword level. It is well known from information retrieval that keyword-based information access has principal limitations (concerning precision and recall).

Enable automated information extraction

Another main limitation of these approaches is that they usually deliver raw documents (in case of Web search engines these documents are URLs). This requires human effort to extract the required answer (i.e. browse and read the delivered documents until the information has been found). This burdens the human user and drastically hampers automated information extraction by agents. On-To-Knowledge will provide a query answering mechanim for unstructured, weakly structured and formalized documents. Besides query answering facilities (used by humans or software agents) On-To-Knowledge will provide means for creating user-specific views on information documents, for maintaining information content, and for automatically generating new documents from existing.

Exploit ontologies

We will use ontologies to mediate information access and will provide an integrated tool environment that covers acquisition, maintenance, and access to online information based on ontologies. To our knowledge no such project already exists. Ontologies can provide more complex definitions (ranging as far as logical axioms) than is possible with thesauri used in information retrieval. They are our key asset in automating query answering, maintenance, and automatic document generation. The integration of ontologies and automated information retrieval (IR) approaches (as support for ontology generation) are investigated. Where some approaches from IR exist that deal with text analysis, the novelty here is the way in which such techniques are integrated in ontology creation, maintenance, comparison and visualization.

Support for information maintenance

The issue of maintenance, as mentioned in the proposal, clearly goes beyond existing work in information retrieval. We will provide tool support enabling automatic maintenance and view definitions on this knowledge. That is, we will provide systematic support for information providers which is essential in a knowledge management environment.

Expected achievements/impact

The major outcomes of On-To-Knowledge are a methodology, tools, and best-practises for knowledge management:

1. A methodology that provides guidelines for introducing knowledge management concepts and tools into enterprises, helping knowledge providers to present their knowledge efficiently and effectively. The methodology will include the identification of goals that should be achieved by knowledge management tools and will be based on an analysis of business processes and the different roles knowledge workers play in organisations.
2. An intelligent search tool that supports users in accessing information and a tool environment for maintenance, conversion, and acquisition of information sources. These tools are based on a three-layered architecture that helps to bridge the large gap between information needs of users on the one hand and available information sources on the other hand.
3. Three large case studies will help to develop the technology according to the actual needs of large and/or virtual organisations and will provide an ideal testbed for evaluating methods and tools. They will also be highly useful in directly employing the achieved results and in deriving commercial spin-offs from the project.

Co-ordinator contact details