Ontology
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The document provides an overview of ontology, including its origins, definitions, and key concepts in artificial intelligence and information management. It discusses the importance of ontologies in enhancing communication among people and systems, addressing semantic ambiguities, and supporting knowledge sharing across various application areas. Additionally, it outlines different types of ontologies, their complexities, and their applications in fields such as medicine, e-science, and information retrieval.
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The Definitions of Ontology
Formal, explicit specification of a shared conceptualization
commonly accepted
understanding
conceptual model
of a domain
(ontological theory)
unambiguous
terminology definitions
machine-readability
with computational
semantics
[Gruber93]](https://image.slidesharecdn.com/ontology-141123090946-conversion-gate01/85/Ontology-6-320.jpg)
![Why using the ontology(2)
•Inability to use the abundant information resources on the web
The WEB has tremendous collection of useful information however getting information from the web is difficult.
Search engines are restricted to simple keyword based techniques. Interpretation of information contained in web documents is left to
the human user.
•Difficulty in Information Integration
The integration of data from various sources is a challenging task because of synonyms and homonyms.
•Problem in Knowledge Management
Multi-actor scenario involved in distributed information production and management.
“People as well as machines can‘t share knowledge if they do not speak a common language
[T. Davenport]
Ontologies provide the required conceptualizations and knowledge representation to meet these challenges.](https://image.slidesharecdn.com/ontology-141123090946-conversion-gate01/85/Ontology-10-320.jpg)
Ontology
- 1. Introduction of the Ontology Supervisor Dr. Shiri >[email protected]< Ahmed altememe <[email protected]> artificial intelligence Group Computer Science Department Amirkabir University of Technology
- 2. Overview • The Origin of Ontology • The Definitions of Ontology • Why using the Ontology • Some Examples • Benefits of Ontology • Application Areas of Ontologies • Type of ontology • Complexity of Ontologies • Some Concepts related with ontology • Some Example for language ontology • References
- 3. The Origin of Ontology • study or concern about what kinds of things exist • what entities there are in the universe. • the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things. • The term is borrowed from philosophy, (Not very useful definition for our purpose!!) • What characterizes being? • Eventually, what is being?
- 4. The Definitions of Ontology? (1) In information management and knowledge sharing place, ontology can be defined as follows: • An ontology is a set of concepts - such as things, events, and relations that are specified in some way in order to create an agreed-upon vocabulary for exchanging information. • (Tom Gruber, an AI specialist at Stanford University.) • An ontology is a vocabulary of concepts and relations rich enough to enable us to express knowledge and intention without semantic ambiguity. • Ontology describes domain knowledge and provides an agreed upon understanding of a domain. • Ontologies: are collections of statements written in a language such as RDF that define the relations between concepts and specify logical rules for reasoning about them.
- 5. The Definitions of Ontology? (3) A more formal definition is: “An ontology is a formal, explicit specification of a shared conceptualization” (Tom Gruber) • “explicit” means that “the type of concepts used and the constraints on their use are explicitly defined”; • “formal” refers to the fact that “it should be machine readable”; • “shared” refers to the fact that the knowledge represented in an ontology are agreed upon and accepted by a group”; • “conceptualization” refers to an abstract model that consists the relevant concepts and the relationships that exists in a certain situation , used to help programs and humans share knowledge." The basis of ontology is CONCEPTUALIZATION. Consider the following: The conceptualization consists of - the identified concepts (objects, events, beliefs, etc) - E.g. Concepts: disease, symptoms, treatments - the conceptual relationships that are assumed to exist and to be relevant. - E.g. Relationships: “disease causes symptoms”, “therapy treats disease”
- 6. 6 The Definitions of Ontology Formal, explicit specification of a shared conceptualization commonly accepted understanding conceptual model of a domain (ontological theory) unambiguous terminology definitions machine-readability with computational semantics [Gruber93]
- 7. World without ontology = Ambiguity Example (1) Cook? You mean •chef •information about how to cook something, •or simply a place, person, business or some other entity with "cook" in its name. The problem is that the word “cook” has no meaning, or semantic content, to the computer.
- 8. World without ontology = Ambiguity Example (2) Ambiguity for humans Cat The Vet and Grand beby associate different view for the concept cat.
- 9. Why using the ontology (1) The reason for ontologies becoming so important is that currently we lack standards (shared knowledge) which are rich in semantics and represented in machine understandable form. Ying Ding, Ontoweb Ontologies have been proposed to solve the problems that arise from using different terminology to refer to the same concept or using the same term to refer to different concepts. Howard Beck and Helena Sofia Pinto
- 10. Why using the ontology(2) •Inability to use the abundant information resources on the web The WEB has tremendous collection of useful information however getting information from the web is difficult. Search engines are restricted to simple keyword based techniques. Interpretation of information contained in web documents is left to the human user. •Difficulty in Information Integration The integration of data from various sources is a challenging task because of synonyms and homonyms. •Problem in Knowledge Management Multi-actor scenario involved in distributed information production and management. “People as well as machines can‘t share knowledge if they do not speak a common language [T. Davenport] Ontologies provide the required conceptualizations and knowledge representation to meet these challenges.
