Ontologies represent rich semantics in a lexical way. Lexical labels are used to identify concepts and relationships, though there is no bijective mapping between them. Phenomenons such as synonyms and homonyms exemplify this, and can result in frustrating misunderstanding and ambiguity. In the elicitation and application of ontologies, the meaning of the ontological knowledge is dependent on the context. We consider the role of context in ontology elicitation by introducing context in a concept definition server for ontology representation. We also adopt other features of context found in literature, such as packaging of knowledge, aligning elements of different contexts, and reasoning about contexts. Finally, we illustrate context-driven ontology elicitation with a real world case study.

Abstract. A viable ontology engineering methodology requires supporting domain experts in gradually building and managing increasingly complex versions of ontological elements and their converging and diverging interrelationships. Contexts are necessary to formalise and reason about such a dynamic wealth of knowledge. However, context dependencies introduce many complexities. In this article, we introduce a formal framework for supporting context dependency management processes, based on the DOGMA framework and methodology for scalable ontology engineering. Key notions are a set of context dependency operators, which can be combined to manage complex context dependencies like articulation, application, specialisation, and revision dependencies. In turn, these dependencies can be used in context-driven ontology engineering processes tailored to the specific requirements of collaborative communities. This is illustrated by a real-world case of interorganisational competency ontology engineering.

Search is one of the main motivations behind semantic web. A lot of proposals on semantic web search engines have been appeared in recent years but most of them are restricted to a limited context of web. Here we propose a scalable Multi Ontological Semantic Search Engine to conquer this problem. In addition some components of this architecture have been implemented and their results are reported.

Abstract — In this paper, we set out to present an original rule-learning algorithm for symbolic natural language processing (NLP), designed to learn the rules of extraction of keywords marked in its training sentences. What really sets our methodology apart from other recent developments in the field of NLP is the implementation of a hierarchy of parts-of-speech at the very core of the algorithm. This makes the rules dependent only on the sentence’s structure rather than on context and domain-specific information. The theoretical development and the experimental results support the conclusion that this improved methodology can be used to obtain an in-depth analysis of the text without being limited to a single domain of application. Consequently, it has the advantage of outperforming both traditional statistical and symbolic NLP methodologies.

Abstract. 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.

The ”Handbook on Ontologies ” is formed as a collection of papers written by different authors and covering different aspects of ontology engineering. As the editors claim in the preface of the book, it is supposed to provide a comprehensive overview of the state of the art and future perspectives in the field of ontologies. The term ontology is ambiguous. On the one hand it stands for a philosophical discipline studying existence; on the other hand it denotes a specific world description (conceptualisation). The Handbook speculates on the second meaning considering ontologies to be engineering artifacts. In a sense, it is an applied book destined mainly for information scientists. The Handbook is divided into four parts devoted to separate aspects of ontologies. Part 1, called Ontology Representation and Reasoning, mainly addresses formal languages and logics designed for representing and handling ontologies. The chapters of Part 2, Ontology Engineering, consider methodological aspects of ontology development and engineering. Part 3, entitled Ontology Infrastructure, mainly focuses on tools in support of the use and processing of ontologies. Finally, different application aspects are discussed in the last and largest Part 4 of the book- Ontology Applications.

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Abstract. In this paper we validate a simple method to objectively assess the results of extracting material (c.q. triples) from text corpora to build ontologies. The EU Privacy Directive has been used as corpus. Two domain experts have manually validated the results. Several experimental settings have been tried. As the evaluation scores are rather modest (sensitivity or recall: 0.5, specificity: 0.539 and precision: 0.21), we see them as a baseline reference for future experiments. Nevertheless, the human experts appreciate the automated evaluation procedure as sufficiently effective and time-saving for usage in real-life ontology modelling situations.

The problem that ontology learning deals with is the knowledge acquisition bottleneck, that is to say the difficulty to actually model the knowledge relevant to the domain of interest. Ontologies are the vehicle by which we can model and share the knowledge among various applications in a specific domain. So many research developed several ontology learning approaches and systems. In this paper, we present a survey for the different approaches in ontology learning from semi-structured and unstructured date

Abstract—The paper describes a methodology of building a semantic model of disease symptoms. The fundamental techniques used for creating the model are text analysis and learning from examples. The text analyser is used for extracting a set of symptom descriptions. The descriptions are a foundation for delivering a user interface, necessary for collecting patient cases. Given the cases a semantic model is built, which is achieved through clusterisation and statistical analysis of cases. The approach to creating the model eliminates the need of direct model manipulation, because the meaning is retrieved from association to diseases instead of purely linquistic interpretation of symptom descriptions. Detection of synonyms is also completely automatized. I.