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Web searches from mobile devices such as PDAs and cell phones are becoming increasingly popular. However, the traditional list-based search interface paradigm does not scale well to mobile devices due to their inherent limitations. In this article, we investigate the application of search results clustering, used with some success for desktop computer searches, to the mobile scenario. Building on CREDO (Conceptual Reorganization of Documents), a Web clustering engine based on concept lattices, we present its mobile versions Credino and SmartCREDO, for PDAs and cell phones, respectively. Next, we evaluate the retrieval performance of the three prototype systems. We measure the effectiveness of their clustered results compared to a ranked list of results on a subtopic retrieval task, by means of the device-independent notion of subtopic reach time together with a reusable test collection built from Wikipedia ambiguous entries. Then, we make a crosscomparison of methods (i.e., clustering and ranked list) and devices (i.e., desktop, PDA, and cell phone), using an interactive information-finding task performed by external participants. The main finding is that clustering engines are a viable complementary approach to plain search engines both for desktop and mobile searches especially, but not only, for multitopic informational queries.

This paper introduces a tool for Conceptual Knowledge Processing with Google. The featured prototype, called FooCA, tries to combine the advantages of two research disciplines, Web Mining and Formal Concept Analysis (FCA).

When building a service-oriented system, a service integrator retrieves a set of potentially useful services from registries and then inspects their documentation to eventually decide which services to use. This task needs to be supported by proper tools that help service interface/documentation understanding, highlighting the relationships existing between the retrieved services. This paper proposes an approach, based on Formal Concept Analysis, to understand relationships between services, as well as between operations of a complex service, by analyzing service interfaces and documentation. The approach allows to cluster similar services, highlights hierarchical relationships and, in general, commonalities and differences between services. To support the proposed approach, we developed a tool that provides several service browsing capabilities. Finally, the approach has been evaluated with different case studies built upon real sets of services.

Abstract. This paper presents SearchSleuth, a program developed to experiment with a form of automatic local analysis that extends the standard Web search interface to include a conceptual neighbourhood focused on a formal concept derived from the query. The conceptual neighbourhood is displayed with upper neighbours representative of a generalisation operation, and lower neighbours representative of a specialisation operation. SearchSleuth also introduces a notion of a categorisation operation, where the conceptual focus can shift to a sibling concept of the search concept. 1

This paper presents a new approach to accessing information on the Web. FooCA, an application in the field of Conceptual Knowledge Processing, is introduced to support a holistic representation of today’s standard sequential Web search engine retrieval results. FooCA uses the itemset consisting of the title, a short description, and the URL to build a context and the appropriate concept hierarchy. In order to generate a nicely arranged concept hierarchy using line diagrams to retrieve and analyze the data, the prior context can be iteratively explored and enhanced. The combination of Web Mining techniques and Formal Concept Analysis (FCA) with contextual attribute elicitation gives the user more insight and more options than a traditional search engine interface. Besides serving as a tool for holistic data exploration, FooCA also enables the regular user to learn step by step how to run new, optimized search queries for his personal information need on the Web.

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Abstract. SearchSleuth is a program developed to experiment with the automated local analysis of Web search using formal concept analysis. SearchSleuth extends a standard search interface to include a conceptual neighborhood centered on a formal concept derived from the initial query. This neighborhood of the concept derived from the search terms is decorated with its upper and lower neighbors representing more general and specialized concepts respectively. In SearchSleuth, the notion of related categories – which are themselves formal concepts – is also introduced. This allows the retrieval focus to shift to a new formal concept called a sibling. This movement across the concept lattice needs to relate one formal concept to another in a principled way. This paper presents the issues concerning exploring and ordering the space of related categories. 1

Abstract. We present a novel method for clustering Web search results based on Word Sense Induction. First, we acquire the meanings of a query by means of a graph-based clustering algorithm that calculates the maximum spanning tree of the co-occurrence graph of the query. Then we cluster the search results based on their semantic similarity to the induced word senses. We show that our approach improves classical search result clustering methods in terms of both clustering quality and degree of diversification. 1

Abstract: Structuring huge documents with a high speed is still a challenge. In this paper, we propose a heuristic based on pseudo-concepts to derive a tree of words reflecting in decreasing "importance " order the semantic macro-structure of the space of documents or the micro-structure of a document. Both macro and micro structures are used to browse inside the space of documents. The advantage of the proposed methods with respect to previous ones using exact formal concepts [2,4,11], is that by only selecting approximate formal concepts associated to the different pairs of a binary relation linking documents or sentences inside a document to indexing words, we improve the structuring process in terms of time complexity while keeping acceptable meaning of generated text structure. Experimentation [12] realized with documents with big size showed that response time of the structuring system as well as the browsing trees are very helpful for users to get the global structured view of the space of documents and the detailed view inside a selected document. Starting from an already created conceptual meta-search engine merging Google and Yahoo search results [4,11], we now have a way to compile more web pages in much shorter time.