Congenial Web Search : A Conceptual Framework for Personalized, Collaborative, and Social Peer-to-Peer Retrieval

Traditional information retrieval methods fail to address the fact that information consumption and production are social activities. Most Web search engines do not consider the social-cultural environment of users' information needs and the collaboration between users. This dissertation addresses a new search paradigm for Web information retrieval denoted as Congenial Web Search. It emphasizes personalization, collaboration, and socialization methods in order to improve effectiveness. The client-server architecture of Web search engines only allows the consumption of information. A peer-to-peer system architecture has been developed in this research to improve information seeking. Each user is involved in an interactive process to produce meta-information. Based on a personalization strategy on each peer, the user is supported to give explicit feedback for relevant documents. His information need is expressed by a query that is stored in a Peer Search Memory. On one hand, query-document associations are incorporated in a personalized ranking method for repeated information needs. The performance is shown in a known-item retrieval setting. On the other hand, explicit feedback of each user is useful to discover collaborative information needs. A new method for a controlled grouping of query terms, links, and users was developed to maintain Virtual Knowledge Communities. The quality of this grouping represents the effectiveness of grouped terms and links. Both strategies, personalization and collaboration, tackle the problem of a missing socialization among searchers. Finally, a concept for integrated information seeking was developed. This incorporates an integrated representation to improve effectiveness of information retrieval and information filtering. An integrated information retrieval process explores a virtual search network of Peer Search Memories in order to accomplish a reputation-based ranking. In addition, the community structure is considered by an integrated information filtering process. Both concepts have been evaluated and shown to have a better performance than traditional techniques. The methods presented in this dissertation offer the potential towards more transparency, and control of Web search

Natural Language Interface to Knowledge Management Systems

Abstract: In this paper, we present linguistic techniques required for natural language driven knowledge management interfaces. We describe two significant aspects of such an interface: First, how the user input is handled to provide an unrestricted natural language user interface, and second, how the gathered knowledge should be preprocessed, classified and thus prepared for a natural language interactive retrieval. A framework of grammatical structures (supertags) is associated with the elements of an ontology (of the respective domain). This combination of ontologies and supertagging describes a novel and very robust way in parsing of nontrivial user utterances and allows natural language feedback generation.

Modeling a Corporate Information System to improve Knowledge Management

Abstract. Knowledge Management Systems (KMSs) are of increasing interest in economy to collect, store and re–use company–relevant knowledge as a resource to gain productivity. In this paper we demonstrate how to leverage structured information for its efficient re–use. We propose the development of ontologies to represent the knowledge to be stored. As it is anyhow the case that in a KMS knowledge has to be fostered the step towards a systematic structuring is a solution which suggests itself. In the paper we outline our experience with the development of an ontological model of the KMS of the IT company sd&m (called K–WEB) in view of a restructuring process of the whole system. On the basis of the implemented ontology we illustrate how frequently asked questions in the company which are difficult to be answered by the currently deployed full–text retrieval–system (for instance, experienced colleagues in a specific programming language can only be found if the currently considered list of relevant documents can be classified as the employee’s home page due to the fact that only on this page a pointer into the skills hierarchy is provided in the K–WEB whereas a full text retrieval system can neither regard the document type nor the physical pointer) become simple in a structural representation.

Beyond the Web: Retrieval in Social Information Spaces

We research whether the inclusion of information about an information user’s social environment and his position in the social network of his peers leads to an improval in search effectiveness. Traditional information retrieval methods fail to address the fact that information production and consumption are social activities. We ameliorate this problem by extending the domain model of information retrieval to include social networks. We describe a technique for information retrieval in such an enviroment and evaluate it in comparison to vector space retrieval

Towards virtual knowledge communities in peer-to-peer networks

Abstract. As a result of the anonymity in todays Web search, it is not possible to receive a personalized search result. Neither prior search results nor search results from other users are taken into consideration. In order to resolve this anonymity towards the search engine, a system is created which locally stores the search results in the scope of a peerto-peer network. Using the Peer Search Memory (PeerSy) all relevant results are stored and associated with the corresponding queries. By this means, repeated access is facilitated. Furthermore, sharing of associations between queries and relevant results in the peer-to-peer network allows grouping of Virtual Knowledge Communities (VKC) in order to obtain a surplus value in knowledge sharing on the Web.

Cooperative Pull-Push Cycle for Searching a Hybrid P2P Network

Information acquisition is a great challenge in the context of a continually growing Web. Nowadays, large Web search engines are primarily designed to assist an information pull by the user. On this platform, only actual information needs are handled without assistance of long-term needs. To overcome these shortcomings we propose a cooperative system for information pull and push on a peer-to-peer architecture. In this paper we present a hybrid network for a collaborative search environment, based on a local personalization strategy on each peer, and a highly-available Web search service (e.g. Google). Each peer participates in the Pull-Push Cycle, and has the function of an information consumer as well as an information provider. Hence, long-term information needs can be identified without any context restrictions, and recommendations are computed based on Virtual Knowledge Communities