Automatic semantic annotation of structured data enables unsupervised integration of data from heterogeneous sources but is difficult to perform accurately due to the presence of many numeric fields and proper-noun fields that do not allow reference-based approaches and the absence of natural language text that prevents the use of language-based approaches. In addition, several of these semanti...

Latent Semantic Analysis is used in many research fields with several applications of classifications. We propose to improve LSA with additional semantic information found with syntactic knowledge.

Many image-based perception tasks can be formulated as detecting, associating and tracking semantic keypoints, e.g. , human body pose estimation tracking. In this work, we present a general framework that jointly detects forms spatio-temporal keypoint associations in single stage, making the fi...

The use of naive Bayesian classifier (NB) and the classifier by the k nearest neighbors (kNN) in classification semantic analysis of authors’ texts of English fiction has been analysed. The authors’ works are considered in the vector space the basis of which is formed by the frequency characteristics of semantic fields of nouns and verbs. Highly precise classification of authors’ texts in the v...

Mapping data to a shared domain ontology is a key step in publishing semantic content on the Web. Most of the work on automatically mapping structured and semi-structured sources to ontologies focuses on semantic labeling, i.e., annotating data fields with ontology classes and/or properties. However, a precise mapping that fully recovers the intended meaning of the data needs to describe the se...