Embedding a VRE in an Institutional Environment (EVIE)

Transcription

1 Embedding a VRE in an Institutional Environment (EVIE) Workpackage 6: Taxonomy Design and Development New taxonomy developments, can these be applied to Virtual Research Environments 1. Introduction Workpackage six within EVIE originally aimed to evaluate FAST and its suitability to Virtual Research Environments (VREs). To support this a set of three use cases for taxonomies within the VRE context were suggested. The evaluation of FAST showed that this tool was not suited for use within the VRE context. However, there have been many taxonomy developments as well as FAST so some notes have been put together on these taxonomy like developments several of which are being associated with Web2.0. This report does not provide the detail necessary to fully understand each of these technologies, nor the effort needed to implement, maintain, and manage the taxonomies. The aim of this report is simply to carry out a review of available technologies and attempt to identify an appropriate method for developing the right kind of taxonomy to support a VRE for at least one of the use cases. A taxonomy is a classification scheme with an associated vocabulary. The word is taken from the Greek taxis which means classification, and nomos meaning management. Taxonomies are often hierarchical in nature but not always; they can be linear or web-like structures depending on the application. In many cases taxonomies are used to improve navigation and resource discovery, they are particularly useful where functional logistics and reasoning are required within applications. They are useful for the organisation, description and management of information enabling related items to be clustered together and maintained as a collection within the context of their own domain. When considering the implementation of a taxonomy there are several options: Traditional, enumerative, hierarchical, classification systems with controlled vocabularies which offer a very rigid, well defined method of organising resources. These vocabularies have developed over considerable time taking input from collections of subject experts but are notoriously inflexible. Faceted classification systems facilitate multiple paths to the same resource via the assignment of multiple class marks (facets). In physical libraries this method is often considered too complex but in the online world this becomes less of a problem as enumeration and shelf mark issues become irrelevant. Assigning a single resource to multiple virtual shelves is a considerable benefit in online libraries and can help address the criticism that classification is too subjective. Semantic Web Ontologies offer the necessary flexibility to create subject specific, tailored but extensible taxonomies. These can, however, be costly to create as subject experts are required to define the vocabulary from scratch. Folksonomies are socially defined (or user defined) taxonomies and can therefore be less expensive to create. However, the uncontrolled nature of the resulting vocabulary can be poor. Unregulated vocabulary contributions can lead to ambiguities and duplication. 2. Traditional Classification Schemes - Library of Congress Classification (LCC) is used in most University and research libraries in the US. Public libraries, especially in the UK, more often use Dewy Decimal Classification. Its development was driven by the physical and practical requirements of the US Library of Congress and for that reason it is sometimes criticised for lacking theoretical basis. It organises resources into broad alphabetical divisions and is 1

2 essentially enumerative and hierarchical in nature just like DDC which influenced its development. Expert librarians are required to properly classify items into the appropriate sub divisions of the scheme. - Dewey Decimal Classification (DDC) was developed by Melvil Dewey in 1876 and has since been modified and expanded over 22 revisions. It is a traditional, hierarchical classification scheme that attempts to organise all knowledge into ten major classes. Each class has ten divisions and each division has ten sections. The class, division and section are identified by three leading digits; further refinements can be specified following a decimal point. DDC can be used for faceted classification by combining elements from different branches of the scheme following the decimal point. The main advantage of DDC is its familiarity and its logical organisation. The disadvantages are that not all subjects divide perfectly into ten subdivisions and sometimes the shape of knowledge changes a branch that shows significant growth during one era of time may be overtaken by another at a later date. Dewey does not always cope well with such shifts in emphasis and significance. Its historical roots can be observed in its bias towards Christianity and towards certain forms of literature. Recent, significant technological and scientific advancements are condensed into branches of the hierarchy that were never expected to grow to the extent that they have. - Library of Congress Subject Headings (LCSH) is a thesaurus of subject headings developed by the US Library of Congress which helps to standardise the way resources are classified across libraries. Although the thesaurus is largely static, some terms may occasionally change for cultural or political reasons. In contrast to the two traditional classification schemes listed above, LCSH provides a means to evaluate the subject content of a resource; it provides access to material via topic, rather than assigning a category within a subject hierarchy. Using a standard vocabulary is good from the point of view of disambiguating terminology and promoting interoperability. 3. Faceted Classification Schemes - The Universal Decimal Classification (UDC) scheme was developed in Belgium at the end of the 19 th century. It is based on DDC but uses special symbols to indicate the presence of certain features or facets. This scheme has been found to be particularly useful in specialist subject libraries. The faceted nature of the classification process is often thought to be too complex for more generic libraries and collections, although the numbering scheme is more extensible than DDC. The scheme has undergone many modifications and is under constant review. A core version with 60,000 subdivisions is now available via the Master Reference File (MRF) database. The current full version has 220,000 subdivisions. - Faceted Application of Subject Terminology FAST is intended to combine an online metadata schema with a controlled vocabulary, in this case LCSH. This is described in more detail in the EVIE report Applicability of FAST to Virtual Research Environments. 4. Semantic Web Ontologies The aim of the semantic web is to give meaning to the web. One way to achieve this is to provide an infrastructure for the specification of machine-readable metadata for all web resources. Other paradigms involve representing content or knowledge in an XML based semantic web language which can be transformed for presentation in XHTML. One of the primary technologies supporting the semantic web infrastructure is the Resource Description framework (RDF). RDF enables the definition of extensible, interoperable metadata element sets for describing web resources that are expressed in RDF/XML. Standard element sets can be extended to meet domain specific requirements in the interest of interoperability. RDF is discussed in more detail in the section below on expression languages. 2

