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Conceptualization (information Science)
In information science a conceptualization is an abstract simplified view of some selected parts of the world, containing the objects, concepts, and other entities that are presumed of interest for some particular purpose and the relationships between them. An explicit specification of a conceptualization is an ontology (information science), ontology, and it may occur that a conceptualization can be realized by several distinct ontologies. An ''ontological commitment'' in describing ontological comparisons is taken to refer to that subset of elements of an ontology shared with all the others. "An ontology is ''language-dependent''", its objects and interrelations described within the language it uses, while a conceptualization is always the same, more general, its concepts existing "independently of the language used to describe it". The relation between these terms is shown in the figure to the right. Not all workers in knowledge engineering use the term "conceptualization", b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontological Commitments
Ontology is the philosophical study of existence, being. It is traditionally understood as the subdiscipline of metaphysics focused on the most general features of reality. As one of the most fundamental concepts, being encompasses all of reality and every entity within it. To articulate the basic structure of being, ontology examines the commonalities among all things and investigates their classification into basic types, such as the Theory of categories, categories of particulars and Universal (metaphysics), universals. Particulars are unique, non-repeatable entities, such as the person Socrates, whereas universals are general, repeatable entities, like the color ''green''. Another distinction exists between Abstract and concrete, concrete objects existing in space and time, such as a tree, and abstract objects existing outside space and time, like the number 7. Systems of categories aim to provide a comprehensive inventory of reality by employing categories such as Substance t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology Alignment
Ontology alignment, or ontology matching, is the process of determining correspondences between concepts in ontologies. A set of correspondences is also called an alignment. The phrase takes on a slightly different meaning, in computer science, cognitive science or philosophy. Computer science For computer scientists, concepts are expressed as labels for data. Historically, the need for ontology alignment arose out of the need to integrate heterogeneous databases, ones developed independently and thus each having their own data vocabulary. In the Semantic Web context involving many actors providing their own ontologies, ontology matching has taken a critical place for helping heterogeneous resources to interoperate. Ontology alignment tools find classes of data that are semantically equivalent, for example, "truck" and "lorry". The classes are not necessarily logically identical. According to Euzenat and Shvaiko (2007),Jérôme Euzenat and Pavel Shvaiko. 2013Ontology matching ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation
Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of ''reasoning''. Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom system ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Engineering
Knowledge engineering (KE) refers to all aspects involved in knowledge-based systems. Background Expert systems One of the first examples of an expert system was MYCIN, an application to perform medical diagnosis. In the MYCIN example, the domain experts were medical doctors and the knowledge represented was their expertise in diagnosis. Expert systems were first developed in artificial intelligence laboratories as an attempt to understand complex human decision making. Based on positive results from these initial prototypes, the technology was adopted by the US business community (and later worldwide) in the 1980s. The Stanford heuristic programming project led by Edward Feigenbaum was one of the leaders in defining and developing the first expert systems. History In the earliest days of expert systems, there was little or no formal process for the creation of the software. Researchers just sat down with domain experts and started programming, often developing the requi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Acquisition
Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies. Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems. Researchers at Stanford and other AI laboratories worked with doctors and other highly skilled experts to develop systems that could automate complex tasks such as medical diagnosis. Until this point computers had mostly been used to automate highly data intensive tasks but not for complex reasoning. Technologies such as inference engines allowed developers for the first time to tackle more complex problems. As expert systems scaled up from demonstration prototypes to indust ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology
Ontology is the philosophical study of existence, being. It is traditionally understood as the subdiscipline of metaphysics focused on the most general features of reality. As one of the most fundamental concepts, being encompasses all of reality and every entity within it. To articulate the basic structure of being, ontology examines the commonalities among all things and investigates their classification into basic types, such as the Theory of categories, categories of particulars and Universal (metaphysics), universals. Particulars are unique, non-repeatable entities, such as the person Socrates, whereas universals are general, repeatable entities, like the color ''green''. Another distinction exists between Abstract and concrete, concrete objects existing in space and time, such as a tree, and abstract objects existing outside space and time, like the number 7. Systems of categories aim to provide a comprehensive inventory of reality by employing categories such as Substance t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Translation
Semantic translation is the process of using semantic information to aid in the translation of data in one representation or data model to another representation or data model. Semantic translation takes advantage of semantics that associate meaning with individual data elements in one dictionary to create an equivalent meaning in a second system. An example of semantic translation is the conversion of XML data from one data model to a second data model using formal ontologies for each system such as the Web Ontology Language (OWL). This is frequently required by intelligent agents that wish to perform searches on remote computer systems that use different data models to store their data elements. The process of allowing a single user to search multiple systems with a single search request is also known as federated search. Semantic translation should be differentiated from data mapping tools that do simple one-to-one translation of data from one system to another without actu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Matching
Semantic matching is a technique used in computer science to identify information that is semantically related. Given any two graph-like structures, e.g. classifications, taxonomies database or XML schemas and ontologies, matching is an operator which identifies those nodes in the two structures which semantically correspond to one another. For example, applied to file systems, it can determine that a folder labeled "car" is semantically equivalent to another folder "automobile" because they are synonyms in English. This information can be taken from a linguistic resource like WordNet. In recent years many of them have been offered. S-Match is an example of a ''semantic matching operator''. It works on lightweight ontologies, namely graph structures where each node is labeled by a natural language sentence, for example in English. These sentences are translated into a formal logical formula (according to an artificial unambiguous language) codifying the meaning of the node takin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Semantic Integration
Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information (physical, psychological, and social), documents of all sorts, contacts (including social graphs), search results, and advertising and marketing relevance derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value. Applications and methods In enterprise application integration (EAI), semantic integration can facilitate or even automate the communication between computer systems using metadata publishing. Metadata publishing potentially offers the ability to automatically link ontology (computer science), ontologies. One approach to (semi-)automated ontology mapping requires the definition of a semantic distance or its inverse, semantic similarit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ontology (information Science)
In information science, an ontology encompasses a representation, formal naming, and definitions of the categories, properties, and relations between the concepts, data, or entities that pertain to one, many, or all domains of discourse. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as ''applied ontology''. Every academic discipline or field, in creating its terminology, thereby lays the groundwork for an ontology. Each uses ontological assumptions to frame explicit theories, research and applications. Improved ontologies may improve problem solving within that domain, interoperability of data systems, and discoverability of data. Translating research papers within every field is a problem made easier when experts from different countries mainta ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Representation And Reasoning
Knowledge representation (KR) aims to model information in a structured manner to formally represent it as knowledge in knowledge-based systems whereas knowledge representation and reasoning (KRR, KR&R, or KR²) also aims to understand, reason, and interpret knowledge. KRR is widely used in the field of artificial intelligence (AI) with the goal to represent information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog. KR incorporates findings from psychology about how humans solve problems and represent knowledge, in order to design formalisms that make complex systems easier to design and build. KRR also incorporates findings from logic to automate various kinds of ''reasoning''. Traditional KRR focuses more on the declarative representation of knowledge. Related knowledge representation formalisms mainly include vocabularies, thesaurus, semantic networks, axiom system ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
