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  • 1. The Basics of Ontologies Nordic Agricultural Ontology Service (AOS) Workshop Royal Veterinary and Agricultural University Copenhagen, Denmark February 28, 2003 Frehiwot Fisseha
  • 2. What this talk is all about
      • The Origin of Ontology
      • The Definitions of Ontology
      • Motivation for Developing Ontology
      • Some Examples
      • Benefits of Ontology
      • Application Areas of Ontology
      • Types of Ontology
      • Similarities and Differences of Ontologies and Thesauri
      • Things to keep in mind
      • Conclusion
  • 3.
    • The term “ ontology ” has been used for a number of years by the artificial intelligence and knowledge representation community but is now becoming part of the standard terminology of a much wider community including information systems modelling.
    • The term is borrowed from philosophy , where ontology mean ‘a systematic account of existence’. (Not very useful definition for our purpose!!)
    The Origin of Ontology
  • 4. What is Ontology? (1)
    • An ontology is "the specification of conceptualizations, used to help programs and humans share knowledge."
    • 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.)
    • Ontologies establish a joint terminology between members of a community of interest. These members can be human or automated agents.
  • 5.
    • In information management and knowledge sharing arena, ontology can be defined as follows:
      • 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.
        • Computers will "understand" the meaning of semantic data on a web page by following links to specified ontologies.
    What is Ontology? (2)
  • 6. What is 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
      • 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, therapy
        • the conceptual relationships that are assumed to exist and to be relevant.
          • E.g. Relationships : “disease causes symptoms”, “therapy treats disease”
  • 7. World without ontology = Ambiguity Example (1)
    • Ambiguity for computer
    • Rice?
      • International Rice Research Institute
      • Rice Research Program
      • Rice Carrier Service Center
      • Africa Rice Center
      • Rice University
    • 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 “ rice “ or “ 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 ma associate different view for the concept cat.
  • 9. Motivation (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. Motivation (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.
  • 11. Motivation (3)
    • Database-style queries are effective
      • Find red cars, 1993 or newer, < $5,000
        • Select * From Car Where Color=“red” And Year >= 1993 And Price < 5000
    • Web is not a database
      • Uses keyword search
      • Retrieves documents, not records
    • Ontologies provide the required knowledge and representation to search the web in a database fashion through implicit Boolean search.
  • 12. What do ontologies look like?
  • 13. Example: Car-Ad Ontology Year Price Make Mileage Model Feature PhoneNr Extension Car has has has has is for has has has 1..* 0..1 1..* 1..* 1..* 1..* 1..* 1..* 0..1 0..1 0..1 0..1 0..1 0..1 0..* 1..* Graphical Car [0:1] has Year [1:*]; Year {regexp[2]: “d{2} : b’d{2}b, … }; Car [0:1] has Make [1:*]; Make {regexp[10]: “bchevb”, “bchevyb”, … }; Car [0:1] has Model [1:*]; Model {…}; Car [0:1] has Mileage [1:*]; Mileage {regexp[8] “b[1-9]d{1,2}k”, “ 1-9]d?,d{3} : [^$d][1-9]d?,d{3}[^d]” } {context: “bmilesb”, “bmi.”, “bmib”}; Car [0:*] has Feature [1:*]; Feature {regexp[20]: -- Colors “ baquas+metallicb”, “bbeigeb”, … -- Transmission “ (5|6)s*spdb”, “auto : bauto(.|,)”, -- Accessories “ broofs+rackb”, “bspoilerb”, … ... Textual
  • 14. Example: People Ontology
  • 15. 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
  • 16.
    • 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
    Application Areas of Ontologies
  • 17. Types of Ontologies
    • Ontologeis can be classfied according to the degree of conceptualization
        • Top-level ontologies
    • describes very general notions which are independent of a particular problem or domain
    • are applicable across domains and includes vocabulary related to things, events, time, space, etc
        • Domain ontologies
    • knowledge represented in this kind of ontologies is specific to a particular domain such as forestry, fishery, etc.
    • They provide vocabularies about concepts in a domain and their relationships or about the theories governing the domain.
        • Application or task ontologies
    • describe knowledge pieces depending both on a particular domain and task.
    • Therefore, they are related to problem solving methods .
  • 18. 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)
  • 19. Thesauri and Ontology Similarities
    • Both serve the same purpose, namely to provide a shared conceptualisation about a specific part of the world to different users in order to facilitate an efficient communication of complex knowledge.
    • Both disciplines are based on concept systems representing highly complex knowledge independent of any language.
    • Both are concerned about covering a broad range of terminology used in a particular domain, and in understanding the relationships among these terms.
    • Both utilize a hierarchical organization to group terms into categories and subcategories.