3 The semantic web is associated with Web the umbrella under which second generation web services that promote collaboration and information sharing on-line are described. It is envisaged that on-line applications will eventually completely replace those on the desktop. All that will be required of the client is a browser that will enable the user to access on-line applications that are constantly updated and allow interaction with other information providers and individuals. In order to support the kind of communication required for this level of collaboration, a well defined set of semantics are necessary. The increased metadata introduced by the Semantic Web is intended to give meaning to the data so that it can be used for improved resource discovery and information management. A key concept in this paradigm is the mapping of concepts within documents onto hierarchical Ontologies in order to provide context and resolve ambiguities. The most well known language for defining web ontologies is the Web Ontology Language (OWL). OWL is discussed in more detail in the section below on expression languages. Some examples of Semantic Web Ontologies expressed in OWL are: WordNet - a popular tool used in natural language processing. It is increasingly used in semantic web research for Annotation, reasoning, and as background knowledge in ontology mapping tools. Rswub - provides semantic web support for biostatistics. R is an open source dialect of the language S which was developed by Bell labs in the 1970s for interactive data analysis. Rswub stands for R Semantic Web Utilities for Bioinformatics. ISO specifies a conceptual data model for computer representation of technical information about process plants. The Periodic Table - a representation of the elements to support semantic web applications in chemistry and related disciplines Folksonomies The term folksonomy - a combination of the words folk and taxonomy - refers to an unconventional method of social classification that has recently emerged on the web. Interactive, applications encourage users to apply their own keywords to resources, these keywords are known as tags. The tag vocabulary is then used to organise resources into categories. This kind of classification was first demonstrated by Del.icio.us in 2003 and the term folksonomy was soon after coined by Thomas Vander Wal. Folksonomies play a potentially important role in the advancement of the semantic web by promoting the provision of machine readable metadata for every web page. The kind of simple tagging involved in folksonomy development is considered easier for users to construct than, for example, embedded Dublin Core metadata. It is thought, therefore, that extensive use of folksonomies could accelerate the evolution of the semantic web. The vocabulary associated with a folksonomy is thought to be less expensive to create than that of a semantic web ontology or traditional classification scheme which have controlled vocabularies. This is because they are created by users and not dedicated subject experts. The idiosyncratic nature of folksonomy classification is thought to be as engaging as it is misleading. It is thought that the vocabulary reveals something about the user and therefore appeals to further like-minded users. There is however an obvious, legitimate concern that unregulated vocabulary generation can lead to poor organisation and increased complexity for resource discovery. Words with multiple meaning that could give rise to ambiguity are a major concern in 3

