Survey on Ontology learning from Web and open issues

IEEE Expert / IEEE Intelligent Systems, 2002

The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck.

International Journal of Information Technology and Computer Science, 2013

In present age of co mputers, there are various resources for gathering information related to given query like Radio Stations, Television, Internet and many mo re. A mong them, Internet is considered as major factor for obtaining any informat ion about a given domain. When a user wants to find some informat ion, he/she enters a query and results are produced via hyperlinks linked to various documents available on web. But the information that is retrieved to us may or may not be relevant. This irrelevance is caused due to huge collection of documents available on web. Tradit ional search engines are based on keyword based searching that is unable to transform raw data into knowledgeable representation data. It is a cumbersome task to extract relevant informat ion fro m large collection of web documents. These shortcomings have led to the concept of Semantic Web (SW) and Ontology into existence. Semantic Web (SW) is a well defined portal that helps in extract ing relevant informat ion using many Information Retrieval (IR) techniques. Current Info rmation Retrieval (IR) techniques are not so advanced that they can be able to exploit semantic knowledge with in documents and give precise result. The terms, Informat ion Retrieval (IR), Semantic Web (SW) and Ontology are used differently but they are interconnected with each other. Information Retrieval (IR) technology and Web based Indexing contributes to existence of Semantic Web. Use of Ontology also contributes in building new generation of web-Semantic Web. With the help of ontologies, we can make content of web as it will be markup with the help of Semantic Web documents (SWD's). Ontology is considered as backbone of Software system. It improves understanding between concepts used in Semantic Web (SW). So, there is need to build an ontology that uses well defined methodology and process of developing ontology is called Ontology Development.

HAL (Le Centre pour la Communication Scientifique Directe), 2013

The Semantics Web is a vision for the future of the Web in which information is given explicit meaning, making it easier for machines to automatically process and integrate information available on the Web. An ontology defines the terms used to describe and represent an area of knowledge. Ontologies are used by people, databases, and applications that need to share domain information (a domain is just a specific subject area or area of knowledge, like medicine, tool manufacturing, real estate, automobile repair, financial management, etc). The amount of data caused by the success of Internet is demanding methodologies and tools to automatically extract unknown and potentially useful knowledge out of it, generating structured representations with that knowledge. The main goal of this paper is to present comprehensive introduction into MDAbased ontology development. It will provide an introduction to the field of the Semantics Web and ontology engineering, a description of several UML-and metamodeling-based solutions and tools for ontology development.

2010

Semantic web provides virtual communities that enable intelligent interaction between software agents and people due to availability of standard open ontologies. But, as the semantic web is gaining much popularity, there is a massive growth seen in the ontology development which poses new research challenges such as ontology classification, ranking, searching, retrieval, etc. This results many recent developments, like OntoKhoj, Swoogle, OntoSearch, that facilitate user for such tasks. These semantic web portals mainly treat ontologies as plain texts and use traditional classification algorithms of plain text for classifying ontologies in directories and assigning predefined labels rather than using semantic knowledge hidden within the ontologies. These approaches suffer with many types of classification problems and lack of accuracy, especially in the case of overlapping ontologies that share common vocabularies. In this paper, we define ontology classification problem and categorized it into many sub-problems. We present a new methodology for ontology classification that is based on ontology approach for ontology classification and retrieval. The proposed framework, ONTCLASSIFIRE, benefit construction, maintenance or expansion of ontologies directories on the semantic web, and helps in ontology management and retrieval for software agents and people. We conclude that the use of context specific knowledge hidden in ontologies gives more accurate results of ontology classification and retrieval.

2008

Ontologies constitute an approach for knowledge representation that can be shared establishing a shared vocabulary for different applications and are also the backbone of the Semantic Web. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. However, ontology development is a difficult and time consuming task. Ontology learning is an approach for the problem of knowledge acquisition bottleneck that aims at reducing the cost of ontology construction through the development of automatic methods for the extraction of knowledge about a specific domain and its representation in an ontology like structure. This paper provides a discussion on existing ontology learning techniques and the state of the art of the field.

This paper proposes the design of a System for Automatic Learning of Ontologies and Lexical Information (SALOX) for the Dynamic Semantic Ontological Framework for the Semantic Web (DSOFSW). DSOFSW interprets query in natural language (Spanish) to the web, and is composed by five parts; a linguistic ontology for the grammar of Spanish, a lexicon for the lexical information, a database of facts about the system experiences, a task ontology for the linguistic analysis process, and an interpretative ontology of the context. SALOX integrates several methods, approaches and techniques for information extraction, discovery and actualization (pragmatic (user profile, context knowledge), lexical and semantic linguistic information, etc.) in order to update the knowledge used for DSOFSW. SALOX has a component to map the sources of learning with the learning methods, and another to update the linguistic ontology and the lexicon of the DSOFSW. Specifically, in this paper we present the design of the learning unit of lexical information.

Sci. Ann. Cuza Univ., 2000

The paper presents a system for querying (in natural language) a set of text documents from a limited domain. The domain knowledge, represented in description logics (DL), is used for filtering the documents returned as answer and it is extended dynamically (when new concepts are identified in the texts), as result of DL inference mechanisms. The conceptual hierarchy is built semi-automatically from the texts. Concept instances are identified using shallow natural language parsing techniques.

Database : the journal of biological databases and curation, 2018

Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniqu...

We present initial experimental results of an approach to learning ontological concepts from text. For each word to be learned, our system a) creates a corpus of sentences, derived from the web, containing this word; b) automatically semantically annotates the corpus using the OntoSem semantic analyzer; c) creates a candidate new concept by collating semantic information from annotated sentences; and d) finds in the existing ontology concept(s) "closest" to the candidate. In the long term, our approach is intended to support the continual mutual bootstrapping of the learner and the semantic analyzer as a solution to the knowledge acquisition bottleneck problem in AI.