- 12. 12 Ontology Example Concept conceptual entity of the domain Attribute property of a concept Relation relationship between concepts or properties Axiom coherent description between Concepts / Properties / Relations via logical expressions name email Person isA – hierarchy (taxonomy) Student Professor attends Lecture student nr. research field topic lecture nr. holds holds(Professor, Lecture) Lecture.topic Professor.researchField
- 13. Applications of Ontologies • e-Science, e.g., Bioinformatics • Open Biomedical Ontologies Consortium (GO, MGED) • Used e.g., for “in silico” investigations relating theory and data • E.g., relating data on phosphatases to (model of) biological knowledge
- 14. Applications of Ontologies • Medicine • Building/maintaining terminologies such as Snomed, NCI & Galen
- 15. Applications of Ontologies • Organising complex and semi-structured information • UN-FAO, NASA, Ordnance Survey, General Motors, Lockheed Martin, …
- 16. Applications of Ontologies • Military/Government • DARPA, NSA, NIST, SAIC, MoD, Department of Homeland Security, … • The Semantic Web and so-called Semantic Grid
- 17. Benefits of Ontology • To facilitate communications among people and organisations aid to human communication and shared understanding by specifying meaning • To facilitate communications among systems with out semantic ambiguity. i,e to achieve inter-operability • To provide foundations to build other ontologies (reuse) • To save time and effort in building similar knowledge systems (sharing) • To make domain assumptions explicit Ontological analysis clarifies the structure of knowledge allow domain knowledge to be explicitly defined and described
- 18. Application Areas of Ontologies • Information Retrieval As a tool for intelligent search through inference mechanism instead of keyword matching Easy retrievability of information without using complicated Boolean logic Cross Language Information Retrieval Improve recall by query expansion through the synonymy relations Improve precision through Word Sense Disambiguation (identification of the relevant meaning of a word in a given context among all its possible meanings) • Digital Libraries Building dynamical catalogues from machine readable meta data Automatic indexing and annotation of web pages or documents with meaning To give context based organisation (semantic clustering) of information resources Site organization and navigational support • Information Integration Seamless integration of information from different websites and databases • Knowledge Engineering and Management As a knowledge management tools for selective semantic access (meaning oriented access) Guided discovery of knowledge • Natural Language Processing Better machine translation Queries using natural language
- 19. Types of Ontologies • Top level ontology this type of ontology describes very general concepts or common sense knowledge such as space, time, event, action, etc. These concepts are independent of a problem or a particular area. • Domain ontology this ontology governs a set of vocabularies and concepts describing an application domain or the target world. It characterizes the knowledge of the area where the task is performed. Most existing ontologies are domain ontologies. • Task ontology this type of ontology is used to conceptualize specific tasks in systems. It governs a set of vocabularies and concepts describing a structure of performing the tasks domain-independent. • Application ontology this ontology is the most specific. The concepts in the application ontology are very domain specific and particular application. In other words, the concepts often correspond to roles played by domain entities while performing a certain activity.
- 20. Classification of ontologies according to the object of conceptualization
- 21. Complexity of Ontologies Depending on the wide range of tasks to which the ontologies are put ontologies can vary in their complexity Ontologies range from simple taxonomies to highly tangled networks including constraints associated with concepts and relations. •Light-weight Ontology • concepts • ‘is-a’ hierarchy among concepts • relations between concepts •Heavy-weight Ontology • cardinality constraints • taxonomy of relations • Axioms (restrictions)
- 22. Concepts related with ontology There are at least 40 terms or concepts across these various disciplines Concept related with Ontology
- 23. 23 RDF and RDFS • RDF stands for Resource Description Framework • is a W3C standard, which provides tool to describe Web resources • provides interoperability between applications that exchange machine-understandable information • RDFS extends RDF with “schema vocabulary”.
- 24. 24 RDF Example • Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila <rdf:RDF> <rdf:Description about= "http://www.w3.org/Home/Lassila"> <s:Creator>Ora Lassila</s:Creator> </rdf:Description> </rdf:RDF>
- 25. 25 OWL became standard 10 February 2004 the World Wide Web Consortium announced final approval of two key Semantic Web technologies, the revised Resource Description Framework (RDF) and the Web Ontology Language (OWL).
- 26. 26 OWL Example There are two types of animals, Male and Female. <rdfs:Class rdf:ID="Male"> <rdfs:subClassOf rdf:resource="#Animal"/> </rdfs:Class> The subClassOf element asserts that its subject - Male - is a subclass of its object -- the resource identified by #Animal. <rdfs:Class rdf:ID="Female"> <rdfs:subClassOf rdf:resource="#Animal"/> <owl:disjointWith rdf:resource="#Male"/> </rdfs:Class> Some animals are Female, too, but nothing can be both Male and Female (in this ontology) because these two classes are disjoint (using the disjointWith tag).
- 28. refrance • http://www.jfsowa.com/ontology/index.htm • http://techwiki.openstructs.org/index.php/A_Basic_Guide_to_Ontolo gies#Overview_and_Role_of_Ontologies • http://www.slideshare.net/athmanhajhamou/use-of-ontologies-in-natural- language-processing-2 • http://www.intechopen.com/books/ehealth-and-remote-monitoring/ ontological-architecture-for-management-of-telemonitoring- system-and-alerts-detection
- 29. Thank you for your attention Any questions , comments?