Self-Organized Collaborative Knowledge Management

In the first part of the thesis, various aspects of solutions for ontology-based peer-to-peer knowledge management (P2PKM) are presented. We propose a strategy for computing compact self-descriptions of knowledge bases (and thus peers) that can be used for network self-organization. All parts of the diploma thesis, which are not explicitly mentioned below, are the author's original work.

Knowledge Management

Definitions

Knowledge Management Approaches

However, these rather strict KM processes fail to account for the fact that knowledge creation and sharing often occurs in unexpected ways and that the overhead of adhering to strict practices can deter individuals from sharing knowledge in the first place. Ba is a Japanese word that roughly means 'space', but not just in the sense of place and time, but rather as 'a concept that unites physical space such as an office space, virtual space such as email and mental space. space like shared ideals.” (Nonaka et al., 2000). In this thesis, the aim will be to provide systems and thus technical foundations for a ba in which knowledge sharing between individuals in the SECI process can take place efficiently and effectively.

Figure 1.1.: Three elements of the knowledge-creating process (from (Nonaka et al., 2000))

Collaborative KM Beyond Structured Processes

Collective Abilities and Informal Networks

Examples of these tools include Personal Information Managers (PIMs), email applications, mind mapping tools, or searchable office documents on the user's personal computer. In this thesis, we adopt this perspective and focus on two KM paradigms that emphasize the individual's demand for little overhead and little disruption to personal workflow. They use a metaphor of "knowledge markets" (Davenport and Prusak, 1998; p. 25f), in which buyers, sellers and intermediaries interact to distribute knowledge.

The Cathedral and the Bazaar: The Knowledge

Factors such as altruism, trust, reciprocity and reputation are proposed as the price system within these markets. Problems such as these that occur when knowledge must be elicited for use in a KM system have been called the knowledge acquisition (KA) bottleneck (Hayes-Roth et al., 1983). The development cycles are therefore shortened to their absolute minimum, whereas heavyweight processes such as the RUP assume this. after thorough analysis and design phases, a more or less finished software system will be implemented at the end.

Self-Organization and Knowledge Management

In this thesis, we will thus focus on two KM paradigms that encourage the participation of a large number of users in a KM system by reducing the effort required to contribute. In this thesis, we will follow a process-oriented view of KM and propose two types of systems that can increase the possibilities of professionals to share and create knowledge in a collaborative way. We will further observe KM systems through their evolution and analyze their path through the state space.

Contributions of this Thesis

To help the user navigate the folksonomy and generate useful recommendations, we will reduce the folksonomy to two dimensions with different projections and examine the structure of the folksonomy to extract association rules. We also show how the clear results of this rule-based mining step can be extended to fuzzy sets of thematically related tags, users, or resources with the aforementioned classification algorithm.

Structure of this Thesis

In the following, we will specify some of the operations that are necessary for our algorithms. The arrays permσ, where σ is a permutation of the dimension names (U, T, R) (hereafter identified with the. One of the most distinct groupings in the graph is the subgraph in Figure 11.3.

Peer-to-Peer Knowledge Management 13

Peer-to-Peer and the Semantic Web

On the one hand, a sophisticated knowledge management system in a centralized way requires considerable effort to set up and maintain; A certain investment must therefore be made before the first benefits can be reaped. On the other hand, P2P systems running on users' machines can provide immediate rewards by making resources on other users' desktops available to everyone, so that, for example, duplication of work that produces redundant results can be eliminated. Thus, the combination of Semantic Web and P2P technology opens up a feasible way to combine rich knowledge representation formalism on the one hand with low overhead and immediate benefit on the other hand.

Use Cases

Knowledge Sharing within Communities of Interest 17
Large-Scale Knowledge Sharing about People: So-

Online social networks such as Orkut2, LinkedIn3 or Facebook4 are attracting a large number of users who are willing to describe themselves in user profiles in order to discover and be discovered by friends or potential new acquaintances. Similarly, Friend-of-a-Friend (FOAF)5 profile files stored on common web servers are being widely used by people who want to provide machine-interpretable descriptions of themselves to create a social network with friends. , their colleagues. , and relationships. Since these user profiles are inherently associated with a particular person, the logical step would be to have each user's profile on the user's personal laptop or portable device so that the user can obtain the peer "e his" with you, keep your profile high. to date, and have control over who is or isn't allowed to read their profile, rather than uploading it to one or more central servers.

Existing Systems

Edutella and the Courseware Watchdog
Bibster
Conzilla/SHAME
Edamok
DBin

Another semantic P2P application aimed at a particular use case is Bibster (Brookstra et al., 2004), developed in the SWAP8 project. A related application from the same group, SHAME (Naeve et al., 2005), also connects the Edutella network. While the P2P parts of the aforementioned systems follow a similar approach – peers maintain knowledge bases and queries are forwarded between peers to find relevant information – the DBin system (Tummarello et al., 2006) uses a different approach.

Conclusion and Outlook

In order to introduce the relevant concepts and nomenclature, we will give a brief overview of the Semantic Web and related technologies in Chapter 3. In particular, we will define our understanding of ontologies as a core technology for knowledge management P2P semantics and we will show how ontologies can be used to measure the similarity of entities in the P2PKM system. Next, we will focus on the other two aforementioned problems within P2PKM applications. To build P2PKM applications, we will use technologies that are used in an extension of the current web called the Semantic Web.

The Layer Cake

Describing resources here means asserting statements of the form (subject, predicate, object) about resources, in which subject and predicate are themselves resources, and object can be either a resource or a literal value. Ontology Vocabulary: The ontology layer of the Semantic Web Layer Cake consists of three different variants of the Web Ontology Language (OWL). The remaining layers of the Semantic Web layer cake will not be needed in this thesis; we describe them briefly for completeness.

