P2P Knowledge Management: an Investigation of the Technical Architecture and Main Processes

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1 P2P Management: an Investigation of the Technical Architecture and Main Processes Oscar Mangisengi, Wolfgang Essmayr Software Competence Center Hagenberg (SCCH) Hauptstrasse 99, A-4232 Hagenberg, Austria {oscar.mangisengi, Abstract This paper presents the architecture of peer-to-peer (P2P) management of knowledge artifacts based on a distributed ontology. We analyze the main processes of Management (KM), namely, knowledge acquisition and knowledge retrieval. P2P information management and ontologies yield significant advantages when combined and applied as advanced Web application for KM: (1) the knowledge owner (called knowledge peer) keeps control of his/her ontologies and knowledge artifacts against other knowledge peers, (2) corporations benefit because valuable knowledge will spread among knowledge peers due to P2P properties and thus will not be lost even if particular individuals leave the corporation. 1. Introduction is a critical resource and an essential element for any business activity as well as for supporting an enterprise s business strategy respectively [7]. The classical myth of knowledge management (KM) systems for finding knowledge relevant to the problem at hand is becoming increasingly critical in the dynamically changing world of business [5][6]. A classical KM approach is managed by a centralized model, since centralization brings advantages in terms of scope, control, and organization. However, centralized KM systems remain issues on ad-hoc or loosely coupled systems, specialist file sharing, specialist indexing, and deployment [8]. The major obstacle to our mind is that knowledge workers hesitate to release their knowledge from their own control thus giving up autonomy and anonymity. Therefore, corporations fail to establish a centralized knowledge base, which is believed to solve all of their problems. Peer-to-peer (P2P) computing model allows separating the concepts of authoring information and publishing that same information which allows for decentralized application design [20]. The notion of P2P-based distributed application design also enables decentralized and distributed KM systems thus achieving a selforganized management. On the other hand, ontology, as an explicit specification of a conceptualization [4], provides a significant contribution to influence the further development of KM systems [5][11][3][6][19]. There exist some research efforts corresponding to KM systems, ontology, and P2P computing as follows. In [11] [3] [6] [19] KM systems are developed based on ontology approach. The next-generation KM is being investigated in [12]. Recently, substantial research efforts of industrial companies and universities concerning P2P computing are conducted in [2] [13]. In addition, other efforts on using P2P computing for KM are given in [9] [8]. In this paper we propose a model for ontology-based management of knowledge artifacts using a P2P computing model. We identify the processes to acquire knowledge of knowledge artifact resources as well as to retrieve knowledge from a distributed knowledge base. We approach the superimposed layer for classifying knowledge artifacts using XTM Topic Maps. The remainder of this paper is organized as follows. Section 2 presents the role of ontologies and P2P in KM as well as related work. Section 3 presents an overview of the implemented architecture. In section 4 we present the knowledge acquisition process of knowledge artifacts where as section 5 presents the knowledge retrieval process. Finally, conclusions and further work are given in section The Role of Ontologies and Peer-to-Peer Computing in Management A knowledge artifact is anything that allows knowledge to be communicated independently of its holder [14] (e.g., documents, database entities, s, workflows). artifacts may either be of structured, semistructured, or unstructured nature. In KM systems, an ontology [4] is used to capture implicit knowledge of the knowledge workers and to associate it with knowledge artifacts for classification, search, and browsing purposes.

