Abstract not found

The recent advances in Formal Concept Analysis (FCA) together with the major changes faced by modern Information Retrieval (IR) provide new unprecedented challenges and opportunities for FCA-based IR applications. The main advantage of FCA for IR is the possibility of creating a conceptual representation of a given document collection in the form of a document lattice, which may be used both to improve the retrieval of specific items and to drive the mining of the collection’s contents. In this paper, we will examine the best features of FCA for solving IR tasks that could not be easily addressed by conventional systems, as well as the most critical aspects for building FCA-based IR applications. These observations have led to the development of CREDO, a system that allows the user to query Web documents and see retrieval results organized in a browsable concept lattice. This is the second major focus of the paper. We will show that CREDO is especially useful for quickly locating the documents corresponding to the meaning of interest among those retrieved in response to an ambiguous query, or for mining the contents of the documents that reference a given entity. An on-line version of the system is available for testing at

In this paper we consider methods for automatic query expansion from top retrieved documents (i.e., retrieval feedback) which make use of various functions for scoring expansion terms within Rocchio’s classical reweighting scheme. An analytical comparison shows that the retrieval performance of methods based on distinct term-scoring functions is comparable on the whole query set but considerably differs on single queries, consistent with the fact that the ordered sets of expansion terms suggested for each query by the different functions are largely uncorrelated. Motivated by these findings, we argue that the results of multiple functions can be merged, by analogy with ensembling classifiers, and present a simple combination technique based on the rank values of the suggested terms. The combined retrieval feedback method is effective not only with respect to unexpanded queries but also to any individual method, with notable improvements on the system’s precision. Furthermore, the combined method is robust with respect to variation of experimental parameters and it is beneficial even when the same information needs are expressed with shorter queries.

Formal Concept Analysis (FCA) has been applied to the task of document retrieval in many different ways. In this paper we present a new document management tool, based on FCA and aimed at facilitating the retrieval of documents and the understanding of the structure of collections of standard documents such as PDF, HTML or Word files. The user interface is designed to allow easy access to the tool without prior knowledge of FCA. 1

Abstract. This paper extends standard help system technology to demonstrate the suitability of Formal Concept Analysis in displaying, searching and navigating help content. The paper introduces a method for building suitable scales directly from the help system index by computing a keyword extension set. The keyword extension technique is generalisable in any document collection where a hand-crafted index of terms is available. 1

Abstract. One of the major challenges in the post genomic era consists in exploiting the vast amounts of biological data stored in the numerous heterogeneous biological databases distributed worldwide. Most research projects in bioinformatics start with data retrieval from selected sources. However, identifying appropriate data sources is not trivial and requires the representation of the knowledge about data sources. We present here the BioRegistry project which aims at providing means to represent and exploit knowledge associated with biological databases. As a first step, a repository structure has been designed to organise metadata associated with databases consisting of five metadata categories: database identification, topics covered, quality information, access/availability, and tracking of the metadata. The BioRegistry model and its relationships with the DCMI (Dublin Core Metadata Initiative) are described. Prototypes with various functionalities to feed, maintain and exploit the repository are presented. 1

Abstract. This paper introduces a framework for relational schema navigation via a Web-based browser application that uses Formal Concept Analysis as the metaphor for analysis and interaction. Formal Concept Analysis is a rich framework for data analysis based on applied lattice and order theory. The application we develop, D-SIFT, is intended to provide users untrained in Formal Concept Analysis with practical and intuitive access to the core functionality of Formal Concept Analysis for the purpose of exploration of relational database schema. D-SIFT is an information systems architecture that supports natural search processes over a predefined database schema and its attribute values. This enables the user to build concept lattices interactively through the selection and refinement of dynamic definitions of search boundaries, (via interaction with an object “zoom ” feature), and dynamic selection of search scales, (via interaction with an attribute “filter ” feature), based on the attribute values contained within the database. In detail, the paper presents the architecture of the D-SIFT browser and illustrates the resulting D-SIFT-systems on example database. The two examples presented illustrate the generality of system integration outcomes from D-SIFT to schema browsing using Formal Concept Analysis. The Conceptual Information Systems that result from applying the D-SIFT architecture present a new workflow for building and interacting with Formal Concept Analysis-based information systems. This workflow more closely aligns with dynamic schema interaction an increasingly popular technique used in conceptual modeling and analysis.

Abstract—In this paper, a model for an information retrieval system is proposed which takes into account that knowledge about documents and information need of users are dynamic. Two methods are combined, one qualitative or symbolic and the other quantitative or numeric, which are deemed suitable for many clustering contexts, data analysis, concept exploring and knowledge discovery. These two methods may be classified as inductive learning techniques. In this model, they are introduced to build “long term ” knowledge about past queries and concepts in a collection of documents. The “long term ” knowledge can guide and assist the user to formulate an initial query and can be exploited in the process of retrieving relevant information. The different kinds of knowledge are organized in different points of view. This may be considered an enrichment of the exploration level which is coherent with the concept of document/query structure.

Formal Concept Analysis is a technique based on lattice theory that is well suited for document retrieval. The structure of the concept lattice created maps to a query in a natural way and refinements can be found easily. These refinements are ensured to be smaller but never empty. From the lattice structure a ranking on the documents can be found to give results that are related but do not lexically match.

Abstract not found