Abstract

Fuzzy sets, having been the long-standing mainstay of modeling and manipulating imperfect information, are an obvious candidate for representing uncertain beliefs.

Unfortunately, unadorned fuzzy sets are too limited to capture complex or potentially inconsistent beliefs, because all too often they reduce to absurdities ("nothing is possible") or trivialities ("everything is possible").

However, we show that by combining the syntax of propositional logic with the semantics of fuzzy sets a rich framework for expressing and manipulating uncertain beliefs can be created, admitting Gärdenfors-style expansion, revision, and contraction operators and being moreover amenable to easy integration with conventional ``crisp'' information processing.

The model presented here addresses many of the shortcomings of traditional approaches for building fuzzy data models, which will hopefully lead to a wider adoptance of fuzzy technologies for the creation of information systems.

Keywords

fuzzy belief revision, fuzzy information systems, soft computing, fuzzy object-oriented data model

Abstract

We present a system that produces 10-word summaries based on the single summarization strategy of outputting noun phrases representing the most important text entities (as represented by noun phrase coreference chains). The coreference chains were computed using fuzzy set theory combined with knowledge-poor corefernce heuristics.

Abstract

We present a fuzzy-theory based approach to coreference resolution and its application to text summarization.

Automatic determination of coreference between noun phrases is fraught with uncertainty. We show how fuzzy sets can be used to design a new coreference algorithm which captures this uncertainty in an explicit way and allows us to define varying degrees of coreference.

The algorithm is evaluated within a system that participated in the 10-word summary task of the DUC 2003 competition.

Abstract

Software reverse engineering (RE) is often hindered not by the lack of available data, but by an overabundance of it: the (semi-)automatic analysis of static and dynamic code information, data, and documentation results in a huge heap of often incomparable data. Additionally, the gathered information is typically fraught with various kinds of imperfections, for example conflicting information found in software documentation vs. program code.

Our approach to this problem is twofold: for the management of the diverse RE results we propose the use of a repository, which supports an iterative and incremental discovery process under the aid of a reverse engineer. To deal with imperfections, we propose to enhance the repository model with additional representation and processing capabilities based on fuzzy set theory and fuzzy belief revision.

Keywords

fuzzy reverse engineering, meta model, extension framework, iterative process, knowledge evolution

Abstract

Last year, we presented a system, ERSS, which constructed 10 word summaries in form of a list of noun phrases. It was based on a knowledge-poor extraction of noun phrase coreference chains implemented on a fuzzy set theoretic base. This year we present the performance of an improved version, ERSS 2004 and an extension of the same basic system: Multi-ERSS constructs 100-word extract summaries for clusters of texts. With very few modifications we ran ERSS 2004 on Tasks 1 and 3 and Multi-ERSS on Tasks 2, 4, and 5, scoring generally above average in all but the linguistic quality aspects.

Abstract

The different stages in the life-cycle of content—creation, storage, retrieval, and analysis—are usually regarded as distinct and isolated steps. In this paper we examine the synergies resulting from their integration within a single architecture.

Our goal is to employ such an architecture to improve user support for knowledge-intensive tasks. We present a case study from the area of building architecture, which is currently ongoing.

Abstract

On first glance agent technology seems more like a hostile intruder into the database world. On the other hand, the two could easily complement each other, since agents carry out information processes whereas databases supply information to processes. Nonetheless, to view agent technology from a database perspective seems to question some of the basic paradigms of database technology, particularly the premise of semantic consistency of a database. The paper argues that the ensuing uncertainty in distributed databases can be modelled by beliefs, and develops the basic concepts for adjusting peer-to-peer databases to the individual beliefs in single nodes and collective beliefs in the entire distributed database.

Abstract

We present ERSS 2005, our entry to this year's DUC competition. With only slight modifications from last year's version to accommodate the more complex context information present in DUC 2005, we achieved a similar performance to last year's entry, ranking roughly in the upper third when examining the ROUGE-1 and Basic Element score.

We also participated in the additional manual evaluation based on the new Pyramid method and performed further evaluations based on the Basic Elements method and the automatic generation of Pyramids. Interestingly, the ranking of our system differs greatly between the different measures; we attempt to analyse this effect based on correlations between the different results using the Spearman coefficient.

Abstract

Constructing focused, context-based multi-document summaries requires an analysis of the context questions, as well as their corresponding document sets. We present a fuzzy cluster graph algorithm that finds entities and their connections between context and documents based on fuzzy coreference chains and describe the design and implementation of the ERSS summarizer implementing these ideas.

Abstract

Despite impressive advances in the development of generic NLP frameworks, content-specific text mining algorithms, and NLP services, little progress has been made in enhancing existing end-user clients with text analysis capabilities. To overcome this software engineering gap between desktop environments and text analysis frameworks, we developed an open service-oriented architecture, based on Semantic Web ontologies and W3C Web services, which makes it possible to easily integrate any NLP service into client applications.