KAON The Karlsruhe Ontology and Semantic Web Meta Project Alexander Maedche1 & Steffen Staab2 Forschungszentrum Informatik, Karlsruhe, Germany, http://www.fzi.de/wim 2 Institut AIFB, Universität Karlsruhe, Germany, http://www.aifb.uni-karlsruhe.de/WBS & Ontoprise GmbH, Karlsruhe, Germany, http://www.ontoprise.de 1
Introduction The Semantic Web is composed of applications that share, exchange and link self-describing data (instead of text) via the World Wide Web rendering it a kind of global, distributed database – with a lot of semi-tidy information in it that may also allude to very different conceptualisations. To formally specify the data and the conceptualisation, there exists an architectural agreement in form of the famous Semantic Web representation layer cake. The cake (http://www.w3.org/2001/09/06-ecdl/slide17-0.html) characterizes different language layers on which data may be self-describing (cf. a slightly modified version on the vertical axis in Figure 1).
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Figure 1: Orthogonal Layers in the Semantic Web The layer cake builds on XML as a syntactic exchange format. It uses RDF (Resource Description Format) and RDFS (RDF Schema) in order to provide a data model and a simple structuring mechanism that abstracts from serialization concerns. On top of these two layers, different logical mechanisms are put in order to offer more sophisticated mechanisms for modelling and reasoning, i.e. OWL (Web Ontology Language, http://www.w3.org/TR/owl-ref/) and further languages that yet need to be defined. This picture of the layer cake abstracts from two dimensions that we consider pre-eminent for the Semantic Web:
1. The first dimension is a Semantic Web management challenge, viz. applications in the Semantic Web will have to deal with a lot of dynamics in their lifetime, such as transactions, evolving schemata/ontologies/etc. (cf. horizontal axis of Figure 1). 2. The second observation is a potential benefit, viz. that self-describing, interlinked and interchangeable data will allow for generalized applications that are configured by modelling and combining modules to a large extent rather than being built from scratch each time a new application domain comes up (cf. diagonal axis of Figure 1). With the Karlsruhe Ontology and Semantic Web Meta-Project KAON we target at • • •
moving along from the bottom to the top of the Semantic Web representation layer cake, handling (at least some of) the dynamic nature of the Semantic Web, and demonstrating how some applications like portals, content management or Web Services may be driven by modelling and some commonly available modules (at least to a large extent).
In order to give an idea of how KAON puts this into practice, we here survey some modules of the KAON tool suite. As a running example we here consider a portal for a research & development community, i.e. the “VISION” community that gathers to share insight about knowledge management research and practice. First, the portal is built using a service management component allowing for plug-n-play design and implementation of portal functionalities. Second, the portal requires a concrete ontology and data repository able to support their development, extension and evolution. Third, the portal needs ways to gather data and metadata – such as provided by annotation tools we have developed. Finally, the portal needs to be realized with a concrete user interface. These four (out of a richer set of) components are presented in the remainder of the paper. We want to note here that the modules of the KAON tool suite have been or are currently developed in a number of different projects, which is the reason why we call KAON a meta project, viz.: WonderWeb (EU IST FET-O), SWAP – Semantic Web and Peer-to-Peer (EU IST), OntoWeb - Ontology based information exchange for knowledge management and electronic commerce (EU IST), OntoLogging - Corporate Ontology Modelling and Management System (EU IST), SWWS - Semantic Web Enabled Web Services (EU IST) and PADLR – Personalized Access to Distributed Learning Resources (BMBF). The currently available source code and executables can be downloaded from http://kaon.semanticweb.org (also cf. [2]).
KAON Server – A Semantic Web Management System KAON Server [9] is a comprehensive and sophisticated software entity enabling the management of Semantic Web components. It has been (and still is) developed in order to serve the very different needs of clients, such as Semantic Web tools (like OntoMatAnnotizer, cf. below) or applications like the KAON Portal (cf. below). Its purpose is to integrate different functional components (e.g. KAON storage and transaction modules like the OI model engineering server, cf. below) and external services (e.g. inference engines like Triple [11]). Our motivation is that if the Semantic Web can be very roughly compared to a kind of global database, then a Semantic Web management system is completely lacking so far. In a way, this situation is unavoidable as the functionality of such a system is far from being well understood at the current point in time with many theoretical foundations concerning such a system currently being under research, such as views [15], evolution [13], or versioning [6] to name but three.
