Framework for Automatic Semantic Annotation of Arabic Websites

In order to achieve the vision of the semantic Web, it is important to have enough amount of semantic content on the Web sources. To produce the semantic content on the existing Web, semantic annotation of the Web sources is required. Semantic annotation adds machine-readable content to the Web sources. Because the Web is growing at an exponential rate, semantic annotation by hand is not possible. In this paper, we present an Automatic Semantic Annotation Framework (ASAF) for semantic annotation of Arabic Web sources based on the domain ontologies. We present a learning approach that utilizes public Arabic resources, such as Wikipedia and WordNet for building Arabic ontologies. Moreover, we present different approaches for extracting name entities and relationships from Arabic Web sources. As a case study, we have developed and expanded a set of Arabic ontologies related to food, health, and nutrition through a set of processes. We have also developed the ASAF prototype, and showed how it can utilize these ontologies for extracting health, food related name entities, and relationships from the Web sources in order to annotate and store them in the knowledge-base. We conducted several experiments to test the capability of ASAF in recognizing the name entities and relationships using different approaches. Empirical evaluations of ASAF show promising performance results in terms of precision, recall, and F-measure. The outcome of the presented framework could be utilized by semantic Web searching applications to retrieve precise answers to the end user smarter queries. An important feature of ASAF is that it could be ported to other domains with minimal extension. ASAF also contributes to the vision of the semantic Web in the target domains in Arabic Web sources.