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Sunday, April 26, 2020 | History

4 edition of Ontology learning for the semantic Web found in the catalog.

Ontology learning for the semantic Web

Alexander Maedche

Ontology learning for the semantic Web

  • 346 Want to read
  • 13 Currently reading

Published by Kluwer Academic Publishers in Boston .
Written in English

    Subjects:
  • Web site development.,
  • Metadata.,
  • Ontology.,
  • Artificial intelligence.,
  • Semantic Web.,
  • Knowledge acquisition (Expert systems)

  • Edition Notes

    Includes bibliographical references (p. [229]-241) and index.

    Statementby Alexander Maedche.
    SeriesThe Kluwer international series in engineering and computer science -- SECS 665
    Classifications
    LC ClassificationsTK5105.888 .M33 2002
    The Physical Object
    Paginationxxiii, 244 p. :
    Number of Pages244
    ID Numbers
    Open LibraryOL20644680M
    ISBN 100792376560
    LC Control Number2001058188

    Towards a Better Web! •The Semantic Web vision articulated in a Scientific American article by Tim Berners-Lee, James Hendler and Ora Lassila (May ). – “The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where agents roaming from page to page readily carry out sophisticated tasks for File Size: KB.


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Ontology learning for the semantic Web by Alexander Maedche Download PDF EPUB FB2

Ontology Learning for the Semantic Web is designed for researchers and developers of semantic web applications. It also serves as an excellent supplemental reference to advanced level courses in ontologies and the semantic web. The Amazon Book Review Author interviews, book reviews, editors' picks, and more.

3/5(2). Books on Semantic Web: Intro. This page contains information on books that are strictly on the Semantic Web and Linked are, of course, lots of other books on Knowledge Representation, Logic, XML, Databases, etc, that are all relevant for the Semantic Web, but adding these to this list would be counter productive.

Ontology Learning for the Semantic Web Article (PDF Available) in Intelligent Systems, IEEE 16(2) March with 1, Reads How we measure 'reads'. With this book, the promise of the Semantic Web - in which machines can find, share, and combine data on the Web - is not just a technical possibility, but a practical reality Programming the Semantic Web demonstrates several ways to implement semantic web applications, using current and emerging standards and by: Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support Read more.

This book is intended for undergraduate engineering students who are interested in exploring the technology of Semantic web. The book simplifies the tough concepts associated with Semantic web and hence it can be considered as the base to build the knowledge about Web /5(11).

The OWL Standard and Ontology Modelling. In recent years, there has been an uptake of expressing ontologies using ontology languages such as the Web Ontology Language (OWL).

OWL is a semantic web computational logic-based language, designed to represent rich and complex knowledge about things and the relations between them.

Summary. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The notion of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured and semi-structured data in order to support a semi-automatic, cooperative ontology engineering process.

Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies.

The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary Brand: Springer US. Ontology learning (ontology extraction, ontology generation, or ontology acquisition) is the automatic or semi-automatic creation of ontologies, including extracting the corresponding domain's terms and the relationships between the concepts that these terms represent from a corpus of natural language text, and encoding them with an ontology language for easy retrieval.

Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances provides relevant theoretical foundations, and disseminates new research findings and expert views on the remaining challenges in ontology learning. This book is invaluable resource as a library or personal reference for graduate students, researchers, and.

mated ontology learning from domain text. It is the only system,as far as we know,that uses natural lan-guage processing and machine learning techniques, and is part of a more general ontology engineering architecture.4,5 Here, we describe the system and an experiment in which we used a machine-learned tourism ontology to automatically File Size: KB.

With this book, the promise of the Semantic Web -- in which machines can find, share, and combine data on the Web -- is not just a technical possibility, but a practical reality Programming the Semantic Web demonstrates several ways to implement semantic web applications, using current and emerging standards and technologies.

You'll learn how to incorporate existing. An Architecture for Ontology Learning Given the task of constructing and maintaining an ontology for a Semantic Web application, e.g. for an ontology-based knowledge portal that we have been dealing with (cf. [10]), we have produced a wish list of what kind of support we would fancy.

Ontology Engineering Workbench OntoEdit. Haase, P. and Völker, J. Ontology learning and reasoning - dealing with uncertainty and inconsistency.

In In Proceedings of the Workshop on Uncertainty Reasoning for the Semantic Web (URSW, pages 45– Ícaro Medeiros (CIn - UFPE) Ontology Learning Septem 55 / 57 The W3C Web Ontology Language (OWL) is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.

OWL is a computational logic-based language such that knowledge expressed in OWL can be exploited by computer programs, e.g., to verify the consistency of that knowledge or. Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies.

The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary. An Introduction to Ontology Learning Jens LEHMANNa and Johanna VÖLKERb;1 a Informatics Institute, University of Leipzig, Germany b Data & Web Science Research Group, University of Mannheim, Germany Ever since the early days of Artificial Intelligence and the development of the first knowledge-based systems in the 70s [32] people have dreamt of self-learning File Size: KB.