Nevertheless, instead of a flock of tools that only interoperate by reading and writing data into files in some Semantic Web format, there is an immediate need for a Semantic Web Management System that, (i), provides interoperability between Semantic Web modules, (ii), is flexibly configurable, and, (iii), is open to new developments in theory and implementation. KAON Server complements the static part of the Semantic Web layer cake by managing components that cover the dynamic aspects of the Semantic Web (cf. Figure 1). It consists of two core building blocks, viz. •
a Component Management module, which is able to o load and unload components on the fly as needed, o register and manage the components, o provide communication between components through a flexible event mechanism.
•
a Semantic Web data API, which o allows for dynamic instantiations by components that extend the functionality of the core KAON Server modules towards a full-fledged Semantic Web management system. o answers to the needs put forward by the static part of the Semantic Web layer cake, viz. the definitions of Semantic Web languages.
The principal concept of KAON Server has been developed in a way that it may easily accommodate minor changes in the structure of the Semantic Web layer cake or in the dynamic requirements. Additionally, it integrates tools that support different individual layers of the layer cake. KAON Server also supports to channel information between these components and to coordinate the information flow. Speaking in technical terms, it implements a service oriented architecture (SOA), representing either standalone or embedded middleware. Unlike in classical database management systems (and besides of a number of other distinctions) there is actually not a single data model for the Semantic Web (cf. Figure 1). Instead, several data models are used, some of them are even unspecified by now, e.g. the data models employed to implement the rule language, the proof interchange and trust layers. At first glance, this appears not to be of any problem since all data models rely on the same meta-grammar, namely XML. However, any practical usage requires to talk about the specific data model addressed, e.g. when trying to implement transactional behaviour or security. Almost all functionality in the domain of the management system shows such a dependency. Hence, all those characteristics directly lead to the need of an open, flexible and extensible architecture, such as implemented for KAON Server. In addition to the extensibility, the interoperation of components must be ensured since upper layers often rely on functionality specified and provided by lower layers, e.g. data typing for RDF and OWL taken from XML Schema. However, due to the inherent complexity no single server was yet able to support everything simultaneously. In order to build a reasonably complete system to support the Semantic Web one must draw from functionalities of existing software and must be able to include and manage them. 1
KAON Server builds on current state-of-the-art solutions for component management. It will allow for integration of existing clients like ontology editors (e.g. OILEd [1] or OntoEdit [14]) through dynamically loadable adapters that translate between their internal structures and the KAON Server API. Additionally, it allows for a large variety of configurations, which are capable of providing different functionality depending on the selection of hosted components. In the following we highlight a KAON functional component (the OI-model Engineering Server), a client (the OntoMat-Annotizer) and an application (KAON Portal).
Functional backend component: OI-model Engineering Server The OI-model Engineering Server is an implementation of the KAON Server Data API using relational databases for ontology management. The name Engineering Server stems from the fact that the server is optimized for ontology engineering, where creation and deletion of concepts is a common operation. Hence, the Engineering Server has a fixed number of tables in the schema, rather than allocating a table per concept for storing the extensions of concepts and properties. The OI-model Engineering Server implements several important elements required for ontology management:
The optimized loading component is responsible for bulk-loading of ontology entities. To improve performance, entities are cached at the client. Concurrency conflict detection is responsible for detecting and resolving conflicts resulting in concurrent updates of different users. For example, if one user updates the ontology, then other active users must be notified of this update. Alternatively, if a user attempts to update the ontology using stale information, the conflict must be detected. Change reversibility is responsible for keeping track of the ontology changes in an evolution log in order to be able to reverse them at user's request. Further, the evolution log is also used by the distributed ontology evolution. Evolution strategies are responsible for making sure that all changes applied to the ontology leave the ontology in a consistent state and for preventing illegal changes. Also evolution strategies allow the user to customize the evolution process. Ontology inclusion facilities in conjunction with corresponding evolution strategies are responsible for managing multiple ontologies within one node. Ontology replication facilities together with distributed evolution strategies are responsible for enabling reuse of distributed ontologies.
