Survey on ontology learning from Web and open issues <s> Ontology learning approaches from Web ontologies <s> The Semantic Web is intended to enable machine understandable web content and seems to be a solution for many drawbacks of the current Web. It is based on metadata that formally describe the semantics of Web contents. In this paper we present an integrated and semi-automatic approach for generating shared-understandable metadata for data-intensive Web applications. This approach is based on mapping a given relational schema into an already existing ontology structure using a reverse engineering process. As a case study we present this style of a schema- and data-migration for our Institute web portal. The presented approach can be applied to a broad range of today’s data-intensive Web sites. <s> BIB001 </s> Survey on ontology learning from Web and open issues <s> Ontology learning approaches from Web ontologies <s> In this paper, we present a method for learning ontologies from RDF annotations of Web resources by systematically generating the most specific generalization of all the possible sets of resources. The preliminary step of our method consists in extracting (partial) resource descriptions from the whole RDF graph gathering all the annotations. In order to deal with algorithmic complexity, we incrementally build the ontology by gradually increasing the size of the resource descriptions we consider. <s> BIB002 </s> Survey on ontology learning from Web and open issues <s> Ontology learning approaches from Web ontologies <s> Developing the Semantic Web, seeking to improve the semantic awareness of computers connected via the Internet, requires a systematic, computer-oriented world representation. Researchers often refer to such a model as an ontology. Despite the work done on them in recent years, ontologies have yet to be widely applied and used. Research has devoted only limited attention to such practical issues as techniques and tools aimed at an ontology's actual construction and content. The authors have built a software environment, centered around the OntoLearn tool, which can build and assess a domain ontology for intelligent information integration within a virtual user community. OntoLearn has already been tested in two European projects, where it functioned as the basis for a semantic interoperability platform used by small- and medium-sized tourism enterprises. Further, developers can easily adapt OntoLearn to work with other general-purpose ontologies. <s> BIB003 </s> Survey on ontology learning from Web and open issues <s> Ontology learning approaches from Web ontologies <s> Semantic web search engines are evolving and many prototype systems and some implementation have been devel- oped. However, there are some different views on what a semantic search engine should do. In this paper, a categorization scheme for semantic web search engines are introduced and elaborated. For each category, its components are described according to a proposed general architecture and various approaches employed in these components are discussed. We also propose some factors to evaluate systems in each category. <s> BIB004
The idea about online ontology building from Web is not a new one BIB004 . Harnessing RDF files on the Web might be the first step towards achieving true reuse. In BIB004 an approach for learning ontology from RDF annotations of Web resources was proposed. To perform the learning process, a particular approach of concept formation is adopted, considering ontology as a concept hierarchy, where each concept is defined its extension by a cluster of resources and intension by the most specific common description of these resources. A resource description is a RDF subgraph containing all resources reachable from the considered resource through properties. Stojanovic BIB001 presents an approach for an automated migration of data-intensive web sites into the Semantic Web. They extract light ontologies from resources such as XML Schema or relational database schemata and try to build light ontologies from conceptual database schemas using a mapping process that can form the conceptual metadata annotations that are automatically created from the database instances. [38] presents an approach TANGO (Table Analysis  for Generating Ontologies) to generating ontologies based on HTML table analysis. TANGO. Providing support for reuse during ontology development from specific ontology libraries has been studied before (e.g.  BIB002 ). However, the objective was mainly to enable users to reuse or import whole ontologies or ontology modules. They provided no support for ranking available ontologies, or for extracting and merging the ontology parts of interest, or for evaluating the resulting ontology. In BIB003 , a framework for integrating multiple ontologies from structured web pages into a common ontology is proposed. A universal similarity paradigm reflecting the implicit coherences among the ontologies is presented. Ontology alignment and construction methods are described. According to BIB003 , the output ontology will follow users' configuration such as their preferred structure and filtering threshold. It facilitates deep annotation and interoperation in structured web pages from heterogeneous systems BIB003 . Several approaches were proposed to use ontology search engines or ontology meta-search engines to build ontologies by aggregating many searched domain ontologies. There are an increasing number of online libraries for searching and downloading ontologies. Examples of such libraries include Ontolingua, Protégé, and DAML. Few search engines have recently appeared that allow keyword-based search for online ontologies, such as Swoogle and OntoSearch. In  , a new approach consists in searching online ontologies for representations of certain concepts, ranks the retrieved ontologies according to some criteria, then extract the relevant parts of the top ranked ontologies, and merge those parts to acquire the richest domain representation as possible. We don't deny that such approaches could lead easily to have many domain ontologies but some problems still remain. In fact, we are still worry about many issues:  The reliability of existent Web ontology is not an evidence  The availability of ontologies to be reused in terms of numbers and domain variety  The quality of output ontology depends on the quality of input ontologies.  The use of Ontology searching, ontology ranking, ontology mapping, ontology merging, and ontology segmentation methods make this approach more complex.