While all the other answers provided by Marin Dimitrov, Kendall Clark and Phillip Rhodes suggest excellent resources, here are few more that I found useful both from a technical and business perspective: BOOKS: 1. Introduction to Semantic Web an.

Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies.

The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a Author: Alexander Maedche. The Semantic Web Ontology Learning for the Semantic Web Alexander Maedche and Steffen Staab, University of Karlsruhe The Semantic Web relies heavily on formal ontologies to structure data for com-prehensive and transportable machine understanding.

Thus, the proliferation of ontologies factors largely in the Semantic Web’s Size: KB. Books shelved as semantic-web: Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL by Dean Allemang, Learning SPARQL by Bob DuCha.

Semantic Web Technologies provides a comprehensive overview of key semantic knowledge technologies and research. The authors explain (semi-)automatic ontology generation and metadata extraction in depth, along with ontology management and mediation.

Describes methods for ontology learning and metadata generation. exampled-based book on. The Semantic Web is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C).

The goal of the Semantic Web is to make Internet data machine-readable. To enable the encoding of semantics with the data, technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL) are used.

These. • Semantic Web aims to make web content more accessible to automated processes – Adds semantic annotations to web resources • Ontologies provide vocabulary for annotations – Terms have well defined meaning • OWL ontology language based on (description) logic – Exploits results of basic research on complexity, reasoning, Size: 2MB.

Taxonomy vs Ontology: Machine Learning Breakthroughs By Michelle Knight on Octo Octo The difference between Taxonomy vs Ontology is a topic that often perplexes even the most seasoned data professionals, Data Scientists, Data Analysts, and many a technology writer.

This article discusses the area of ontologies and semantic web technologies in E-Learning and compares the state of research in years and It. Semantic = meaning Semantic web = meaning + web Web + Data base + Knowledge Representation O extension of the current web technology O a web where the focus is placed on the meaning of words O a metadata based infrastructure [1]Semantic Web is a group of methods and technologies to allow of machines to understand the meaning or "semantics.

Ontology is an explicit specification of conceptualization. Ontology is a body of knowledge describing some domain, typically common sense knowledge domain. The definition 1 is the meaning in philosophy as we have discussed above, however it. Semantic search + ontology is a bit of an oxymoron. A semantic search ontology is a static list used to, in a semi-automatic fashion, expand the meaning of a particular concept.

So, searching for "java"on a system with an ontology might expand tha. Ontology Learning for the Semantic Web by Alexander Maedche,available at Book Depository with free delivery worldwide.4/5(1). This book series reports on the state-of-the-art in foundations, methods and applications of Semantic Web and its underlying technologies.

It is a central forum for the communication of recent developments and comprises research monographs, textbooks and edited volumes on all topics related to the Semantic Web. 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. Machine Learning Methods of Mapping Semantic Web Ontologies Caden Howell [email protected] Novem Abstract This paper is an overview of the application of machine learning to ontology mapping at a high level.

This paper introduces ontologies and ontology research for the Semantic Web. It compares several. Semantic Web Technologies A set of technologies and frameworks that enable the Web of Data: Resource Description Framework (RDF) A variety of data interchange formats (e.g.

RDF/XML,N3,Turtle,N-Triples) Notations such as RDF Schema (RDFS) and the Web Ontology Language (OWL) All are intended to provide a formal. Semantic Web, Ontology, and Linked Data: /ch Enormous amount of information is being produced every day and get consumed according to the needs of human being.

Semantic web and ontology representCited by: 1. KR4SW – Winter – Pascal Hitzler Knowledge Representation for the Semantic Web Winter Quarter Slides 1 – 01/05/ Pascal Hitzler.

This book, motivated by the Ontology tutorial given for many years at what was originally the Semantic Technology Conference (SemTech) and then later from a semester-long university class, is designed to provide the foundations for ontology engineering. The book can serve as a course textbook or a primer for all those interested in ontologies.

This book will help you: Learn how the Semantic Web allows new and unexpected uses of data to emerge Understand how semantic technologies promote data portability with a simple, abstract model for knowledge representation Become familiar with semantic standards, such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL.

“ontology-learning layer cake,” clearly influenced by Tim Berners-Lee’s Semantic Web layer cake, which starts with terms as the foundation and works up through synonyms, concepts, concept hierarchies, and relations to rules at the top. The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data.

This data is necessary in order to support a .Ontology learning for the semantic web.ontology mapping is crucial to the success of the Semantic Web [34]. 2 Overview of Our Solution In response to the challenge of ontology matching on the Semantic Web and in numerous other application contexts, we have developed the GLUE system, which applies machine learning techniques to semi-automatically create se-mantic Size: KB.