    • Both can be applied to cataloguing and organizing information.
  • 20. Thesauri and Ontology Differences
    • Formality of the definition:
      • Thesauri uses text in natural language to define the meaning of terms. The correct interpretation of the intended meaning depends on the user.
      • Ontologies specify conceptual knowledge explicitly using a formal language with clear semantics, which allows an unambiguous interpretation of terms.
    • Computational support:
      • The available tools are quite different for thesauri and ontologies.
      • Most thesauri maintenance tools provide limited or no means for an explicit representation of knowledge.
      • Ontology maintenance tools provide systems with powerful knowledge representation languages and inference mechanisms that allow formal consistency checks, inference of new knowledge, and a more user-friendly interaction.
    • Users:
      • Thesauri are intended for human users, where domain experts constitute the major user group.
      • Ontologies are mainly developed for knowledge sharing between (both human and artificial) agents.
  • 21.
    • Little possibility of re-use due to inherent semantic ambiguity and lack of the explicitness of their semantics .
    • Difficulties in the diversity of their representational form (no common representational language)
    • Developed for human use. They lack of expressive mechanisms to represent, maintain, and reason about complex knowledge in an explicit form- interpretation is left for humans.
    • (Source:
    Reasons to evolve thesauri to ontologies
  • 22. Problems with Thesaurus Modelling BT/NT relations-AGROVOC
    • Thesauri have not been constructed with purely defined semantics. It is common for BT/NT relations within a thesauri to include at least:
    • subtype of (e.g. soil/ subsoil)
    • instance (e.g. Development Agency/IDRC))
    • part of (e.g. soil/top soil)
    • role (e.g. Development Agency/Voluntary agency)
    • property of (e.g. maize/sweet corn)
    • MAIZE
    •   NT dent maize   NT flint maize   NT popcorn   NT soft maize   NT sweet corn   NT waxy maize  
    • SOIL
    • NT top soil NT subsoil
    • Development Agencies
    • NT development banks NT voluntary agencies NT IDRC
  • 23. Problems with Thesaurus Modelling Equivalence relations – UF, USE
    • UF/USE - between the descriptor and the non-descriptor (s).
    • Associative relationship can represent:
      • genuine synonymy, or identical meanings;
      • near- synonymy, or similar meanings;
      • In some thesaurus, antonym, or opposite meanings; ( eg. Eurovoc)
    • UF aid institutions 1
    • 1- Similar but not necessarily identical concept
  • 24. Problems with Thesaurus Modelling Associative relations- RT
    • The RT associative relation is more even open to interpretation than the hierarchical relation
    • For some thesaurus, it can contain:
      • cause and effect
      • agency or instrument
      • hierarchy - where polyhierarchy has not been allowed the missing hierarchical relationships are replaced by associative relationships
      • sequence in time or space
      • constituent elements
      • characteristic feature
      • object of an action, process or discipline
      • location
      • similarity (in cases where two near-synonyms have been included as descriptors)
      • antonym
  • 25. RT in AGROVOC
    • Degradation
    • RT chemical reactions 1 RT discoloration RT hydrolysis
    • RT shrinkage
    • MAIZE
    • RT corn flour RT corn starch 2 RT zea mays
    • IDRC
    • BT development agencies RT canada 3
    • 1- cause and effect
    • 2- characteristic feature
    • 3- location
  • 26. Thesauri and Ontology how to migrate
    • Analyze the existing relations and establish semantically meaningful relations:
      • BT/NT => ‘Is-A’ relation
      • RT => analyzed to roles/properties/attributes
      • (like “produces”, “used by”, “made for”).
    • Allow for machine-processable definitions:
      • Fencing sword = sword used for: fencing”
      • Weapon = object used for: fighting or hunting
      • Mother = human & female & which has born: human
  • 27.
    • There is no one correct way to model a domain
      • Modeling the required knowledge heavily relies on to what purpose the ontology will be used.
    • Ontology development is a collaborative process
      • Knowledge captured in the ontology should be derived from consensus. This will ensure reuse and share-ability.
    • Ontology works in a network fashion
      • No single ontology but networks of ontologies
    • Ontology development is necessarily a dynamic and iterative process
      • Ontologies should evolve through time
    Things to keep in mind....
  • 28. Conclusion
    • Ontology provides better semantic representation and machine understandable representation of knowledge.
    • Ontologies are natural successors of thesauri particular for information retrieval and knowledge management.
    • Developing thesauri to ontologies requires increased precision of the semantics of the existing relations in thesauri.
    • Ontology repositories will be distributed on the Web
      • methods and tools for accessing/reusing/aligning ontology's are needed.
  • 29. Thank you for your attention ! Any questions, comments?