2 Both, the knowledge artifacts and the ontology are to be managed in a P2P style thus residing on a node in the P2P information system and acting both as client and server (i.e. servent). Each node can directly communicate with other nodes (i.e. building a P2P network) for acquiring and retrieving knowledge. Based on the key properties of P2P systems [1][2], we believe that P2P - based on ontologies enhances KM due to the several aspects: First, P2P systems are used for information discovery and sharing. If data and information are constantly enriched with semantics (e.g. by using ontologies), P2P can be the ideal basis for supporting KM. Furthermore, P2P systems have no central coordination or database. This corresponds to the fact that KM is a process where corporations fail to enforce central control and authority. In contrast, the global behavior of a P2P KM system (i.e., capturing, sharing, and leveraging knowledge) emerges from individual local interactions among the participating peers (i.e, the knowledge workers). Additionally, P2P nodes are autonomous. This corresponds to the problem that knowledge workers hesitate to disseminate their knowledge. Applying P2P entitles knowledge workers to keep the autonomy on their knowledge since their artifacts and ontology reside at their own peer. Thus, knowledge workers can control who should have access to which parts of the resources. Generally, in P2P environments every node pays its participation by providing access to its resources. Thus, sharing knowledge artifacts is a win-win situation comparable to sharing MP3s ( I have something you do not have and I need something you have ). The widespread usage of systems like Napster and Gnutella illustrated the potential of such approaches. Furthermore, computational efficiency is not the main goal of P2P systems. Instead, exploiting existing resources, autonomy, or anonymity are (some of) the main driving forces. Thus, exploiting existing knowledge artifacts whilst preserving the autonomy and to some extend the anonymity of knowledge workers can contribute significantly to the success of KM efforts. Finally, P2P systems are self-organized information systems. Thus, P2P has the potential to stimulate the acquisition and retrieval of knowledge. Corporations will also benefit because valuable knowledge will spread among knowledge workers and will not be lost even if particular individuals leave the corporation. P2P also captures great potential to overcome semantic heterogeneity with respect to distributed ontologies due to its self-organizing properties. 3. Architecture Overview This section provides an overview of the system architecture of our model for ontology-based management of knowledge artifacts using a P2P approach. Figure 1 illustrates the four layers implementing a knowledge peer namely the P2P communication layer, the knowledge artifact management layer, the knowledge artifact resources layer, and the data sources layer. The following sub-sections describe the particular layers in more detail. P2P communication layer Browsing artifact management layer Loading, extraction artifact resources layer Media Data sources layer Searching P2P Network Query services Modeling artifact Database Merging, integration metadata acquisition management Capture Descriptive metadata Document Figure 1. Four-layered architecture 3.1. Data Sources Layer The data sources layer holds the knowledge artifact sources stored in different systems and they consist of different types in structure (i.e., (un-/semi-) structured sources, e.g. documents, s, database entities, etc.). Document sources may also have different file formats. The physical sources of these back-end systems are exported to the knowledge artifact resources layer using a back-end interface abstraction. It allows identifying metadata required for the self-organized management.

3 3.2. Artifact Resources Layer The knowledge artifacts layer realizes a central local for storing knowledge artifacts within a peer node. It also contains descriptive metadata derived from importing data sources used for realizing selforganized management. Providing a local knowledge artifact has advantages in terms of consistency with the local ontology and - as it requires an additional step - also facilitates the awareness of knowledge workers, which of their knowledge artifacts they want to share with others. On the other hand, the redundancy has obviously disadvantages in terms of consistency with the back-end systems. There are several possibilities for realizing the layer. However, since the majority of knowledge artifacts are represented as unstructured documents, then, to realize our prototype and for search improvement we use a simple file-based approach, namely a file format classification. Additionally, this approach is compliant to the flat file storage structure of the JXTA P2P computing we use within our prototype. It is yet to evaluate, which form of is best for our model when realizing a larger scale application Artifact Management Layer The knowledge artifact management layer is a superimposed layer to the knowledge artifacts in the knowledge artifact. The layer uses semantic modeling intended for representing relationships among knowledge artifacts using XML Topic Maps (XTM). The knowledge artifact management layer comprises the following: The knowledge acquisition process is the process to acquire knowledge artifacts and to create a superimposed layer on top of the knowledge artifact resources. The process captures the semantics of knowledge artifacts represented as ontologies. In our work the superimposed layer is