Taxonomy classification and query answering are the core reasoning services provided by most of the Semantic Web (SW) reasoners. However, the algorithms used by those reasoners are based on Tableau method or Rules. These well-known methods in the literature have already shown their limitations for large-scale reasoning. In this demonstration, we shall present the CEDAR system for classifying an...

Classifying image regions into one of several pre-defined semantic categories is a typical image understanding problem. Different image regions and object types might have very similar color or texture characteristics making it difficult to categorize them. Without contextual information it is often impossible to find reasonable semantic labeling for outdoor images. In this paper, we combine an...

Measuring relational similarity between words is important in numerous natural language processing tasks such as solving analogy questions and classifying noun-modifier relations. We propose a method to measure the similarity between semantic relations that hold between two pairs of words using a web search engine. First, each pair of words is represented by a vector of automatically extracted ...

Question terminology is a set of terms which appear in keywords, idioms and fixed expressions commonly observed in questions. This paper investigates ways to automatically extract question terminology from a corpus of questions and represent them for the purpose of classifying by question type. Our key interest is to see whether or not semantic features can enhance the representation of strongl...

Following the development of semantic web technologies, many ontologies and thesauri have been proposed to index resources during the last decade. However, despite their expressiveness, those knowledge models do not always cover all the points of interest within dedicated applications. Therefore, alternative ad hoc taxonomies have been developed to support resources classifying processes. This ...

Vision-based autonomous driving requires classifying each pixel as corresponding to road or not, which can be addressed using semantic segmentation. Semantic segmentation works well when used with a fully supervised model, but in practice, the required work of creating pixel-wise annotations is very expensive. Although weakly supervised segmentation addresses this issue, most methods are not de...

Ontology learning refers to generating scalable ontologies based on the web of documents. It includes two main processes: first, extracting concepts and their semantic relationships; second, classifying instances based on the extracted concepts. In this paper, we proposed an effective approach called Max Similarity Min Distance Algorithm (MSMDA) to address the second process. Traditionally, the...

Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new level. Belief networks, or Bayesian network...

The need for supporting the classification and semantic annotation of services constitutes an important challenge for service–centric software engineering. Late–binding and, in general, service matching approaches, require services to be semantically annotated. Such a semantic annotation may require, in turn, to be made in agreement to a specific ontology. Also, a service description needs to p...

Classifying images into a set of semantic categories that are meaningful to humans has proved to be a challenging and attractive problem in the field of content-based retrieval. Addressing this problem is typically based on the initial extraction of low-level features for the images and the subsequent application of a pattern recognition technique, to divide the feature space in a number of sub...