Given the dominance and importance of the WWW, the syntax of such a language must be formulated using existing web standards for information representation. The RDF framework for the encoding, exchange, and reuse of structured metadata provides a means for adding semantics to a document without making any assumptions about the structure of the document. Objects, Classes, and Properties can be described.
In relation to ontologies, RDF provides two important contributions: a standardized syntax for writing ontologies, and a standard set of modelling primitives like instance-of and subclass-of relationships. Why not Ontolingua? It has been designed to support the design and specification of ontologies with a clear logical semantics based on KIF. Ontolingua extends KIF with additional syntax to capture intuitive bundling of axioms into definitional forms with ontological significance; plus a Frame Ontology to define object-oriented and frame- language terms.
The problem with Ontolingua is its high expressive power provided without any means to control it. Not surprisingly, no reasoning support has been provided for Ontolingua. OIL takes the opposite approach.
We start with a very simple and limited core language. The web has proven that restriction of initial complexity and controlled extension when required is a very successful strategy.
OIL takes this lesson to heart. In On-To-Knowledge, OIL will be extended to a full-fledged environment for knowledge management in large intranets and websites. Unstructured and semi-structured data will be automatically annotated, and agent-based user interface techniques and visualization tools will help the user to navigate and query the information space. Here, On-To-Knowledge continues a line of research that was set up with SHOE and Ontobroker [5]: using ontologies to model and annotate the semantics of information resources in a machine-processable manner.
On-To-Knowledge is carrying out three industrial case studies to evaluate the tool environment for ontology-based knowledge management Section 2 and the associated web inference layer OIL Section 3.
These case studies are chosen such that they ensure a broad coverage, involving three different industry sectors insurance, telecom, energy in three different countries, and facing different knowledge management problems. Swiss Life: organizational memory. Swiss Life's vision is to build an organizational memory with an intranet-based portal.
Three case studies explore the problem space: 1. A skills database contains a large variety of structured and unstructured documents like CVs, recruitment profiles, course and project descriptions. Today these documents do not exist or are not integrated into a single repository. Furthermore, there is no common vocabulary i. Information about an insurance product comprises documents for sales persons, for training purposes, about performing office tasks, etc.
This information is created in different places, in different formats and often not distributed to the right places. The document's web pages make it very hard to find relevant passages, even though there is a division into chapters and sections.
BT: call centres. Call Centres are an increasingly important mechanism for customer contact in many industries. Every transaction should emphasize the uniqueness of both the customer and the customer service person. To do this one needs effective knowledge management [7, 8]. This includes knowledge about the customer but also knowledge about the customer service person, so that the customer is directed to the right person to answer their query.
This knowledge must also be used in a meaningful and timely way. The On-To-Knowledge techniques provide an intuitive front-end to these heterogeneous information sources, to ensure that the performance of the best agents is transferred to the others. Figure 2. Automatically generated semantic structure maps of the EnerSearch website. EnerSearch: virtual enterprise. EnerSearch is a virtual organization researching new IT- based business strategies and customer services in deregulated energy markets e.
Essentially, EnerSearch is a knowledge creation company, knowledge that must be transferred to its shareholders and other interested parties. Its website is one of the mechanisms for this. However, it is rather hard to find information on certain topics — the current search engine supports free text search rather than content-based search.
It shows two semantic structure maps of the EnerSearch website, produced by the WebMaster tool of AIdministrator [6], and based on a domain ontology concerning important IT in Energy research topics. Every node represents a webpage that can be directly opened in a browser by clicking on the node ; edges denote hypertext links. Left, we see a map of subtypes subtopics of agent research by EnerSearch. It is easy to see how subtopics are related and find the relevant webpages.
Right, we see an interactively generated sitemap showing how the topic of e-commerce intersects with other topics dark blue nodes. A nice feature of the visualization is that spatial proximity correlates very well with semantic closeness. In addition to the toolset and the OIL language, On-To-Knowledge is developing an associated methodology for ontology-based knowledge management. Input to this are existing European research results, such as the CommonKADS approach to knowledge engineering and management [3], experiences from knowledge-based software engineering [12] and tool development [], ontology composition [2] and information retrieval techniques [14], and feedback from industry case studies.
