Web Person Name Disambiguation Using Social Links and Enriched Profile Information

In this article, we investigate the problem of cross-document person name disambiguation, which aimed at resolving ambiguities between person names and clustering web documents according to their association to different persons sharing the same name. The majority of previous work often formulated cross-document name disambiguation as a clustering problem. These methods employed various syntactic and semantic features either from the local corpus or distant knowledge bases to compute similarities between entities and group similar entities. However, these approaches show limitations regarding robustness and performance. We propose an unsupervised, graph-based name disambiguation approach to improve the performance and robustness of the state-of-the-art. Our approach exploits both local information extracted from the given corpus, and global information obtained from distant knowledge bases. We show the effectiveness of our approach by testing it on standard WePS datasets. The experimental results are encouraging and show that our proposed method outperforms several baseline methods and also its counterparts. The experiments show that our approach not only improves the performances, but also increases the robustness of name disambiguation

The state of the art in ontology learning: a framework for comparison

In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some distinguishing factors and have many features in common. This paper presents the state of the art in ontology learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework answer to questions about what to learn, from where to learn and how to learn. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulted ontology and also the evaluation process. To extract the framework over 50 OL systems or modules from the recent workshops, conferences and published journals are studied and seven prominent of them with most differences are selected to be compared according to our framework. In this paper after a brief description of the seven selected systems we will describe the framework dimensions. Then we will place the representative ontology learning systems into our framework. At last we will describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features (dimensions ’ values) to create or use an OL system for their own domain or application

OILSW: A New System for Ontology Instance Learning in Semantic Web

Abstract. The Semantic Web is expected to extend the current Web by providing structured content via the addition of annotations. Because of the large amount of pages in the Web, the manual annotation is very time consuming. Finding an automatic or semiautomatic method to change the current Web to the Semantic Web is very helpful. In a specific domain, Web pages are the instances of that domain ontology. So we need semiautomatic tools to find these instances and fill their attributes. In this article, we propose a new system named OILSW for instance learning of an ontology from Web pages of Websites in a common domain. This system is the first comprehensive system for automatically populating the ontology for websites. By using this system, any Website in a certain domain can be automatically annotated