Search

There are abundance of existing biomedical ontologies such as the National Cancer Institute\ud Thesaurus (NCIT), the Foundational Model of Anatomy (FMA) and the Systematized\ud Nomenclature of Medicine-Clinical Terms (SNOMED CT). Implementing these ontologies in\ud a particular system however, may cause unnecessary high usage of memory and slows down\ud the systems’ performance. On the other hand, building a new ontology from scratch will\ud require additional time and efforts. Therefore, this research explores the ontology reuse\ud approach in order to develop an Abdominal Ultrasound Ontology (AUO) by extracting\ud concepts from existing biomedical ontologies. This paper presents the reader with a step by\ud step method in reusing ontologies together with suggestions of the off-the-shelf tools that can\ud be used to ease the process. The result shows that ontology reuse is beneficial especially in\ud the biomedical field as it allows for developers from the non-technical background to build\ud and use domain specific ontology with ease. It also allows for developers with technical\ud background to develop ontologies with minimal involvements from domain experts. The\ud methodology proposed is also adaptable as it allows the ontology to be updated fairly easily.

International audience; In this paper, we use movies and series subtitles and applied text mining and Natural Language Processing methods to evaluate emotions in videos. Three different word lexicons were used and one of the outcomes of this research is the generation of a secondary dataset with more than 3658 records which can be used for other data analysis and data mining research. We used our secondary dataset to find and display correlations between different emotions on the videos and the correlation between emotions on the movies and users' scores on IMDb using the Pearson correlation method and found some statistically significant correlations.

Representing and reasoning on imprecise temporal information is a common requirement in the field of Semantic Web. Many works exist to represent and reason on precise temporal information in OWL; however, to the best of our knowledge, none of these works is devoted to represent and reason on imprecise time intervals. To address this problem, we propose a fuzzy-based approach for representing and reasoning on imprecise time intervals in ontology. Our approach is based on fuzzy sets theory and fuzzy tools and is modeled in Fuzzy-OWL 2. The 4D-fluents approach is extended, with new fuzzy components, in order to represent imprecise time intervals and qualitative fuzzy interval relations. The Allen’s interval algebra is extended in order to compare imprecise time intervals in a fuzzy gradual personalized way. Inferences are done via a set of Mamdani IF-THEN rules.

Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.

In last years, the spread of social Web has promoted a strong interest in analyzing how information related to a given topic diffuses. Nevertheless, this is still quite an unexplored field in the literature. In this paper we propose a general approach that makes use of a set of Natural Language Processing (NLP) techniques to analyse some of the most important features of information related to a topic. The domain of this study is Twitter, since here topics are easily identified by means of hashtags. In particular, our aim is to analyse the possible change over time of the content sub-topicality and sentiment in the tracked tweets, and bring out their relationships with the users’ demographic features.