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.

Abstract. This paper presents an algorithm, called In-Close, that uses incremental closure and matrix searching to quickly compute all formal concepts in a formal context. In-Close is based, conceptually, on a well known algorithm called Close-By-One. The serial version of a recently published algorithm (Krajca, 2008) was shown to be in the order of 100 times faster than several well-known algorithms, and timings of other algorithms in reviews suggest that none of them are faster than Krajca. This paper compares In-Close to Krajca, discussing computational methods, data requirements and memory considerations. From experiments using several public data sets and random data, this paper shows that In-Close is in the order of 20 times faster than Krajca. In-Close is small, straightforward, requires no matrix pre-processing and is simple to implement. 1

We present an innovative approach to information retrieval for domain-specific digital library collections. We use a combination of Formal Concept Analysis (FCA) and a notion of information anchors to facilitate information delivery to the end user. This approach (1) uses ranked objects in attribute concepts to facilitate topical queries for experts and expertise profiles; (2) formulates (keyword by keyword) context for concept lattice construction via a set of heuristics, including those based on information anchors for selecting descriptive phrases, (3) bootstraps the learning of domain-specific concept hierarchies using FCA, and (4) incorporates the learnt concept hierarchies and WordNet for content-based document classification. To demonstrate the feasibility and utility of this approach, we implemented a prototype online information retrieval systemmemsworldonline.case.edu (MWOL) for the emerging engineering discipline of MEMS (microelectromechanical systems) incorporating these ideas. MWOL has been actively used by a non-trivial group of MEMS practitioners; all user queries are processed in a fraction of a second as a result of inverse indexing strategy using Berkeley DB. Voluntary user feedback using online forms has been encouraging. However, no other systems with similar features are available for a comparative study at this point.

Abstract. This paper discusses how facet-like structures occur as a commonplace feature in a variety of computer science disciplines as a means for structuring class hierarchies. The paper then focuses on a mathematical model for facets (and class hierarchies in general), called formal concept analysis, and discusses graphical representations of faceted systems based on this model.

We propose a novel application of Formal Concept Analysis (FCA) to neural decoding: instead of just trying to figure out which stimulus was presented, we demonstrate how to explore the semantic relationships in the neural representation of large sets of stimuli. FCA provides a way of displaying and interpreting such relationships via concept lattices. We explore the effects of neural code sparsity on the lattice. We then analyze neurophysiological data from high-level visual cortical area STSa, using an exact Bayesian approach to construct the formal context needed by FCA. Prominent features of the resulting concept lattices are discussed, including hierarchical face representation and indications for a product-of-experts code in real neurons. 1

Abstract. The design and construction of navigation menus for websites have traditionally been performed manually according to the intuition of a web developer. This paper introduces a new approach, FcAWN (pronounced “fawn”) – Formal concept Analysis for Web Navigation – to assist in the design and generation of a coherent and logical navigation hierarchy for a set of web documents. We provide an algorithmic process for generating multi-layered menu models using FcAWN and demonstrate its feasibility with an experimental case study. Our study reveals a fundamental difference between the traditional tree-based menu structure and the lattice-based menu structure by FcAWN: a FcAWN-generated lattice structure is more general than a tree structure and yet is mathematically sound and uniquely suited for menu design and construction. FcAWN is the first mathematical principle for menu design and generation, providing a practical basis for human-computer interaction. 1

This new paradigm defines security policies on cause-effect relations and models security mechanisms in analogy with pattern recognition classifiers. It augments the arsenal of formal computer security evaluation tools with new techniques. A causality model represents possible causes and effects; the causes include threats and the effects may be undesired. Target security policies derived from functional specifications select permitted causalities. Security mechanisms extract features from causes and effects and enforce mechanism-specific policies, approximating the target policy. Advantages of the classifier paradigm are the ability to generalize from incomplete information and examples, to measure classification error and mechanism performance, and to analyze mechanism ensembles and compositions. The classifier paradigm also offers a conception of problem complexity and suggests paying more attention to the impact of mechanisms rather than to their inner workings.

MontyLingua, an integral part of ConceptNet which is currently the largest commonsense knowledge base, is an English text processor developed using Python programming language in MIT Media Lab. The main feature of MontyLingua is the coverage for all aspects of English text processing from raw input text to semantic meanings and summary generation, yet each component in MontyLingua is loosely-coupled to each other at the architectural and code level, which enabled individual components to be used independently or substituted. However, there has been no review exploring the role of MontyLingua in recent research work utilizing it. This paper aims to review the use of and roles played by MontyLingua and its components in research work published in 19 articles between October 2004 and August 2006. We had observed a diversified use of MontyLingua in many different areas, both generic and domainspecific. Although the use of text summarizing component had not been observe, we are optimistic that it will have a crucial role in managing the current trend of information overload in future research.

Abstract. This paper proposes a novel application of Formal Concept Analysis (FCA) to neural decoding: the semantic relationships between the neural representations of large sets of stimuli are explored using concept lattices. In particular, the effects of neural code sparsity are modelled using the lattices. An exact Bayesian approach is employed to construct the formal context needed by FCA. This method is explained using an example of neurophysiological data from the high-level visual cortical area STSa. Prominent features of the resulting concept lattices are discussed, including indications for a product-of-experts code in real neurons. 1

Abstract. This paper discusses the application of concept neighbourhoods (in the sense of Formal Concept Analysis) to Knowledge Organisation Systems. Examples are provided using Roget’s Thesaurus, WordNet and Wikipedia categories. 1