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Proceedings ArticleDOI

Semantic data mining: A survey of ontology-based approaches

TLDR
This survey paper investigates why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process.
Abstract
Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.

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User Modeling and User-Adapted Interaction

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Semantic Web in data mining and knowledge discovery

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Ontology-based Deep Learning for Human Behavior Prediction with Explanations in Health Social Networks.

TL;DR: An ontology-based deep learning model (ORBM+) for human behavior prediction over undirected and nodes-attributed graphs is proposed, which extends a well-known deep learning method, the Restricted Boltzmann Machine.
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An automatic literature knowledge graph and reasoning network modeling framework based on ontology and natural language processing

TL;DR: An automatic literature knowledge graph and reasoning network modeling framework based on ontology and Natural Language Processing is proposed, to facilitate the efficient knowledge exploration from literature abstract to help cover all aspects of the literature using the traditional manual review approach.
Proceedings ArticleDOI

Ontology-based Recommender System in Higher Education

TL;DR: The main objective of the ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one.
References
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Book

Data Mining: Concepts and Techniques

TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Book

Artificial Intelligence: A Modern Approach

TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Journal ArticleDOI

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI

Toward principles for the design of ontologies used for knowledge sharing

TL;DR: The role of ontology in supporting knowledge sharing activities is described, and a set of criteria to guide the development of ontologies for these purposes are presented, and it is shown how these criteria are applied in case studies from the design ofOntologies for engineering mathematics and bibliographic data.
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