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Ontology Learning and Knowledge Discovery Using the Web

Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances provides relevant theoretical foundations, and;

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Semantic Systems. The Power of AI and Knowledge Graphs

submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information;

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Ontology Learning and Population from Text

web. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications discusses ontologies for the semantic web, as well as;

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Semantic Web Technologies

The Semantic Web combines the descriptive languages RDF (Resource Description Framework) and OWL (Web Ontology Language), with the data;

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Knowledge Seeker - Ontology Modelling for Information Search and Management

that transforms the learning outcome to the Ontology Graph format for machine processing and also can be visualized for human validation;

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Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi;

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Fuzzy Knowledge Management for the Semantic Web

Semantic Web ontology mapping, and fuzzy rules and their interchange in the Semantic Web. The book aims to provide a single record of current;

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Fuzzy Knowledge Management for the Semantic Web

Semantic Web ontology mapping, and fuzzy rules and their interchange in the Semantic Web. The book aims to provide a single record of current;

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Machine Learning for Predictive Analysis

intelligence, intelligent information system development using design science principles, intelligent web mining and knowledge discovery systems.;

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Knowledge Discovery Enhanced with Semantic and Social Information

There is generalagreementthat the quality of Machine Learning and Kno- edgeDiscoveryoutputstronglydependsnotonlyonthequalityofsourcedata;

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Data Integration in the Life Sciences

submissions. The papers cover a range of important topics such as algorithms for ontology matching, interoperable frameworks for text mining using;

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Semantic Knowledge Management

applications in Semantic Web technologies. It serves the fundamental interests of researchers and developers in this field in both academia and;

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Knowledge Discovery, Knowledge Engineering and Knowledge Management

selected from 167 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering;

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Knowledge Discovery, Knowledge Engineering and Knowledge Management

selected from 220 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering;

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Knowledge Discovery, Knowledge Engineering and Knowledge Management

selected from 287 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering;

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Applied Semantic Web Technologies

integrates theory with case studies to illustrate the history, current state, and future direction of the semantic web. It maintains an emphasis on;

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Ontology Learning from Text

evaluation methods for ontology learning, aiming at defining procedures and metrics for a quantitative evaluation of the ontology learning task; and;

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Knowledge Discovery, Knowledge Engineering and Knowledge Management

reviewed and selected from 157 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval;

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Ontological Engineering approach of developing Ontology of Information Science

(IS) is one of these areas that need a unified ontology model to facilitate information access among the heterogeneous data resources and share a;

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Teaching Beginning Web Page Design

knowledge is need on the part of the teacher as this course relies on student self discovery and research skills to complete course.;

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Description of Logic Framework to Synchronize Orm Model and Owl Langua

ontologies using the web ontology language. The logical foundation of the ontology language (Description Logics) is represented in order to;

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Reasoning Web. Declarative Artificial Intelligence

various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The;

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Hierarchical Feature Selection for Knowledge Discovery

This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases;

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Mining User Navigation Patterns from Web Access Log Files

Since the Web is the biggest and most widely known information source that is easily accessible and searchable, on the positive side, there;

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Extracting Knowledge From Opinion Mining

, including the differences between ontology-based opinion mining and feature-based opinion mining, this book is an ideal reference source for;

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Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. First Edition

information, information retrieval, integration of databases, b- informatics, and education; and in new emerging fields like the Semantic Web. Primary;

Vergelijkbare producten zoals Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. First Edition

Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. First Edition

information, information retrieval, integration of databases, b- informatics, and education; and in new emerging fields like the Semantic Web. Primary;

Vergelijkbare producten zoals Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. First Edition