represented as semantic metadata (i.e. XTM) further explained in section 4. represents the central knowledge of a peer node. It stores semantic metadata and provides tools for semantic modeling. retrieval process is the process for retrieving knowledge in a local peer or other peers in P2P environment. The retrieval process can be done using local, and point-to-point search mechanisms as further described in section P2P Communication Layer The P2P communication layer particularly supports the dissemination and sharing of knowledge using ontology and artifact exchanges. This layer consists of query services and the P2P network itself. The P2P network provides connections and communication among peers as well as a set of protocols for advertising and publishing resources of a peer. The query service is responsible to send a query to another peer node respectively to receive a query form other peer nodes. The query service is used for supporting the discovery services and allows for the propagation of queries to other peer nodes. The query service consists of query sender, a query receiver, query requests, and query responses. In our prototype, the JXTA framework has been applied for realization. 4. The Acquisition Process In this section we present the knowledge acquisition process in more detail. Due to achieving autonomous peer nodes (knowledge workers), the peer nodes have to create semantics of knowledge artifacts resources themselves, before they can publish its knowledge resources. The knowledge workers define their knowledge scope within knowledge artifacts in their local repositories. The knowledge acquisition process is given in Figure 2. The local semantic metadata is created from descriptive metadata, and the descriptive metadata consists of descriptions of knowledge artifact resources. Furthermore, the semantic metadata describes occurrences to knowledge artifacts. The process comprises the activities described in more detail in the subsections below. Local knowledge subject 1 (XTM) subject 2 Local knowledge artifact Desciptive Figure 2. acquisition process 4.1. Artifact Loading and Extraction The loading process imports knowledge artifact resources from the data sources layer to the local knowledge artifact of a peer node. The results of the loading and extraction process are knowledge artifact resources stored in the local knowledge artifact and descriptive metadata. The descriptive metadata is obtained from extracting properties of the knowledge artifact sources. It

4 facilitates managing knowledge artifact resources from various different formats (i.e. unstructured data, semistructure data). In our work coherent description content is applied for supporting the descriptive metadata. It is intended to make knowledge artifact resources logical and well organized/managed, as well as convergence description information to provide the metadata of the knowledge artifact resources. The latter facilitates the capturing of the semantics of knowledge artifact resources. The descriptive metadata consists of information such as title, author, source, location, date, type of files, date of upload, and member of access Ontology Preparation and Capture The ontology preparation is intended for preparing knowledge represented as ontologies for a local peer. This preparation process describes knowledge artifact resources and stores the descriptions of knowledge artifact resources in the metadata as descriptive metadata as given in Section 4.1. The ontology capturing converts descriptive metadata into semantic metadata as a superimposed layer on top of the knowledge artifact resources. The semantic metadata supports descriptions for accessing knowledge artifact resources in a local peer or other peers. Therefore, it provides location information of the knowledge artifact resources. The information is understandable to other peers for exchanging data and supporting interoperability among applications, such as the Uniform Resource Locator (URL) or the Uniform Resource Identifier (URI). The semantic metadata of knowledge artifacts resources is stored in the knowledge. Within our work, we use XTM [15] to represent semantic metadata. Other semantic metadata that supports Web ontology are, for instance, RDF [16], OIL [18], and DAML [17]. To model knowledge artifacts resources into XTM Topic Maps, we assume that: A knowledge artifact consists of a set of subjects on knowledge artifact resources. A set of subjects on knowledge artifact resources can be represented as a set of topic maps. A topic map denotes a subject on knowledge artifact resources and consists of a set of topics. A topic (or addressable subject) has a set of addressable knowledge artifact resources represented as occurrences. A set of addressable information may be an URL address to data resources Ontology Merging and Integration acquisition also supports merging and integrating ontologies. The process occurs when a peer node sends query requests to search knowledge in a P2P network and the peer node receives query responses (a set of topic map ontologies) from other peers. In our work, the merging and integration of ontologies are approached based on the merging and integration of the XTM Topic Maps. The following merging and integration features are provided: The peer is able to add topics, when the peer finds that topics are not available in its topic maps. The peer is able to merge and integrate topic maps for the same subject. In the merging and integration process of a peer node, the local topic maps ontology and the topic maps ontology from other peer nodes can be merged and integrated to establish a