Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> The WordNet lexical database is now quite large and offers broad coverage of general lexical relations in English. As is evident in this volume, WordNet has been employed as a resource for many applications in natural language processing (NLP) and information retrieval (IR). However, many potentially useful lexical relations are currently missing from WordNet. Some of these relations, while useful for NLP and IR applications, are not necessarily appropriate for a general, domain-independent lexical database. For example, WordNet’s coverage of proper nouns is rather sparse, but proper nouns are often very important in application tasks. The standard way lexicographers find new relations is to look through huge lists of concordance lines. However, culling through long lists of concordance lines can be a rather daunting task (Church and Hanks, 1990), so a method that picks out those lines that are very likely to hold relations of interest should be an improvement over more traditional techniques. This chapter describes a method for the automatic discovery of WordNetstyle lexico-semantic relations by searching for corresponding lexico-syntactic patterns in large text collections. Large text corpora are now widely available, and can be viewed as vast resources from which to mine lexical, syntactic, and semantic information. This idea is reminiscent of what is known as “data mining” in the artificial intelligence literature (Fayyad and Uthurusamy, 1996), however, in this case the ore is raw text rather than tables of numerical data. The Lexico-Syntactic Pattern Extraction (LSPE) method is meant to be useful as an automated or semi-automated aid for lexicographers and builders of domain-dependent knowledge-bases. The LSPE technique is light-weight; it does not require a knowledge base or complex interpretation modules in order to suggest new WordNet relations. <s> BIB001 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> The focused access to knowledge resources like intranet documents plays a vital role in knowledge management and supports in general the shifting towards a Semantic Web. Ontologies act as a conceptual backbone for semantic document access by providing a common understanding and conceptualization of a domain. Building domain-specific ontologies is a time-consuming and expensive manual construction task. This paper describes our actual and ongoing work in supporting semi-automatic ontology acquisition from a corporate intranet of an insurance company. We present a comprehensive architecture and generic method for discovering a domain-tailored ontology from given intranet resources. <s> BIB002 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> For many knowledge intensive applications, it is necessary to have extensive domain-specific knowledge in addition to general-purpose knowledge bases usually built around Machine Readable Dictionaries. This paper presents a methodology for acquiring domain specific knowledge from text and classifying the concepts learned into an ontology that extends WordNet. The method was tested for three seed concepts selected from the financial domain: interest rate, stock market, and inflation. Queries were formed with each of these concepts and a small corpus of 500 sentences was extracted automatically from the Internet for each concept. The system learned a total of 151 new concepts and 69 new relations. <s> BIB003 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> We present in this paper a method to insert new concepts in an existing information extraction system based on a conceptual graph architecture. We use verb patterns as conceptual sub-graphs to characterize unknown terms in sentences. The objective is to help an information extraction system to cope with unknown expressions contained in a specific semantic context. <s> BIB004 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> In order to produce a good summary, one has to identify the most relevant portions of a given text. We describe in this paper a method for automatically training topic signatures-sets of related words, with associated weights, organized around head topics and illustrate with signatures we created with 6,194 TREC collection texts over 4 selected topics. We describe the possible integration of topic signatures with outologies and its evaluaton on an automated text summarization system. <s> BIB005 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> Ontologies have become an important means for structuring knowledge and building knowledge-intensive systems. For this purpose, efforts have been made to facilitate the ontology engineering process, in particular the acquisition of ontologies from domain texts. We present a general architecture for discovering conceptual structures and engineering ontologies. Based on our generic architecture we describe a case study for mining ontologies from text using methods based on dictionaries and natural language text. The case study has been carried out in the telecommunications domain. Supporting the overall text ontology engineering process, our comprehensive approach combines dictionary parsing mechanisms for acquiring a domain-specific concept taxonomy with a discovery mechanism for the acquisition of non-taxonomic conceptual relations. <s> BIB006 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> We introduce a methodology for automating the maintenance and growth of domain-specific concept taxonomies and grammatical class hierarchies simultaneously, based on knowledge capture from natural language texts. The assimilation process is centered around the linguistic and conceptual `quality' of various forms of evidence underlying the generation, assessment and on-going refinement of lexical and concept hypotheses. On the basis of the strength of evidence, hypotheses are ranked according to plausibility, and the most reasonable ones are selected for assimilation into the given lexical class hierarchy and domain ontology. <s> BIB007 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while ensuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC). <s> BIB008 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <s> This paper describes work done to explore the common ground between two different ongoing research projects: the standardization of lexical and terminological resources, and the use of conceptual ontologies for information extraction and data integration. Specifically, this paper explores improving the generation of extraction ontologies through use of a comprehensive terminology database that has been represented in a standardized format for easy tool-based implementation. We show how, via the successful integration of these two distinct efforts, it is possible to leverage large-scale terminological and conceptual information having relationship-rich semantic resources in order to reformulate, match, and merge retrieved information of interest to a user. <s> BIB009 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <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> BIB010 </s> Survey on ontology learning from Web and open issues <s> Domain-Dependent approach for Ontology learning from textual documents <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> BIB011
OL approaches from Web content consists generally in enriching a small ontology called "minimal" or "granular" with new concepts and new relationships using text mining techniques. Learning ontologies from texts has been widely used in the community of knowledge engineering. This is in particular the work of:       BIB007 BIB001  BIB008 BIB002 BIB009 BIB003  BIB004 38] . However, no sufficiently detailed methodology has been presented to assist the learning process ontology. Indeed, the literature is limited to the presentation of guidelines more or less general. Thus, for each approach, it is important to know the aims and scope of the learning process, its main stages, the sources of knowledge used in learning, the main techniques applied in the process, re-usability of ontologies existing and the study of its feasibility. These approaches to ontology learning from text are generally based on the use of a corpus of texts. This corpus should be representative of the domain of the ontology. Using a set of techniques, we try to project in the ontology knowledge contained in texts by extracting concepts and relations. We distinguish mainly five categories of text mining techniques:  Linguistic techniques  and lexico-syntactic patterns BIB001 ;  Clustering techniques and / or classification techniques    BIB005 ;  Statistical techniques    38] ;  Association rule based techniques BIB006  and hybrid ones BIB011  . Besides of ontology learning from texts, ontology learning from Web appears to be a second category in domain-dependant OL. The most known approaches exploit the textual Web content to enrich concepts using Wordnet  . Several approaches described in  and  enrich ontologies from Web documents. Another approach is proposed in BIB001 in order to reduce the terminological and conceptual ambiguity among members of a virtual community. This approach proposes the discovery of concepts and relations from the Web sites and lead to the development by the system OntoLearn BIB010 . In these approaches, domain knowledge a priori is required. For this reason, they are dependent to the domain of the ontology and the collection of Web documents related to this domain need user intervention.