Survey on ontology learning from Web and open issues <s> Web Structure mining-based approach for ontology learning from Web <s> This paper focuses on how to construct domain ontologies, in particular, a hierarchically structured set of domain concepts without concept definitions, reusing a machine readable dictionary (MRD) and making it adjusted to specific domains. In doing so, we must deal with concept drift, which means that the senses of concepts change depending on application domains. So here are presented the following two strategies: match result analysis and trimmed result analysis. The strategies try to identify which part may stay or should be moved, analyzing spell match results between given input domain terms and a MRD. We have done case studies in the filed of some law. The empirical results show us that our system can support a user in constructing a domain ontology. <s> BIB001 </s> Survey on ontology learning from Web and open issues <s> Web Structure mining-based approach for ontology learning from Web <s> IntroductIon Ontology integration is one of the most important research topics in knowledge integration and knowledge reconciliation. It has been widely applied in knowledge engineering, database and data warehouse, e-commerce as well as semantic Web (Dill, Eiron, Gibson, Gruhl & Guha, 2003; Maedche, 2002).�s more and more organizations in industry, academia and government publish their information (such as services and products) on the Web, how to add semantic annotation for machine-access and thus enhance the interoperability among these autonomous, distributed, and semantically heterogeneous information sources becomes more urgent. Usually, it is feasible to utilize weighted string-matching method to handle the explicit Automatically Integrating Heterogeneous ontologies from structured Web Pages <s> BIB002
In BIB001 , the underlying assumption behind this work is that the noun phrases appearing in the headings of a document as well as the document's hierarchical structure can be used to discover the concepts and taxonomic relations from documents. A system that supports this approach is implemented and applied on a set of Arabic agricultural extension documents. It takes as input a root concept, analyzes all input documents' heading structure, extracts concepts from headings and builds a taxonomical ontology BIB002 In this section, several approaches of ontology learning from web were detailed. Ontology extraction from texts belongs to this same work. Since, many semantic Web documents appeared on the Web and new semantic search engines are developed to search them, several approaches are interested to ontology construction by aggregating online ontologies. This will be the subject of the next section.