Ontologies

Note that in the rest of this thesis we will limit our use of the term "ontology" to the definition of Section 3.3, which is more similar to the capabilities provided by the RDF Schema language and does not make use of advanced OWL constructs. Briefly, a core ontology consists of a partially ordered set of concepts1, where the partial order is the "sub-concept", and relations1 between these concepts. A knowledge base or OIModel (for ontology-instance model) consists of a core ontology plus instances of the concepts and relationships; e.

Metrics on Ontology Entities

Metric Used in this Thesis
Similarity, Relatedness, and Semantic Distances—
Caveats and Pitfalls on Real-World Ontologies
Obtaining of Proper Parameters

In the next four sections, we describe specific Courseware Watchdog modules in more detail. As shown in the scenario, user interaction with the ontology is central to all ontology-based tools. An instance of Courseware Watchdog can also act as an information provider on the Edutella network.

The Courseware Watchdog: A P2PKM Application 33

E-Learning in the Semantic Web
Use Case and User Requirements

Usage Scenarios
User Requirements

The Courseware Watchdog

Overview
The Courseware Watchdog and the KAON Frame-

The User Interface: Browsing the Watchdog Data

Displaying an Ontology
Interacting with the Ontology

Retrieval Components: Focused Crawler and Edutella

Focused Crawler
Integrating the Edutella Peer–to–Peer Network . 47
Subjective Clustering
Ontology Evolution

Related Work
Conclusion and Outlook

Self-Organized Network Topologies for P2PKM 57

Basics and Definitions

Model of the P2P network
Characteristic Path Length

Rewiring and Routing Algorithms

Rewiring Algorithms

Evaluation

Setting
Clustering Coefficients
The Influence of Clustering on Recall and Network
Clustering Too Much

Related Work

Conclusion
Outlook and Future Work

Semantic Summarization of Knowledge Bases for P2PKM 75

Preliminaries and Definitions

Model of a Semantic Peer-to-Peer Network
Shared and Personal Parts of the Knowledge Bases 76

Graph Clustering for Content Aggregation

Clustering the Knowledge Base
Determining the number of centroids

Experimental Evaluation

Setup
Expertise Extraction Strategies
Results

Related Work

Conclusion
Outlook and Work in Progress

Knowledge Management in Folksonomies 89

Folksonomies and Ontologies

Since Part I of this thesis deals with P2P applications using Semantic Web technology, including ontologies, and this part deals with folksonomies, we will briefly discuss the differences and similarities of folksonomies on the one hand with ontologies as contemplated by the Semantic Web community (cf. section 3.3), on the other hand. According to him, the purpose of ontologies is to determine the "correct" set of concepts to describe the world, as well as the "correct" concept for each resource to be described. In the same vein, Gendarmi et al. 2007) call traditional ontology engineering procedures "elitist approaches" as they rely on the presence of an omniscient ontology engineer who can anticipate all possible applications of the ontology to be designed.

In what he calls "folksological", he proposes that folksonomies in their current form be described using Semantic Web languages such as OWL (cf. Section 3.2). By asserting in an OWL statement that A's apple is different from B's, the user can contribute to a clean-up phase that will help ethnography to move from a folksonomy to something resembling an ontology in the stricter sense , to converge. 1See Table 11.14 in Section 11.2 for an indication that Shirky's essay is indeed popular in the Semantic Web community.

He applies mining algorithms and network analysis to the folksonomy structure and automatically infers connections between tags or between tags and resources ("concepts" and "instances" . in his terminology). Regarding the popularity of folksonomy-based KM solutions compared to more formal, RDF- or ontology-based solutions, Halpin (2006) notes that when discussing the semantics of RDF in the W3C, "[i]. For the purpose with this thesis it will be sufficient to consider the concepts of ontology and folksonomy as defined in the respective chapters.

However, it will be interesting to see how these two concepts will interact and influence each other, and whether there will be a unified view of both in the future that combines the characteristics of both.

Classification of Folksonomy Systems

While the W3C continued not to address the slippery concept of social meaning, social software gained momentum", and further, regarding folksonomies, "Although it is unclear whether such a technique can be subordinated to [the] logic-based Semantic Web or [is] a cheap alternative to the Semantic Web, it seems that 'tagging' is here to stay due to its large rolled out user base”. Resource connectivity: Are there links between resources, such as between web pages in a social bookmarking system. The social bookmarking systems we will consider in the following chapters (see also the "social bookmarking" use case in Section 7.2.2) are all of the same type with respect to these dimensions.

They allow tagging by everyone, make tag suggestions, and count multiple tags of the same source. Tagable resources are web pages (or, more precisely, anything with a URI2), so there are usually links between resources in the form of hyperlinks.

Folksonomies and their Applications

Advantages
Problems
Solutions Discussed in this Thesis

Note that each of the three hyperends has exactly one endpoint in the three sets U, T, R. The folksonomy graphs considered in this part of the thesis are 3-uniform, tripartite hypergraphs (Berge, 1989), i.e. For a comprehensive review of the most relevant results in that area, refer to (Newman et al., 2006).

In this paper, we analyzed the network structure of the folksonomies of two social resource sharing systems, del.icio.us and BibSonomy. This roughly corresponds to the tag rank given by the total number of tags in the dataset. The classification of resources for the tag “boomerang” given in Table 11.8 also offers interesting insights.

In the following, we will discuss some of the clusters in the graph of association rules depicted in Figure 11.2. The FolkRank algorithm makes use of the usual PageRank algorithm introduced by Page et al. In the following, we will highlight some of the upcoming research areas and point to possible future research paths.

In the remainder of this section, we will present some of the current work at the intersection of Web 2.0 and the Semantic Web.