The OI-model Engineering Server has been heavily optimized and tested on an ontology consisting of 100,000 concepts, 66,000 properties and 1,000,000 instances. To give some example figures: loading of related information about 20 ontology entities takes under 3 seconds, while deleting a concept in the middle of the concept hierarchy takes under 5 seconds. It works on a common single processor desktop computer running Windows XP with 256MB of RAM.
Client: Metadata Generation with the OntoMat-Annotizer The OntoMat-Annotizer implements the (S-)CREAM framework [3] that provides for metadata generation from web pages. It implements five types of metadata generation: 1. Metadata generation as conventional knowledge acquisition by typing of facts with guidance from the ontology. 2. Metadata generation by mark-up of web pages using guidance from the ontology. 3. Metadata generation by authoring web pages using an existing fact base. 4. Metadata generation by learning wrappers and automatic information extraction rules from example annotations and applying them to previously unseen pages [4]. 5. Metadata generaion by constructing mappings between databases and ontologies [5]. In order to actually exploit these four types of metadata generation, it has been necessary to provide a comprehensive infrastructure. For instance, it is necessary to dynamically load an ontology. Second, one may have to crawl metadata from specified web sites in order to find existing facts, e.g. all the facts about one’s colleagues and co-authors. Then only, one may explore the fact base – using reasoning with instances – in order to correctly relate objects for which new identifiers are created in the Semantic Web with objects (e.g. colleagues) for which such an object identifier has already been created by someone else (e.g. the colleague herself). Hence, the OntoMat client needs comprehensive support to store,
access, modify, replicate and visualize ontology-based information – which is a task for the KAON tool suite.
Application: Building semantics-driven Web portals using KAON Portal KAON Portal is an application framework for developing semantics-driven Web portals based on TomCat and the SEAL methodology [7]. KAON Portal exploits ontologies available via KAON server for allowing users a semantics-based access to content available within a Web portal. KAON portal includes the following components:
Templates for concepts, instances and properties Tree View component Similarity and Ranking component Search component Logging component User management component
A concrete instantiation of KAON Portal is the VISION Portal1. VISION is a EU-funded knowledge management roadmap project that first describes state-of-the-art of knowledge technologies and second develops a roadmap for future research and development in this field. The state-of-the-art report with respect to relevant projects, organizations and software has been also represented in the form of an ontology and associated instances and a semantics-driven access to this data has been made available via a Web portal. KAON served as an integrated infrastructure for setting up and running this portal.
Conclusion We have introduced the Karlsruhe Ontology and Semantic Web Meta-Project KAON as a framework for putting the Semantic Web into practice. KAON complements the Semantic Web representation layer cake with the dimensions of management and application that are essential for leading the Semantic Web to its full potential. The comprehensive software infrastructure provided by KAON enabled us to successfully implement applications in the fields of E-Learning [10], Tourism information systems [8], Knowledge Management [12], and Web Portals [7].
References [1]
S. Bechhofer, I. Horrocks, C. Goble, and R. Stevens. OilEd: A reason-able ontology editor for the semantic web. In KI-2001: Advances in Artificial Intelligence, LNAI 2174, pages 396–408. Springer, 2001.
[2]
E. Bozsak, M. Ehrig, S. Handschuh, A. Hotho, A. Mädche, B. Motik, D. Oberle, C. Schmitz, S. Staab, L. Stojanovic, N. Stojanovic, R. Studer, G. Stumme, Y. Sure, J. Tane, R. Volz, V. Zacharias. KAON –
1
http://wim.fzi.de:8080/visionportal/dispatcher
Towards a large scale Semantic Web. In: Proceedings of EC-Web 2002. Aix-en-Provence, France, September 2-6, 2002. LNCS, Springer, 2002, pp. 304-313. th
[3]
S. Handschuh, S. Staab. Authoring and Annotation of Web Pages in CREAM. In: Proceedings of the 11 International World Wide Web Conference, WWW 2002, Honolulu, Hawaii, May 7-11, 2002. ACM Press.
[4]
S. Handschuh, S. Staab, F. Ciravegna. S-CREAM – Semi-automatic CREAtion of Metadata. In: Proc. of the European Conference on Knowledge Acquisition and Management – EKAW-2002. Madrid, Spain, October 1-4, 2002. LNCS/LNAI 2473, Springer, 2002, pp. 358-372.