global topic maps ontology in the context of a peer node. The global topic maps ontology resolves conflicts between ontologies among peers. To reach the global topic maps ontology, the peer node distinguishes between ontologies based on the similarity relations between concepts. To find out the similarities between two ontologies expert familiar or hybrid semi-automatic methods are used. The global topic maps ontology will be stored back in the knowledge. Therefore, with this process the peer can achieve autonomy and a decentralized as well as self-organized management Other Ontology Services Other ontology services available in the knowledge acquisition layer are provided, such as browsing to accessing knowledge artifact, searching to find a particular topic of knowledge, and metadata management for managing semantic metadata (i.e., storing, writing, updating). 5. The Retrieval Process and Case Study This architecture provides local and point-to-point search mechanisms using protocols of P2P computing for communication and ontology searching for retrieving knowledge. The local search mechanism enables a peer to search knowledge in the local peer, whereas the point-topoint search mechanism provides searching between 2 dedicated peer nodes. Sharing knowledge and accessing knowledge resources are provided using content management service Case Study In our work, we create an ontology in XML Topic Map (XTM) document to represent security mechanisms and its type. Based on our case study, a user (peer) can easily create its local ontology and the user can easily to retrieve and summarize which knowledge available in the local

5 node. The peer can browse a knowledge artifact in relation to a searched topic. In addition, based on the proposed architecture, P2P-based KM, a query, ontology merging and integration mechanisms, and a security mechanism for controlling valuable knowledge become critical to provide self-organized management. 6. Conclusion and Future Work We have presented an architecture for ontology-based P2P management of knowledge artifacts and address the process for building knowledge artifacts management, searching, and querying knowledge in P2P. The architecture facilitates achieving autonomy and selforganized management. We are incrementally developing prototypes applied within a case study conducted in our own corporation. The results can be significant since our corporation is a node within a knowledge network linking industry companies with research institutes. Thus, a range of different requirements with respect to knowledge needs and dissemination concerns will be covered by the case study. Based on the prototype and case study results, we will address particular research issues in further work, such as, investigating the P2P management of knowledge artifacts incorporating richer data (e.g. relational, semistructured, XML, meta-data), further addressing issues concerning the P2P management of distributed ontology (e.g. merging, combining, semantic mismatch, searching), and investigating security issues such as trust, privacy, confidentiality, and authenticity in the distributed peer-topeer knowledge environment. 7. Acknowledgements The authors gratefully acknowledge the support of the Kplus program, which is funded by the Austrian Government, the province of Upper Austria and the Chamber of Commerce of Upper Austria. 8. References [1] H. Garcia-Molina, Peer-to-Peer Data Management, Invited Talk at International Conference on Data Engineering, San Jose, CA, USA, February TalkV6.ppt. [2] K. Aberer, and Z. Despotovic, Managing Trust in a Peer-2-Peer Information System, Proc. Of 10 th. Int l CIKM 2001, pp , [3] S. Staab, R. Studer, H.P. Schnurr, and Y. Sure, Processes and Ontologies, IEEE Intelligent Systems, [4] T. Gruber, A translation approach to portable ontology specifications, Acquisition, 5(2), , [5] G. Fischer, and J. Ostwald, Management: Problems, Promises, Realities, and Challenges, IEEE Intelligent Systems, [6] L. Stojanovic, N. Stojanovic, and S. Handschuh, Evolution of the in the Ontology-base Management Systems, FZI, University of Karlsruhe, Experience Management, Berlin [7] L. Kerschberg, Management in Heterogeneous Data Warehouse Environments, DAWAK 2001, [8] C. Axton, R. Gear, N. Macehiter, and E. Woods, Peer-to-Peer Computing: Applications and Infrastructure, An Ovum Report, January [9] E. Woods, Management and Peer-to- Peer Computing: Making connections. Management World, KMWorld, Vol. 10, Issue 9, October [10] M. Koenig, The third stage of KM emerges, KMWorld, Vol. 11, Issue 3, March [11] A.V. Smirnov, and C. Chandra, Ontology-Based Management for Co-operative Supply Chain Configuration, AAAI, [12] VISION Towards Next Generation Management, FZI, University of Karsruhe, Germany. [13] M. Rapport, Microsoft, IBM researchers Develop P2P Networking Technology, Peer-to-Peer Central, [14] HCi Consulting, Management Primer - Part 2, HCi Journal, Sydney, September, (last visited April 9 th, 2002). [15] S. Pepper, and G. Moore, XTM Topic Maps (XTM) 1.0 Last Version, [16] W3C, Activity: Resource Description Framework (RDF). [17] The DARPA Agent Markup Language Homepage, [18] I. Horrocks, D. Fensel, J. Broekstra, S. Decker, M. Erdmann, C. Goble, F. Van Harmelen, M. Klein, S. Staab, R. Studer, and E. Motta, The Ontology Inference Layer OIL. [19] S. Thacker, A. Sheth, and S. Patel, Complex Relationships for the Web, To appear Chapter Manuscript for Creating the Web, D. Fensel, J. Hendler, H. Liebermann, and W. Wahlster (eds.), MIT Press, [20] A. Oram, Peer-to-Peer Harnessing the Benefits of a Disruptive Technology, O Reilly & Associations, Inc.,