Knowledge Organization Systems

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Knowledge Organization Systems

  1. 1. Knowledge OrganizationSystemsRajendra AkerkarWNRI, Norway R. Akerkar 1
  2. 2. SKOS a model and vocabulary that is used to y bridge the world of knowledge organization systems (KOS) and the g y ( ) Semantic Web understanding SKOS will enhance your g y understanding about ontology R. Akerkar 2
  3. 3. KOS a set of elements, often structured and controlled, controlled that can be used for describing objects, indexing objects, browsing collections, etc. KOSs are commonly found in cultural heritage institutions such as libraries and museums. They can also be used in other scientific areas, examples include biology and chemistry, p gy y R. Akerkar 3
  4. 4. KOS examples Taxonomy ◦ referring to the classification of things or concepts, as well the schemes underlying such a classification. Thesaurus ◦ Thesaurus can be understood as an extension to taxonomy: allowing subjects to be arranged in hi i a hierarchy and in addition, it adds the h d i ddi i i dd h ability to allow other statements be made about the subjects R. Akerkar 4
  5. 5.  They can make search more robust (instead of simple keywords matching, related words, for example, can k d t hi l t d d f l also be considered). They can help to build more intelligent browsing interfaces (following the hierarchical structure, and explore broader/narrower terms, etc.). terms etc ) They can help us to formally organize our knowledge for a given domain, therefore promote reuse of the knowledge, and also facilitate data interoperability. R. Akerkar 5
  6. 6.  KOSs are used for knowledge g organization, whilst ontologies are used for knowledge representation. g p KOSs are semantically much less rigorous than ontologies, and no formal reasoning g , g can be conducted by just having KOSs. ◦ For example, ontologies can specify a is-a relationship, while in thesauri, the hierarchical relation can represent anything from is- a to part-of, depending on the interpretations rooted from the domain and application. R. Akerkar 6
  7. 7.  The Semantic relation is fairly weak. An ontology is a rich expression of semantic relations While a term list, free or controlled, is a natural arrangement of word forms. g BUT there is a kind of semantic relation between hierarchical li hi hi l lists and relationship li d l i hi lists that they h h are both considered Subject-based classification R. Akerkar 7
  8. 8.  It is any form of content classification that y groups objects b th subjects th are about. bj t by the bj t they b t This can take many forms, and is generally combined with other techniques in order to create a complete solution. Metadata is generally defined as "data about data," which is of course a very broad definition. y In content management and information architecture, metadata generally means "information about objects" ("objects" here used information objects ( objects as defined above), that is, information about a document, an image, a reusable content module, and so on. R. Akerkar 8
  9. 9.  The relation between subject-based classification and subject based metadata is that metadata properties or fields that directly describe what the objects are about by listing discrete subjects use a subject-based classification. j j Note that there is a difference between describing the objects being classified and describing the subjects used to classify these objects Metadata describes objects One of the ways in which it objects. does that, is by connecting objects to the subjects they are about. R. Akerkar 9
  10. 10.  Controlled vocabulary it is a closed list of named y subjects, which can be used for classification. In library science this is sometimes known as an indexing language. The constituents of a controlled g g g vocabulary are usually known as terms. At term i a particular name f a particular concept. is ti l for ti l t (This is pretty much the same as the common-sense notion of a keyword). Same concept may have multiple names, and also that the same term may name multiple concepts concepts. R. Akerkar 10
  11. 11.  Taxonomy is a subject-based classification that y j arranges the terms in the controlled vocabulary into a hierarchy without doing anything further, though in real life you will find the term "taxonomy" applied to y y pp more complex structures as well. Why is Taxonomy f ? Wh i T for? The benefit of this approach is that it allows related terms to be grouped together and categorized in ways that make it easier to find the correct term to use whether for searching or to describe an object. R. Akerkar 11
  12. 12.  Taxonomy y helps p users by y describing g the subjects. Metadata only relates objects to subjects, whereas here we have arranged the subjects in a hierarchy. So a taxonomy describes the subjects being used for classification, but is not itself metadata; it can be used in metadata, however. R. Akerkar 12
  13. 13.  In this diagram, the blue lines are the metadata, g , , while the black lines that make up the taxonomy is part of the subject-based classification scheme. Figure. Using the taxonomy in metadata R. Akerkar 13
  14. 14.  The distinction derives from the blue lines being statements about the paper, but the black line p p , between "topic maps" and "knowledge representation" is not a statement about the p p ; paper; its a statement about "topic maps". One p p consequence of this is that if we have another paper about "topic maps" we do not need to repeat that "topic maps" p p p belong under "knowledge representation". Figure. Using the taxonomy in metadata R. Akerkar 14
  15. 15. A number of important pieces of information about the concepts are not being captured in the above taxonomy as: The fact that "XML Topic Maps" is synonymous with "XTM". XML Maps XTM . The difference between "XTM" and "topic maps". (Many users use these interchangeably, but they do not mean the same thing.) The fact that "topic navigation maps" is synonymous with "topic topic maps topic maps", but should no longer be used. The relationship between topic maps and subject-based classification and topic maps and the semantic web. The relationship between XTM and XML and HyTM and SGML. The similiarity between HyTM and XTM, XTM and their difference from TMQL and TMCL, as well as the similarity between TMQL and XQuery. Figure . Using the taxonomy in metadata R. Akerkar 15
  16. 16. Taxonomy as we defined it here cannot handlethese problems, though it should be noted thatmany systems referred to as taxonomies tosome extent can, as they extend the basicmodel defined here.Figure 2. Using the taxonomy in metadata R. Akerkar 16
  17. 17.  Taxonomies In a taxonomy the means for subject description consist of essentially one relationship: the broader/narrower relationship used to build the hierarchy. The set of terms being described is of course open, but the language used to describe them is closed, since it consists only of a single y g relationship. l ti hi Thesauri Thesauri extend this with the RT and UF/USE relationships, and the SN property, which allow them to better describe the terms. A i th l th t Again the language i closed, since thi i th is l d i this is the entire vocabulary at disposal for describing the terms Ontology In fact, thesauri could in theory be considered ontology where there is only one type called "term" one property type, "term", property, called "scope note", and three relationships (BT/NT, USE/UF, and RT). In practice thesauri are not considered ontologies because their descriptive power is far too weak, p precisely because of this limited vocabulary. y y R. Akerkar 17
  18. 18. What Is SKOS? Simple knowledge organization systems, p g g y ◦ is an RDF vocabulary for representing KOSs, such as taxonomies, thesauri, classification schemes, and subject heading lists. bj t h di li t ◦ can be published on the Web and they can be machine readable and exchanged between software g applications. ◦ SKOS is developed by W3C Semantic Web Development W ki Group (SWDWG) and h an D l Working G d has official Web site: http://www.w3.org/2004/02/skos/ R. Akerkar 18
  19. 19. SKOS It has become a W3C standard on 18 August 2009 Note t at t e URIs in SKOS vocabulary ote that the U s S OS vocabu a y all have the following lead strings: http://www w3 org/2004/02/skos/core# http://www.w3.org/2004/02/skos/core# By convention, this URI prefix string is associated with namespace prefix skos: and is typically used in different sterilization formats with the prefix skos skos. R. Akerkar 19
  20. 20. Example SKOS Core Constructs R. Akerkar 20

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