Semantic Annotation of Documents: A Comparative Study

Semantic annotation, which is considered one of the semantic web applicative aspects, has been adopted by researchers from different communities as a paramount solution that improves searching and retrieval of information by promoting the richness of the content. However, researchers are facing challenges concerning both the quality and the relevance of the semantic annotations attached to the annotated document against its content as well as its semantics, without ignoring those regarding automation process which is supposed to ensure an optimal system for information indexing and retrieval. In this article, we will introduce the semantic annotation concept by presenting a state of the art including definitions, features and a classification of annotation systems. Systems and proposed approaches in the field will be cited, as well as a study of some existing annotation tools. This study will also pinpoint various problems and limitations related to the annotation in order to offer solutions for our future work

Incremental Community Mining in Location-based Social Network

A social network can be defined as a set of social entities connected by a set of social relations. These relations often change and differ in time. Thus, the fundamental structure of these networks is dynamic and increasingly developing. Investigating how the structure of these networks evolves over the observation time affords visions into their evolution structure, elements that initiate the changes, and finally foresee the future structure of these networks. One of the most relevant properties of networks is their community structure – set of vertices highly connected between each other and loosely connected with the rest of the network. Subsequently networks are dynamic, their underlying community structure changes over time as well, i.e they have social entities that appear and disappear which make their communities shrinking and growing over time. The goal of this paper is to study community detection in dynamic social network in the context of location-based social network. In this respect, we extend the static Louvain method to incrementally detect communities in a dynamic scenario following the direct method and considering both overlapping and non-overlapping setting. Finally, extensive experiments on real datasets and comparison with two previous methods demonstrate the effectiveness and potential of our suggested method