4 Folksonomies. Tagging usually involves specifying just one word so it is not clear how ambiguities can be resolved. Two examples of popular folksonomies are: - Del.icio.us Used to organise, merge and share users categorised bookmarks - Flickr Used to organise users digital photographs 6. Expression Languages The Resource Description Framework (RDF) is a language for describing web resources. Originally developed in the 1990 s with a model and syntax specification and accompanying schema, RDF is now defined via six documents (primer, concepts, syntax, semantics, vocabulary and test cases) developed by the RDF Core working group of the W3C Semantic Web Activity. RDF provides a framework for the exchange of interoperable extensible, machine processable metadata describing web resources (e.g. title, author, abstract). It can also be used to describe any item identifiable on the web via a URI. RDF statements are built around the notion that resources have properties and properties have values, also known as subject, predicate and object. John Smith Resource Subject Property Predicate Value Object Objects may be literals, as shown, or URIrefs like the subject and predicate (an object may be the subject of further such statements). Literals may not be used to define subjects or predicates, these must be defined via URI. The predicate in the above statement is defined via a URIref to an element within the Dublin Core metadata element set vocabulary - creator. The above statement is shown graphically using a nodes and arcs diagram, in practice RDF statements are usually expressed in RDF/XML: <?xml version= 1.0?> <rdf:rdf xmlns:rdf= xmlns:dc= > <rdf:description rdf:about= > <dc:creator>john Smith</dc:creator> </rdf:description> </rdf:rdf> Obviously the description element above could be extended to include other properties defined within the Dublin Core vocabulary and these could be expressed alongside properties taken from other vocabularies within the same resource description. A set of URIrefs intended for a specific purpose, like those defining the elements of the Dublin Core element set, are referred to as a vocabulary. A vocabulary may or may not comprise URIrefs defined within the same namespace. Defining new domain specific vocabularies is achieved via the RDF Schema, known as rdfs. rdfs enables the specification of new classes of resources and their properties. Resources can be 4

5 defined as instances of a class. This is similar to common practice in Object Oriented programming. Classes can be hierarchical (i.e. one class can be defined as being the sub class of another class). rdfs facilities are themselves defined as an RDF vocabulary. <?xml version= 1.0?> <!DOCTYPE rdf:rdf [<!ENTITY xsd >]> <rdf:rdf xmlns:rdf= xmlns:rdfs= > <rdf:description rdf:id= Serial > <rdf:type rdf:resource= /> </rdf:description> <rdf:description rdf:id= Journal > <rdf:type rdf:resource= /> <rdfs:subclassof rdf:resource= #Serial /> </rdf:description> </rdf:rdf> The Web Ontology Language (OWL) builds on the low level semantics of RDF to define more precise vocabulary support structures in which the relationship between classes and their properties can be defined. OWL is defined and developed by the Web Ontology Working Group of the W3C Semantic Web Activity. OWL enables web applications to go beyond the identification of basic metadata elements to interpretation of the semantics relating to the content of the resource. As its name suggests, OWL facilitates the definition of web ontologies. An OWL ontology comprises machine processable descriptions of classes and properties. These formal semantics enable the derivation of facts entailed by the semantics. Mapping resources to ontologies enables reasoning, for example: an apple is a type of fruit and fruit is a type of food and food is edible therefore an apple is edible. This means that useful inferences can be made across a collection of resources. Reasoner software should be generic so that it can process any domain ontology. There are three versions of OWL: OWL Lite OWL DL OWL Full OWL Lite provides facilities for building a classification hierarchy with simple constraint features. OWL DL (Description Logic) provides a greater degree of expression via language constructs that support, for example, type separation e.g. a class cannot also be a property or a member of 5

