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The Nature of Statistical Learning Theory

by Vladimir N. Vapnik , 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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on the developed theory were proposed. This made statistical learning theory not only a tool for the theoretical analysis but also a tool for creating practical algorithms for estimating multidimensional functions. This article presents a very general overview of statistical learning theory including both

Ontology Learning for the Semantic Web

by Er Maedche, Steffen Staab - IEEE Intelligent Systems , 2001
"... 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 o ..."
Abstract - Cited by 492 (16 self) - Add to MetaCart
of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, semi

Towards a Standard Upper Ontology

by Ian Niles, Adam Pease , 2001
"... The Suggested Upper Merged Ontology (SUMO) is an upper level ontology that has been proposed as a starter document for The Standard Upper Ontology Working Group, an IEEE-sanctioned working group of collaborators from the fields of engineering, philosophy, and information science. The SUMO provides d ..."
Abstract - Cited by 589 (22 self) - Add to MetaCart
The Suggested Upper Merged Ontology (SUMO) is an upper level ontology that has been proposed as a starter document for The Standard Upper Ontology Working Group, an IEEE-sanctioned working group of collaborators from the fields of engineering, philosophy, and information science. The SUMO provides

Ontology Development 101: A Guide to Creating Your First Ontology

by Natalya F. Noy, Deborah L. Mcguinness , 2001
"... In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web ..."
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In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World

Conversation as Experiential Learning

by Ann C. Baker, Patricia J. Jensen, David A. Kolb , 2005
"... This article proposes a framework relevant to the continuous learning of individuals and organizations. Drawing from the theory of experiential learning, the article proposes conversational learning as the experiential learning process occurring in conversation as learners construct meaning from t ..."
Abstract - Cited by 588 (9 self) - Add to MetaCart
their experiences. A theoretical framework based on five process dialectics is proposed here as the foundational under-pinning of conversational learning. The five dialectics—apprehension and comprehension; reflection and action; epistemological discourse and ontological recourse; individuality and relationality

A translation approach to portable ontology specifications

by Thomas R. Gruber - KNOWLEDGE ACQUISITION , 1993
"... To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions ..."
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To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations

PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

by Natalya Fridman Noy, Mark A. Musen , 2000
"... Researchers in the ontology-design field have developed the content for ontologies in many domain areas. Recently, ontologies have become increasingly common on the WorldWide Web where they provide semantics for annotations in Web pages. This distributed nature of ontology development has led t ..."
Abstract - Cited by 503 (12 self) - Add to MetaCart
to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. The processes of ontology alignment and merging are usually handled manually and often constitute a large and tedious portion

Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce

by Dieter Fensel , 2007
"... Currently computers are changing from single isolated devices to entry points into a world wide network of information exchange and business transactions called the World Wide Web (WWW). Therefore support in the exchange of data, information, and knowledge exchange is becoming the key issue in cur ..."
Abstract - Cited by 656 (45 self) - Add to MetaCart
in current computer technology. Ontologies provide a shared and common understanding of a domain that can be communicated between people and application systems. Therefore, they may play a major role in supporting information exchange processes in various areas. This book discusses the role ontologies

Multitask Learning,”

by Rich Caruana , Lorien Pratt , Sebastian Thrun , 1997
"... Abstract. Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for ..."
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Abstract. Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned

Ontologies are us: A unified model of social networks and semantics

by Peter Mika - In International Semantic Web Conference , 2005
"... Abstract. On the Semantic Web ontologies are most commonly treated as artifacts created by knowledge engineers for a particular community. The task of the engineers is to forge a common understanding within the community and to formalize the agreements, prerequisites of reusing domain knowledge in i ..."
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Abstract. On the Semantic Web ontologies are most commonly treated as artifacts created by knowledge engineers for a particular community. The task of the engineers is to forge a common understanding within the community and to formalize the agreements, prerequisites of reusing domain knowledge
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