The methodology will also cover how to develop the business case for ontology-based knowledge management. World-Wide Web and company intranets have boosted the potential for electronic knowledge acquisition and sharing. Given the sheer size of these information resources, there is a strategic need to move up in the data — information — knowledge chain.
As a necessary step, On-To-Knowledge provides innovative tools for semantic information processing and thus for much more selective, faster, and meaningful user access. References [1] D. Springer- Verlag, Berlin, D, to appear Borst, J. Akkermans, and J. Schreiber, J. Akkermans, A. Anjewierden, R.
Shadbolt, W. Van De Velde, and B. Wielinga: Knowledge Engineering and Management. Reimer Ed. Fensel, S. Decker, M. Erdmann, H. Schnurr, R. Studer, and A. Journal of Cooperative Information Systems, to appear In Proceedings 2nd Int. Davies, S. Stewart, and R. Roy Ed. Bremdal, F. Johansen, C. Spaggiari, R. Engels, R. Maedche, H. Schnurr, S. Staab, and R. In: Frank Ed. Koblenz, D, April Horrocks, D. Fensel, J. Broekstra, S.
Erdmann, C. Goble, F. Klein, S. Different technologies will be exploited to provide these annotations, depending on whether we are dealing with weakly structured data sources, or data sources that consist of free text only. In the first case, we will use wrapper technology in the vein of Jedi or W4. Other tools will be based on automated summarization technology as developed for ProSum by BT [7,8]. Steps [5,6]. After the RDF query has been executed over the data repository, the resulting information is communicated to the user.
Again, this must be done using an ontology-based vocabulary. Furthermore, powerful graphical visualizations of query results in the context of large data sets are developed.
Examples of such visualizations are the semantic sitemaps produced by the WebMaster tool of AIdministrator [6] for some results see Section 4. OIL is a representation and inference layer on top of the Web that is based on ontologies. It unifies three important aspects provided by different communities: i formal semantics and efficient reasoning support as provided by Description Logics, ii epistemologically rich modelling primitives as provided by the Frame community, and iii a standard proposal for syntactical exchange notations as provided by the Web community.
DLs describe knowledge in terms of concepts and role restrictions that are used to automatically derive classification taxonomies. The main effort of the research in knowledge representation is in providing theories and systems for expressing structured knowledge and for accessing and reasoning with it in a principled way. OIL inherits from DL its formal semantics and the efficient reasoning support developed for these languages.
The central modelling primitives of predicate logic are predicates. Frame-based and object-oriented approaches take a different point of view.
Their central modelling primitives are classes frames with certain properties called attributes. Many other refinements of these constructs have been developed leading to the success of this modelling paradigm. Therefore, OIL incorporates the essential modelling primitives of frame-based systems into its language. OIL is based on the notion of a concept and the definition of its superclasses and attributes.
Relations can also be defined not as an attribute of a class but as an independent entity having a certain domain and range. Like classes, relations can be arranged in a hierarchy. Modelling primitives and their semantics are one aspect of an ontology-based exchange language. Given the dominance and importance of the WWW, the syntax of such a language must be formulated using existing web standards for information representation.
The RDF framework for the encoding, exchange, and reuse of structured metadata provides a means for adding semantics to a document without making any assumptions about the structure of the document.
Objects, Classes, and Properties can be described. In relation to ontologies, RDF provides two important contributions: a standardized syntax for writing ontologies, and a standard set of modelling primitives like instance-of and subclass-of relationships.
Why not Ontolingua? It has been designed to support the design and specification of ontologies with a clear logical semantics based on KIF. Ontolingua extends KIF with additional syntax to capture intuitive bundling of axioms into definitional forms with ontological significance; plus a Frame Ontology to define object-oriented and frame- language terms. The problem with Ontolingua is its high expressive power provided without any means to control it.