6 another class. OWL Full supports maximum expression capabilities and allows the meaning of pre-defined vocabularies to be augmented. Classes can be defined as members of other classes in OWL Full. Every OWL class is implicitly a subclass of owl:thing. Properties in OWL can be arranged in a hierarchy of like classes and can be restricted by rdfs:domain and rdfs:range elements. The owl:objectproperty element is used to define new properties, rdfs:domain is used to define the classes to which it can be applied and rdfs:range the value. rdfs:subpropertyof is used to create property hierarchies. <owl:class rdf:id= journal > <rdfs:subclassof rdf:resource= &publication;serial /> <rdfs:subclassof> <owl:restriction> <owl:onproperty rdf:resource= &publication;hasissn /> <owl:cardinality rdf:datatype= &xsd;nonnegativeinterger >1</owl:cardinality> </owl:restriction> </rdfs:subclassof> </owl:class> This extract defines a class journal that is a subclass of a thing called serial that is defined in another ontology called publication. This new class is also the subclass of an anonymous class that represents all those things that have exactly one hasissn property, also defined in the publication ontology. The definition of that property might look as follows: <owl:objectproperty rdf:id= hasissn > <rdfs:domain rdf:resource= #serial /> <rdfs:range rdf:resource= &xsd;positiveinteger /> </owl:objectproperty> This indicates that the property applies to instances of the class serial and the value can be any positive integer. Subsequently the description of an individual instance of the journal class within a document might look as follows: <journal rdf:id= Computer Networks > <hasissn rdf:resource= /> </journal> It can then be inferred that Computer Networks is a member of the class journal and also of the class serial because journal is a subclass of serial. In order to be a valid journal the hasissn property must be present and its value must be a positive integer, as it is shown here. The Simple Knowledge Organisation System (SKOS) is an RDF vocabulary for defining thesauri and other similar knowledge representation structures. It was created primarily for the simple expression of folksonomy vocabularies. It works around the principle of assigning a preferred label and alternative labels to concepts and then defining narrower and broader concepts resulting in a labelled subject hierarchy with synonyms. SKOS core is intended to compliment OWL; the vocabularies are much simpler to understand and create than OWL ontologies. SKOS enables the definition of concepts and concept schemes. The labels assigned to concepts can be words or phrases. Relationships between concepts are referred to as semantic relations. Relations can only exist between the concepts of a particular concept scheme but mappings can be defined between concepts in different schemes. These are termed semantic mappings. 6

7 <rdf:rdf xmlns rdf-syntax-ns xmlns skos/core# > <skos:concept rdf:resource= > <skos:preflabel>xhtml Tutorial</skos:prefLabel> <skos:altlabel>xhtml primer</skos:preflabel> <skos:altlabel>extensible Hypertext Markup Language Tutorial</skos:preflabel> <skos:scopenote>a basic tutorial for beginners</skos:scopenote> <skos:broader rdf:resource= /> <skos:related rdf:resource= /> </skos:concept> </rdf:rdf> In the above example the broader concept could refer to more generic web technology tutorials and the related concept could be defined as perhaps an HTML 4.0 tutorial. 7. Bringing these technologies into the context of VREs Each of the taxonomy sections introduced earlier in this document will be examined for applicability to the three identified use cases. Use case 1: classifying research outputs Use case 2: improving interface design and usability Use case 3: structuring tool development Traditional Classification Schemes None of these covered in this report are appropriate for the three use cases. For example, in the dynamic context of a VRE the LCSH could be too restrictive and not flexible. These schemes are rather prescriptive, static, and rigid. The ability to grow and shape the taxonomy to fit the research activities supported by the VRE is essential, but this is not a supported feature of traditional classification schemes. Faceted Classification Schemes Initial investigation with FAST showed that the taxonomy cannot easily be embedded with a local e-infrastructure. The detailed nature of the hierarchy, coupled with it being maintained by an external entity (OCLC), might not adapt well with the dynamic nature of research material produced within a VRE. Similarly while UDC has 220,000 subdivisions these are created and maintained externally to the local infrastructure. The domain of the controlled vocabularies implies that use case 2 and use case 3 would not be supported by these schemes, so they would be constrained to use case 1. However, in a similar manner to traditional classification schemes, faceted ones are rather static, rigid, and prescriptive. It is not currently possible to grow and shape the taxonomy to suit the VRE requirements for use case 1. Semantic Web Ontologies As stated earlier, the intention of the Semantic Web type metadata is to give meaning that can be utilised for improved resource discovery and information management. While this does not necessarily preclude any of the use cases, it is unlikely that the sort of information management needed for use case 3: structuring tool development would benefit from an RDF based 7

