Learning OWL Class Expressions
Learning OWL Class Expressions

Author: J. Lehmann

Publisher: IOS Press

Published: 2010-04-02

Total Pages: 279

ISBN-13: 1614993408

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With the advent of the Semantic Web and Semantic Technologies, ontologies have become one of the most prominent paradigms for knowledge representation and reasoning. However, recent progress in the field faces a lack of well structured ontologies with large amounts of instance data due to the fact that engineering such ontologies requires a considerable investment of resources. Nowadays, knowledge bases often provide large volumes of data without sophisticated schemata. Hence, methods for automated schema acquisition and maintenance are sought. Schema acquisition is closely related to solving typical classification problems in machine learning, e.g. the detection of chemical compounds causing cancer. In this work, we investigate both, the underlying machine learning techniques and their application to knowledge acquisition in the Semantic Web.

Knowledge Graphs and Semantic Web
Knowledge Graphs and Semantic Web

Author: Boris Villazón-Terrazas

Publisher: Springer Nature

Published: 2020-12-09

Total Pages: 225

ISBN-13: 3030653846

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This book constitutes the thoroughly refereed proceedings of the Second Iberoamerican Conference, KGSWC 2020, held in Mérida, Mexico, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 15 papers presented were carefully reviewed and selected from 45 submissions. The papers cover research and practices in several fields of AI, such as knowledge representation and reasoning, natural language processing/text mining, machine/deep learning, semantic web, and knowledge graphs.

Exploiting Linked Data and Knowledge Graphs in Large Organisations
Exploiting Linked Data and Knowledge Graphs in Large Organisations

Author: Jeff Z. Pan

Publisher: Springer

Published: 2017-01-24

Total Pages: 281

ISBN-13: 3319456547

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This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and “standard” data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps. It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.

Reasoning Web. Reasoning and the Web in the Big Data Era
Reasoning Web. Reasoning and the Web in the Big Data Era

Author: Manolis Koubarakis

Publisher: Springer

Published: 2014-09-03

Total Pages: 397

ISBN-13: 3319105876

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This volume contains the lecture notes of the 10th Reasoning Web Summer School 2014, held in Athens, Greece, in September 2014. In 2014, the lecture program of the Reasoning Web introduces students to recent advances in big data aspects of semantic web and linked data, and the fundamentals of reasoning techniques that can be used to tackle big data applications.

Semantic Web
Semantic Web

Author: Amit Sheth

Publisher: IGI Global

Published: 2013-03-31

Total Pages: 361

ISBN-13: 1466636114

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Semantic web continues to be an increasingly important system for allowing end-users to share and communicate information online. Semantic Web: Ontology and Knowledge Base Enabled Tools, Services and Application focuses on the information systems discipline and the tools and techniques utilized for the emerging use of semantic web. Covering topics on semantic search, ontologies, and recommendation systems, this publication is essential for academics, practitioners, and industry professionals.

Emerging Technologies for Education
Emerging Technologies for Education

Author: Ting-Ting Wu

Publisher: Springer

Published: 2017-02-17

Total Pages: 755

ISBN-13: 331952836X

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This book constitutes the thoroughly refereed post-workshop proceedings of the First International Symposium, SETE 2016, held in conjunction with ICWL 2016, Rome, Italy, in October 2016. The 81 revised papers, 59 full and 22 short ones, were carefully reviewed and selected from 139 submission. They cover latest findings in various areas, such as emerging technologies for open access to education and learning; emerging technologies supported personalized and adaptive learning; emerging technologies support for intelligent tutoring; emerging technologies support for game-based and joyful learning; emerging technologies of pedagogical issues; emerging technologies for affective learning and emerging technologies for tangible learning.

Reasoning Web. Semantic Technologies for Intelligent Data Access
Reasoning Web. Semantic Technologies for Intelligent Data Access

Author: Sebastian Rudolph

Publisher: Springer

Published: 2013-07-22

Total Pages: 293

ISBN-13: 3642397840

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This volume contains the lecture notes of the 9th Reasoning Web Summer School 2013, held in Mannheim, Germany, in July/August 2013. The 2013 summer school program covered diverse aspects of Web reasoning, ranging from scalable lightweight formalisms such as RDF to more expressive ontology languages based on description logics. It also featured foundational reasoning techniques used in answer set programming and ontology-based data access as well as emerging topics like geo-spatial information handling and reasoning-driven information extraction and integration.

Perspectives on Ontology Learning
Perspectives on Ontology Learning

Author: J. Lehmann

Publisher: IOS Press

Published: 2014-04-03

Total Pages: 299

ISBN-13: 1614993793

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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.

Inductive Logic Programming
Inductive Logic Programming

Author: Dimitar Kazakov

Publisher: Springer Nature

Published: 2020-06-05

Total Pages: 154

ISBN-13: 3030492109

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This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.