Manolis Wallace

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.

Towards a context aware mining of user interests for consumption of multimedia documents

As the annotation of multimedia documents uses multiple descriptors, it is possible to define multiple, semantically meaningful, similarity (or dissimilarity) relations among them. Therefore, for cases such as the mining of user interests for consumption of multimedia documents, based on usage history, where the clustering of documents is necessary, it is important to develop context aware clustering algorithms that are able to handle this type of information. We explain the relation between context, user interest and the multiple relations; furthermore, we present a clustering algorithm that is able to mine user interests from multi-relational data sets.

Intelligent one-stop-shop travel recommendations using an adaptive neural network and clustering of history.

The rapid growth of e-commerce during the last years has obliged a significant number of companies and professionals from diverse fields to turn to the Internet as a medium through which they aim to promote their products and services. A main issue for product and service providers is that, as this new market is characterized by the lack of personal contact, it is difficult to offer personalized services to end users; it is this type of service that end users look for and remain faithful to. Recommender systems belong to a new breed of software that aims to fill this gap; they rely on the analysis of past user actions to estimate the optimal way with which to interact with each user. In this article we explain why existing recommender systems are not adequate to provide for efficient personalization of interaction in the area of travel services, as they cannot support the user in all the phases of travel planning, and propose a new scheme to overcome the identified difficulties. Our approach considers the relation between different types of services in the usage history of the system. It is based on a hierarchical clustering of usage history to extract meaningful usage patterns, as well as an adaptive neural network structure that allows for online adaptation to the user, and enables the offering of intelligent recommendations.

Multimodal user's affective state analysis in naturalistic interaction

Affective and human-centered computing have attracted an abundance of attention during the past years, mainly due to the abundance of environments and applications able to exploit and adapt to multimodal input from the users. The combination of facial expressions with prosody information allows us to capture the users’ emotional state in an unintrusive manner, relying on the best performing modality in cases where one modality suffers from noise or bad sensing conditions. In this paper, we describe a multi-cue, dynamic approach to detect emotion in naturalistic video sequences, where input is taken from nearly real world situations, contrary to controlled recording conditions of audiovisual material. Recognition is performed via a recurrent neural network, whose short term memory and approximation capabilities cater for modeling dynamic events in facial and prosodic expressivity. This approach also differs from existing work in that it models user expressivity using a dimensional representation, instead of detecting discrete ‘universal emotions’, which are scarce in everyday human-machine interaction. The algorithm is deployed on an audiovisual database which was recorded simulating human-human discourse and, therefore, contains less extreme expressivity and subtle variations of a number of emotion labels. Results show that in turns lasting more than a few frames, recognition rates rise to 98%.

Computationally efficient sup-t transitive closure for sparse fuzzy binary relations

The property of transitivity is one of the most important for fuzzy binary relations, especially in the cases when they are used for the representation of real-life similarity or ordering information. As far as the algorithmic part of the actual calculation of the transitive closure of such relations is concerned, works in the literature mainly focus on crisp symmetric relations, paying little attention to the case of general fuzzy binary relations. Most works that deal with the algorithmic part of the transitive closure of fuzzy relations focus only on the case of max-min transitivity, disregarding other types of transitivity. In this paper, after formalizing the notion of sparseness and providing a representation model for sparse relations that displays both computational and storage merits, we propose an algorithm for the incremental update of fuzzy sup-t transitive relations. The incremental transitive update (ITU) algorithm achieves the re-establishment of transitivity when an already transitive relation is only locally disturbed. Based on this algorithm, we propose an extension to handle the sup-t transitive closure of any fuzzy binary relation, through a novel incremental transitive closure (ITC) algorithm. The ITU and ITC algorithms can be applied on any fuzzy binary relation and t-norm; properties such as reflexivity, symmetricity and idempotency are not a requirement. Under the specified assumptions for the average sparse relation, both of the proposed algorithms have considerably smaller computational complexity than the conventional approach; this is established both theoretically and verified via appropriate computing experiments.

IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones

Fuzzy Ontologies comprise a relatively new knowledge representation paradigm that is being increasingly applied in application scenarios in which the treatment and utilization of vague or imprecise knowledge are important. However, the majority of research in the area has mostly focused on the development of conceptual formalisms for representing (and reasoning with) fuzzy ontologies, while the methodological issues entailed within the development process of such an ontology have been so far neglected. With that in mind, we present in this paper IKARUS-Onto, a comprehensive methodology for developing fuzzy ontologies from existing crisp ones that significantly enhances the effectiveness of the fuzzy ontology development process and the quality, in terms of accuracy, shareability and reusability, of the process’s output.

An intelligent system for facial emotion recognition

An intelligent emotion recognition system, interweaving psychological findings about emotion representation with analysis and evaluation of facial expressions has been generated and its performance has been investigated with experimental real data. Additionally, a fuzzy rule based system has been created for classifying facial expressions to the six archetypal emotion categories. The continuous 2-D emotion space was then examined and a pool of known and novel classification and clustering techniques have been applied to our data obtaining high rates in classification and clustering into quadrants of the emotion representation space.

Advances in Semantic Media Adaptation and Personalization

Realizing the growing importance of semantic adaptation and personalization of media, the editors of this book brought together leading researchers and practitioners of the field to discuss the state-of-the-art, and explore emerging exciting developments. This volume comprises extended versions of selected papers presented at the 1st International Workshop on Semantic Media Adaptation and Personalization (SMAP 2006), which took place in Athens in December 2006.

Computationally efficient incremental transitive closure of sparse fuzzy binary relations

Existing literature in the field of transitive relations focuses mainly on dense, Boolean, undirected relations. With the emergence of a new area of intelligent retrieval, where sparse transitive fuzzy ordering relations are utilized, existing theory and methodologies need to be extended, as to cover the new needs. This work discusses the incremental update of such fuzzy binary relations, while focusing on both storage and computational complexity issues. Moreover, it proposes a novel transitive closure algorithm that has a remarkably low computational complexity (below O(n/sup 2/)) for the average sparse relation; such are the relations encountered in intelligent retrieval.

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.