International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

This paper proposes an indexing procedure for improving the performance of query processing on a fuzzy database. It focuses on the case when a necessity-measured atomic flexible condition is imposed on the values of a fuzzy numerical attribute. The proposal is to apply a classical indexing structure for numerical crisp data, a B⁺-tree combined with a Hilbert curve. The use of such a common indexing technique makes its incorporation into current systems straightforward. The efficiency of the proposal is compared with that of another indexing procedure for similar fuzzy data and flexible query types. Experimental results reveal that the performance of the proposed method is similar and more stable than that of its competitor.

In this paper a model of soft aggregation of preferences in flexible database querying is proposed, based on the vector p-norm operator. The model allows aggregating conditions with distinct importance by modeling both veto and favour semantics of the conditions. We outline how the semantics of the compound query varies for increasing values of the parameter p, when the selection conditions are ANDed and ORed.

Relevant contributions of fuzzy logic to the logical models in information retrieval is studied. It makes it possible to grasp the graduality of some relevant concepts and to model both imprecision and uncertainty inherent to the retrieval process, still in the framework of the broadly meant logical approach. In this perspective we discuss various extensions to the basic Boolean model which are needed to attain such a greater expressivity. In particular, we show how the well-known semantics of keywords weights may be recovered in various fuzzy logic based information retrieval models.

Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and defining new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by evolution of trends in topics extracted from text streams. In this paper, we are describing a three-layered approach called the "user-system-content method" that is helping us to identify the most relevant knowledge mapping features derived from the USER, SYSTEM and CONTENT perspectives into an overall "context model", that will enable the advanced KBS to automatically streamline the query enrichment process in a much more user-centered, dynamical and flexible way. After a general introduction to our three-layered approach, we will describe into detail the necessary process steps for the implementation of our method and will present a case study for its integration on a real multimedia web-content portal using news streams as major source of unstructured information.

The paper presents a preliminary investigation of potential methods for extracting semantic views of text contents under the form of structured sets of words, which go beyond standard statistical indexing. The aim is to build kinds of fuzzily weighted structured images of semantic contents. A preliminary step consists in identifying the different types of relations (is-a, part-of, related-to, synonymy, domain, glossary relations) that exist between the words of a text, using some general ontology such as WordNet. Then taking advantage of these relations, different types of fuzzy clusters of words can be built. Moreover, apart from its frequency of occurrence, the importance of a word may be also evaluated through some estimate of its specificity. A degree of "centrality" is also computed for each word in a cluster. The size of the clusters, the frequency, the specificity and the centrality of their words are indications that enable us to build a fuzzy set of sets of words that progressively "emerge" from a text, as being representative of its contents. The ideas advocated in the paper and their potential usefulness are illustrated on a running example and on two experiments. It is expected that obtaining a better representation of the semantic contents of texts may help in particular to give indications of what the text is about to a potential reader.

Sequential pattern mining is the method that has received much attention in sequence data mining research and applications, however, a drawback is that it does not profit from prior knowledge of domains. In our previous work, we proposed a belief-driven method with fuzzy set theory for discovering the unexpected sequences that contradict existing knowledge of data, including occurrence constraints and semantic contradictions. In this paper, we present a new approach that discovers unexpected sequences with determining semantic contradictions by using concept hierarchies associated with the data. We evaluate the effectiveness of our approach with experiments on Web usage analysis.

The size of multimedia data is increasing fast due to the abundance of multimedia applications. Modeling the semantics of the data effectively is crucial for proper management of it. In this paper, we present a fuzzy conceptual data model for multimedia data which is also generic in the sense that it can be adapted to all multimedia domains. The model takes an object-oriented approach and it handles fuzziness at different representation levels where fuzziness is inherent in multimedia applications and should be properly modeled. The proposed model also has the nice feature of representing the structural hierarchy of multimedia data as well as the spatial and temporal relations of the data. The model is applied to the news video domain and implemented as a fuzzy multimedia database system where it turns out to be effective in representing the domain and thereby provides an evidence for the general applicability of the model. The model is accompanied by an automatic multimedia annotation scheme which makes use of information extraction techniques on the corresponding multimedia texts.

The comparison of sets of objects is a research topic with applications in diverse fields such as computer science, biology and psychology. Since the introduction of the Jaccard index, many techniques have been proposed. This paper aims at extending an existing framework of comparison indices for sets. Firstly, the novel indices account for similarities between elements, rather than identity of elements as is the case for existing techniques. As a result, a richer framework of comparison indices is obtained. The use of fuzzy quantifiers in this framework is shown. Secondly, the machinery for sets is extended to the case of multisets, which results in two classes of comparison indices. The first class considers each element instance as a separate element, while the second class considers groups of elements instances as an atomic entity. The number of instances is then a property of this group, that is taken into account when calculating similarity between element groups.

Temporal databases offer a common framework to those database applications that need to store or handle different types of temporal data from a variety of sources. They allow the concept of time to be handled from the point of view of meaning, representation, and manipulation. Although at first sight the incorporation of time into a database might appear to be a direct and simple task, it is, however, quite complex: not only must new structures and specific operators be included, but the semantics of conventional DML sentences (insert, update, or delete) and queries must be appropriately changed. In addition, temporal information is not always as precise as desired since it might be affected by imprecision due to the use of natural language or to the nature of the information source. In this paper, we deal with the problem of the update (and, implicitly, insert and delete) and query operations when time is expressed by means of a fuzzy interval of dates.