8 description. Defining subjects and predicates via URIs necessitates managing a service to resolve these URIs, and implies a maintenance overhead. OWL lite requires that a substantial effort is made to build the classification hierarchy, and none of the use cases would gain anything from the inference capability that OWL was designed to contain. For use case 2: improving interface design and usability the vocabulary associated with the taxonomy can be used in query expansion, and the ontology structure can be used for guided navigation or to create a browsable directory of VRE components and tools. (See the KM World report on Dynamic Taxonomies, for examples of taxonomies that have been implemented to improve navigation.) While semantic web ontologies are more suited to use case 1: classifying the research outputs, it is unclear how an institution should collect the appropriate information to structure its VRE outputs. Once the ontology is built, some RDF tagging would still have to be attached to all of the VRE outputs, and it is not clear how to automate this or whether the researchers would be required to supply metadata manually for every VRE output. All of the examples so far of these ontologies are in a single subject area, whereas for use case 1 to be met the ontology would have to cover all of the subject domains of the researchers using the VRE. Folksonomies Again, this type of scheme is most suited to use case 1: classifying research outputs. The concern that unregulated vocabulary generation can lead to poor organisation, and hence increased complexity for resource discovery, cannot be discounted. Words with multiple meanings could give rise to major ambiguities, and this is a major concern in folksonomies. With a small designated community agreeing on a common language, and policing the folksonomy they can be very successful however the domain of an institutional VRE is across many subject areas and the different research communities sometimes reuse terms and words with very different specialist meanings. There is no scalability to allow for disambiguation and tag conflict resolution (through tag tuple analysis), as typically only one word is allowed for tagging (although it reduces the user effort). SKOS enables the definition of concepts and concept schemes, but it is not clear where the population of these would occur. A labelled subject hierarchy may well derive benefits to the researchers that use a VRE, but who would provide and maintain the hierarchy for a multi-faculty and world class research institution (often being leading edge means exploring subjects which have not yet been established, nor fitted into a hierarchy). Some of the thought and effort supplied by researcher to tag their research outputs may possibly be replaced by machine tag generation (through complex document analysis like those used by internet search engines), although it is unlikely that automating tagging for non-textual research outputs would be possible. Technology to do this is not yet available, although there are some products emerging like GammaWare ( or MetaTagger ( It is not yet evident that the self tagging of research outputs by researchers is easier to construct than richer traditional metadata, and may actually only serve to help a researcher find their own old research outputs (this is possibly still a valid purpose, as this is the intention of Flickr and Del.icio.us). Naturally if tags need maintaining and updating (or mapping on to newer terms and usage), or grammars and vocabularies need to be built to support the tags, then this is harder than maintaining tags created by dedicated subject experts. Terms and usage are known to change over time, so consideration needs to be given to this (something tagged robotic in 1970 may be dissimilar from a tagged article robotic in 2005). The idiosyncratic nature of tagging may prove too misleading for researchers of a different mind-set to the original tagger, although they may well provide the raw data for historical research into the evolution of the way researcher thought and worked. A hybrid approach of guided folksonomies may draw out the advantages of self tagging while avoiding a burden of poor organisation, so the researcher can be guided on where to classify the item and given the correct domain vocabulary for the chosen area. This raises the problem of building the appropriate hierarchy or model for each domain. 8

9 Use case 2: improving interface design and usability could possibly be aided through the use of a folksonomy. If all of the interface and content elements are tagged and then a user interface setup tool provided that uses the folksonomy to guide a researcher to find the relevant component then a better VRE experience would be the result. Unfortunately the tools and techniques needed to make this effective are immature, and substantial development would be needed to improve upon the standard categorisations provided in many portal products. For a folksonomy to be successful for use case 2 all of the effort would be placed on the team producing the interface elements, and the team would have to share the same idiosyncracies of the researchers that the VRE supported. Use case 3 would not benefit in any way from folksonomies. 8. Conclusions A VRE must primarily enable a researcher to carry out activities that engender creativity while increasing effectiveness and efficiency. The VRE does this through a dynamic, intuitive interface. Such an application requires a sound platform with clearly defined semantics from which to server Web 2.0 technologies, or any of the other taxonomical techniques discussed. This report has discussed four different kinds of taxonomy, and three use cases to which these could be applied. Traditional library classification schemes, though well defined and tested, are likely to be too rigid and inflexible for deployment in the VRE use case contexts. This is also the case for faceted schemes. Semantic web ontologies and folksonomies provide a much more flexible, extensible, solution that can be tailored to suite the precise domain. Herein lies the crux of the problem, the domain of a VRE is not tangibly precise and rapidly changes. This requires a flexible and evolving vocabulary, which can easily be maintained and extended in several evolving domains. For use case 1 much of the same benefits as provided by taxonomies can be achieved with normal search engines (and indexers running on the VRE content), and the overhead of the latter option is probably less than the former. Granted, this is hard for non-textual research outputs and a simple manual tagging seems more attractive than populating a complex metadata scheme. Neither use case 2 nor use case 3 would benefit at the current time from the use of taxonomies. References RDF Primer OWL Guide SKOS Core Guide An Introduction to Folksonomies Folksonomies: power to the people Folksonomies: Tidying up tags? 9