Perspectives On Ontology Learning

Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning. Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-start in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the creation of simple taxonomical structures, as well as on problems specifically related to knowledge modeling and representation using the Web Ontology Language. Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology engineering methodologies.

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This publication advances the state-of-the-art in ontology learning by presenting a set of novel approaches to the semi-automatic acquisition, refinement and evaluation of logically complex axiomatizations. It has been motivated by the fact that the realization of the semantic web envisioned by Tim Berners-Lee is still hampered by the lack of ontological resources, while at the same time more and more applications of semantic technologies emerge from fast-growing areas such as e-business or life sciences. Such knowledge-intensive applications, requiring large scale reasoning over complex domains of interest, even more than the semantic web depend on the availability of expressive, high-quality axiomatizations. This knowledge acquisition bottleneck could be overcome by approaches to the automatic or semi-automatic construction of ontologies. Hence a huge number of ontology learning tools and frameworks have been developed in recent years, all of them aiming for the automatic or semi-automatic generation of ontologies from various kinds of data. However, both the quality and the expressivity of ontologies that can be acquired by the current state-of-the-art in ontology learning so far have failed to meet the expectations of people who argue in favor of powerful, knowledge-intensive applications based on logical inference. This work therefore takes a first, yet important, step towards the semi-automatic generation and maintenance of expressive ontologies.

"This book provides relevant theoretical foundations, and disseminates new research findings and expert views on the remaining challenges in ontology learning, discussing artificial intelligence, knowledge acquisition, knowledge representation and reasoning, text mining, information extraction, and ontology learning"--.

"This book provides relevant theoretical foundations, and disseminates new research findings and expert views on the remaining challenges in ontology learning, discussing artificial intelligence, knowledge acquisition, knowledge representation and reasoning, text mining, information extraction, and ontology learning"--

An edited collection focusing on the technology involved in enabling integration between lexical resources and semantic technologies.

A critical examination of the ways in which English is conceptualised for learning, teaching, and assessment in a range of domains, from both social and cognitive perspectives. Researchers and postgraduates working on English in L1 and L2 educational contexts will find it valuable for research and collaboration.