Giorgos Akrivas

Context-sensitive semantic query expansion

Modern information retrieval systems match the terms included in a users query with available documents, through the use of an index. A fuzzy thesaurus is used to enrich the query with associated terms. In this work, we use semantic entities, rather than terms; this allows us to use knowledge stored in a semantic encyclopedia, specifically the ordering relations, in order to perform a semantic expansion of the query. The process of query expansion takes into account the query context, which is defined as a fuzzy set of semantic entities. Furthermore, we integrate our approach with the users profile.

Semantic association of multimedia document descriptions through fuzzy relational algebra and fuzzy reasoning

According to the emerging MPEG-7 standard, the semantic description of multimedia documents is expressed in terms of semantic entities such as objects, events, concepts, and relations among them. The semantic entities can be used as index terms, in order to support the semantic search process. In this paper, we propose a method that a) applies fuzzy relational operations (closure, composition) and fuzzy rules to expand a semantic encyclopedia and b) uses the encyclopedia to associate the semantic entities with the aid of a fuzzy thesaurus. This method is shown to reduce the need for human intervention in creating semantic descriptions of multimedia documents, as well as correct for incompleteness and inconsistency.

Automatic thematic categorization of documents using a fuzzy taxonomy and fuzzy hierarchical clustering

In this paper we formally define the problem of automatic detection of thematic categories in a semantically indexed document, and identify the main obstacles to overcome in this process. Furthermore, we explain how detection of thematic categories can be achieved, with the use of a fuzzy quasi-taxonomic relation. Our approach relies on a fuzzy hierarchical clustering algorithm; this algorithm uses a similarity measure that is based on the notion of context.

MPEG-4 authoring tool using moving object segmentation and tracking in video shots

An Authoring tool for the MPEG-4 multimedia standard integrated with image sequence analysis algorithms is described. MPEG-4 offers numerous capabilities and is expected to be the future standard for multimedia applications. However, the implementation of these capabilities requires a complex authoring process, employing many different competencies from image sequence analysis and encoding of audio/visual/BIFS to the implementation of different delivery scenarios: local access on CD/DVD-ROM, Internet, or broadcast. However powerful the technologies underlying multimedia computing are, the success of these systems depends on their ease of authoring. In this paper, a novel Authoring tool fully exploiting the object-based coding and 3D synthetic functionalities of the MPEG-4 standard is described. It is based upon an open and modular architecture able to progress with MPEG-4 versions and it is easily adaptable to newly emerging better and higher-level authoring and image sequence analysis features.

Automatic Thematic Categorization of Multimedia Documents using Ontological Information and Fuzzy Algebra

The semantic gap is the main problem of content based multimedia retrieval. This refers to the extraction of the semantic content of multimedia documents, the understanding of user information needs and requests, as well as to the matching between the two. In this chapter we focus on the analysis of multimedia documents for the extraction of their semantic content. Our approach is based on fuzzy algebra, as well as fuzzy ontological information. We start by outlining the methodologies that may lead to the creation of a semantic index; these methodologies are integrated in a video annotating environment. Based on the semantic index, we then explain how multimedia content may be analyzed for the extraction of semantic information in the form of thematic categorization. The latter relies on stored knowledge and a fuzzy hierarchical clustering algorithm that uses a similarity measure that is based on the notion of context.

Context - Sensitive Query Expansion Based on Fuzzy Clustering of Index Terms

Modern Information Retrieval Systems match the terms contained in a users query with available documents through the use of an index. In this work, we propose a method for expanding the query with its associated terms, in order to increase the system recall. The proposed method is based on a novel fuzzy clustering of the index terms, using their common occurrence in documents as clustering criterion. The clusters which are relevant to the terms of the query form the query context. The terms of the clusters that belong to the context are used to expand the query. Clusters participate in the expansion according to their degree of relevance to the query. Precision of the result is thus improved. This statistical approach for query expansion is useful when no a priori semantic knowledge is available.