Semantic similarity

About: Semantic similarity is a research topic. Over the lifetime, 14605 publications have been published within this topic receiving 364659 citations. The topic is also known as: semantic relatedness.

Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval

Abstract: Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called “Dense-Sift,” that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.

Concepts not alone: exploring pairwise relationships for zero-shot video activity recognition

Abstract: Vast quantities of videos are now being captured at astonishing rates, but the majority of these are not labelled. To cope with such data, we consider the task of content-based activity recognition in videos without any manually labelled examples, also known as zero-shot video recognition. To achieve this, videos are represented in terms of detected visual concepts, which are then scored as relevant or irrelevant according to their similarity with a given textual query. In this paper, we propose a more robust approach for scoring concepts in order to alleviate many of the brittleness and low precision problems of previous work. Not only do we jointly consider semantic relatedness, visual reliability, and discriminative power. To handle noise and non-linearities in the ranking scores of the selected concepts, we propose a novel pairwise order matrix approach for score aggregation. Extensive experiments on the large-scale TRECVID Multimedia Event Detection data show the superiority of our approach.

How Similar Is It? Towards Personalized Similarity Measures in Ontologies

Abstract: Finding a good similarity assessment algorithm for the use in ontologies is central to the functioning of techniques such as retrieval, matchmaking, clustering, data-mining, ontology translations, automatic database schema matching, and simple object comparisons. This paper assembles a catalogue of ontology based similarity measures, which are experimentally compared with a “similarity gold standard” obtained by surveying 50 human subjects. Results show that human and algorithmic similarity predications varied substantially, but could be grouped into cohesive clusters. Addressing this variance we present a personalized similarity assessment procedure, which uses a machine learning component to predict a subject’s cluster membership, providing an excellent prediction of the gold standard. We conclude by hypothesizing ontology dependent similarity measures.

A classification of semantic conflicts in heterogeneous database systems

Abstract: Increasingly companies are doing business in an environment replete with heterogeneous information systems which must cooperate. Cooperation between these systems presupposes the resolution of the semantic conflicts that are bound to occur. In this article, we propose a classification of semantic conflicts which can be used as the basis for the incremental discovery and resolution of these conflicts. We classify conflicts along the two dimensions of naming and abstraction which, taken together, capture the semantic mapping of the conflict. We add a third dimension, level of heterogeneity to assist in the schematic mapping between two databases. The classification provides a systematic representation of alternative semantic interpretations of conflicts during the reconciliation process. As a result, the design of query‐directed dynamic reconciliation systems is possible. The classification is shown to be sound and minimal. Completeness is discussed.

Semantic classes and syntactic ambiguity

Abstract: In this paper we propose to define selectional preference and semantic similarity as information-theoretic relationships involving conceptual classes, and we demonstrate the applicability of these definitions to the resolution of syntactic ambiguity. The space of classes is defined using WordNet [8], and conceptual relationships are determined by means of statistical analysis using parsed text in the Penn Treebank.