An oddly-positioned position paper on context and ontology

Proceedings of the 2008 IEEE International Conference on Semantic Computing,This paper is a theoretical analysis of formal annotation and ontology for the expression of the semantics of document. They are found wanting in this respect, not only for technical reasons, but because they embody a fundamentally misunderstood model of the process of signification. The author proposes an alternative model in which the interpretation context plays a fundamental role, and briefly discuss it and its current technical embodiment

Semantic computing in multimedia: ACM multimedia tutorial

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 19th ACM international conference on Multimedia, http://dx.doi.org/10.1145/2072298.2072401This short overview describes the contents of the tutorial Semantic computing in multimedia, which was offered to the participants of ACM Multimedia 2011. Given the impossibility of summarizing properly the contents of the tutorial in just two pages, the purpose of this overview is mainly to introduce the reader to the relevant bibliography

Benchmarking without ground truth

Simone Santini, "Benchmarking without ground truth", Proc. SPIE 6061, Internet Imaging VII, 60610I (2006). Copyright 2006 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibitedMany evaluation techniques for content based image retrieval are based on the availability of a ground truth, that is on a "correct" categorization of images so that, say, if the query image is of category A, only the returned images in category A will be considered as "hits." Based on such a ground truth, standard information retrieval measures such as precision and recall and given and used to evaluate and compare retrieval algorithms. Coherently, the assemblers of benchmarking data bases go to a certain length to have their images categorized. The assumption of the existence of a ground truth is, in many respect, naive. It is well known that the categorization of the images depends on the a priori (from the point of view of such categorization) subdivision of the semantic field in which the images are placed (a trivial observation: a plant subdivision for a botanist is very different from that for a layperson). Even within a given semantic field, however, categorization by human subjects is subject to uncertainty, and it makes little statistical sense to consider the categorization given by one person as the unassailable ground truth. In this paper I propose two evaluation techniques that apply to the case in which the ground truth is subject to uncertainty. In this case, obviously, measures such as precision and recall as well will be subject to uncertainty. The paper will explore the relation between the uncertainty in the ground truth and that in the most commonly used evaluation measures, so that the measurements done on a given system can preserve statistical significance

An evaluation of novelty and diversity based on fuzzy logic

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Proceedings of the Workshop on Novelty and Diversity in Recommender Systems, DiveRS 2011Information retrieval systems are based on an estimation or prediction of the relevance of documents for certain topics associated to a query or, in the case of recommendation systems, for a certain user profile. Most systems use a graded relevance estimation (a.k.a. relevance status value), that is, a real value r(d,τ ) ∈ [0, 1] for the relevance of document d with respect to topic τ . In retrieval systems based on the Probability Ranking Principle [9], this value has a probabilistic interpretation, that is, r(d, τ ) is equivalent (in rank) to the probability that a user will consider the document relevant. We contend in this paper for an alternative interpretation, where the value r(d, τ ) is considered as the fuzzy truth value of the statement “d is relevant for τ”. We develop and evaluate two measures that determine the quality of a result set in terms of diversity and novelty based on this fuzzy interpretation