Ontology

本体论是一种正式的规范说明,旨在为不同应用程序之间的概念理解提供共享基础。其核心在于定义概念化的明确形式规范,并通过机器可读的语言表达。主要应用于语义网的搜索、信息集成及知识管理等领域。

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Ontology

2007-3-23

l       Definition

n         explicit formal specification of a conceptualization, intended for sharing among different applications and expressed in a language

u       formal: machine readable

u       conceptualization: semantic

u       sharing: goal

l       Language

n         OWL: developed from DAML+OIL

n         Logic base: DL

n         Language base:RDF

l       Construct

n         Tool

n         Methodology

n         Maintenance

l       Application

n         Semantic web :semantic annotation ,used for searching

n         Information integration: ontology mapping, transform information to ontology, theoretical and empirical basis

n         knowledge management : knowledge representation, query

n         Semantic web service : semantic web + web service

l        Mapping

n         algorithm :syntactic, semantic ,machine learning , top-level ontology

n         tools: semi-automatically

n         Schema mapping: similar

l       Summary

n         Language and mapping techniques are base

n         Integration and merging are methods

n         Search and data access are goals

n         Direction:

u       find good methods and build good tools in the ontology domain

u       use these tools to facilitate other research

### Ontology in Computer Science and Data Modeling In the context of computer science and data modeling, an ontology is a formal representation of knowledge as a set of concepts within a domain and the relationships between those concepts. It provides a structured framework for organizing information that can be used by machines to understand complex domains and perform reasoning tasks[^1]. An ontology typically includes: - **Classes (or Concepts)**: These represent categories or types of objects within the domain. - **Properties (or Attributes)**: Characteristics or features associated with classes. - **Relationships**: Links between different entities or classes, defining how they interact or relate to one another. The use of ontologies in artificial intelligence and semantic web technologies allows systems to interpret data more effectively. For instance, when applied to lifelong learning environments, ontologies help manage evolving datasets where new classes are introduced over time while preserving existing ones[^2]. Here’s a simple example illustrating how you might define part of an ontology using OWL (Web Ontology Language): ```owl Prefix(:=<http://www.semanticweb.org/user/ontologies/example#>) Prefix(owl:=<http://www.w3.org/2002/07/owl#>) Ontology(<http://www.semanticweb.org/user/ontologies/example> Declaration(Class(:Animal)) Declaration(Class(:Mammal)) SubClassOf(:Mammal :Animal) Declaration(Class(:Dog)) SubClassOf(:Dog :Mammal) ) ``` This snippet defines basic hierarchical relationships among `Animal`, `Mammal`, and `Dog`.
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