Not surprisingly, no reasoning support has been provided for Ontolingua. OIL takes the opposite approach. We start with a very simple and limited core language. The web has proven that restriction of initial complexity and controlled extension when required is a very successful strategy. OIL takes this lesson to heart. In On-To-Knowledge, OIL will be extended to a full-fledged environment for knowledge management in large intranets and websites.
Unstructured and semi-structured data will be automatically annotated, and agent-based user interface techniques and visualization tools will help the user to navigate and query the information space. Here, On-To-Knowledge continues a line of research that was set up with SHOE and Ontobroker [5]: using ontologies to model and annotate the semantics of information resources in a machine-processable manner.
On-To-Knowledge is carrying out three industrial case studies to evaluate the tool environment for ontology-based knowledge management Section 2 and the associated web inference layer OIL Section 3.
These case studies are chosen such that they ensure a broad coverage, involving three different industry sectors insurance, telecom, energy in three different countries, and facing different knowledge management problems. Swiss Life: organizational memory. Swiss Life's vision is to build an organizational memory with an intranet-based portal.
Three case studies explore the problem space: 1. A skills database contains a large variety of structured and unstructured documents like CVs, recruitment profiles, course and project descriptions. Today these documents do not exist or are not integrated into a single repository. Furthermore, there is no common vocabulary i.
Information about an insurance product comprises documents for sales persons, for training purposes, about performing office tasks, etc. This information is created in different places, in different formats and often not distributed to the right places.
The document's web pages make it very hard to find relevant passages, even though there is a division into chapters and sections. BT: call centres. Call Centres are an increasingly important mechanism for customer contact in many industries.
Every transaction should emphasize the uniqueness of both the customer and the customer service person. To do this one needs effective knowledge management [7, 8]. This includes knowledge about the customer but also knowledge about the customer service person, so that the customer is directed to the right person to answer their query.
This knowledge must also be used in a meaningful and timely way. The On-To-Knowledge techniques provide an intuitive front-end to these heterogeneous information sources, to ensure that the performance of the best agents is transferred to the others. Figure 2. Automatically generated semantic structure maps of the EnerSearch website.
EnerSearch: virtual enterprise. EnerSearch is a virtual organization researching new IT- based business strategies and customer services in deregulated energy markets e. Essentially, EnerSearch is a knowledge creation company, knowledge that must be transferred to its shareholders and other interested parties.
Its website is one of the mechanisms for this. However, it is rather hard to find information on certain topics — the current search engine supports free text search rather than content-based search. It shows two semantic structure maps of the EnerSearch website, produced by the WebMaster tool of AIdministrator [6], and based on a domain ontology concerning important IT in Energy research topics. Every node represents a webpage that can be directly opened in a browser by clicking on the node ; edges denote hypertext links.
Left, we see a map of subtypes subtopics of agent research by EnerSearch. It is easy to see how subtopics are related and find the relevant webpages. Right, we see an interactively generated sitemap showing how the topic of e-commerce intersects with other topics dark blue nodes. A nice feature of the visualization is that spatial proximity correlates very well with semantic closeness. In addition to the toolset and the OIL language, On-To-Knowledge is developing an associated methodology for ontology-based knowledge management.
Input to this are existing European research results, such as the CommonKADS approach to knowledge engineering and management [3], experiences from knowledge-based software engineering [12] and tool development [], ontology composition [2] and information retrieval techniques [14], and feedback from industry case studies.
The methodology will also cover how to develop the business case for ontology-based knowledge management. World-Wide Web and company intranets have boosted the potential for electronic knowledge acquisition and sharing.
Given the sheer size of these information resources, there is a strategic need to move up in the data — information — knowledge chain. As a necessary step, On-To-Knowledge provides innovative tools for semantic information processing and thus for much more selective, faster, and meaningful user access. References [1] D. Springer- Verlag, Berlin, D, to appear Borst, J. Akkermans, and J. Schreiber, J. Akkermans, A. Anjewierden, R. Shadbolt, W. Van De Velde, and B. Wielinga: Knowledge Engineering and Management.
Reimer Ed. Fensel, S. Decker, M.
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