[5]
S. Handschuh, S. Staab, R. Volz. On Deep Annotation. In: Proceedings of the 12th International World Wide Web Conference, WWW 2003, Budapest, Hungary, May 20-24, 2003. ACM Press.
[6]
M. Klein, A. Kiryakov, D. Ognyanov, D. Fensel. Ontology versioning and change detection on the Web. In 13th International Conference on Knowledge Engineering and Knowledge Management (EKAW02), Sigenza, Spain, October 1-4, 2002, 2002.
[7]
A. Mädche, S. Staab, R. Studer, Y. Sure, R. Volz. SEAL — Tying Up Information Integration and Web Site Management by Ontologies. IEEE Data Engineering Bulletin, 25(1): 10-17, 2002.
[8]
A. Mädche, S. Staab. Applying Semantic Web Technologies for Tourism Information Systems. In: K. th Wöber, A. Frew, M. Hitz (eds.), Proceedings of the 9 International Conference for Information and Communication Technologies in Tourism, ENTER 2002. Springer Verlag, Innsbruck, Austria, 23 - 25th January 2002.
[9]
R. Volz, D. Oberle, S. Staab, B. Motik. KAON SERVER - A Semantic Web Management System. In: Proceedings of the WWW-2003 Alternate Track on Practice and Experience, Budapest, Hungary, May 20-24, 2003. Published at http://www2003.org.
[10]
C. Schmitz, S. Staab, R. Studer, G. Stumme, J. Tane. Accessing Distributed Learning Repositories through a Courseware Watchdog. E-Learn-2002 – Proc. of the World Conference on E-Learning in Corporate Government, Health Care, & Higher Education. Montreal, CA, October 15-19, 2002. AACE.
[11]
M. Sintek, S. Decker. TRIPLE: Reasoning with multiple ontologies. In S. Staab, R. Studer (eds.) Handbook on Ontologies in Information Systems. International Handbooks on Information Systems, Springer Verlag (to appear 2003).
[12]
S. Staab. Wissensmanagement mit Ontologien und Metadaten. Informatik Spektrum. Springer, 25(3): 194-209, 2002.
[13]
L. Stojanovic, A. Maedche, B. Motik, N. Stojanovic. User-Driven Ontology Evolution Management. Proc. of EKAW 2002. LNCS, Springer, pp. 285-300. http://link.springer.de/link/service/series/0558/bibs/2473/24730285.htm
[14]
Y. Sure, J. Angele, S. Staab. Guiding Ontology Development by Methodology and Inferencing. In: K. Aberer, L. Liu. ODBASE-2002 – Ontologies, Databases and Applications of SEmantics. Irvine, CA, USA, Oct. 29-31, 2002. LNCS, Springer, 2002.
[15]
R. Volz, D. Oberle, R. Studer. Views for light-weight web ontologies. In Proceedings of the ACM Symposium on Applied Computing SAC 2003, March 9-12, 2003, Melbourne, Florida, USA, 2003.
PD Dr. Steffen Staab Steffen Staab, born 1970, is a lecturer at the Institute for Applied Informatics and Formal Description Methods (AIFB) of the University of Karlsruhe. He has received a M.S.E. from the University of Pennsylvania in 1994, a Dr. rer. nat. from the University of Freiburg in 1998 and his habilitation in applied informatics from the University of Karlsruhe in 2002. Steffen’s research interest lie in a broad range of applying and improving semantic technologies about which he has published over 80 papers in refereed journals, conferences and workshops. In 1999 he has co-founded Ontoprise GmbH. Steffen is particular eager to hear about trends and controversies interesting to the Intelligent Systems community, as he is editing the corresponding department of IEEE Intelligent Systems. Contact him at
[email protected] Dr. Alexander Maedche Alexander Maedche, born 1973, is department manager of the Knowledge Management Research group at the FZI (Research Center for Information Technologies), University of Karlsruhe. He received a Diploma in industrial engineering in 1999 and his PhD in applied
informatics in 2001, both from the University of Karlsruhe. His research interests cover knowledge discovery in data and text, ontology management and learning. Alexander has published over 70 papers in refereed journals, conferences and workshops. Contact him at the FZI Research Center for Information Technology, Univ. of Karlsruhe, Haid-und-Neu-Str. 10-14, 76131 Karlsruhe, Germany,
[email protected].
