DOCUMENTATION
FLEXIBLE METADATA STORING - ELSEM REPORT -
- Introduction
- Storage alternatives for semantic metadata
- OpenCyc
- Overall description of the SLOR prototype
- Integrating the large commonsense ontology
- Problems found
- Final Proposed Architecture
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WORKING GROUP
Further Work
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PROBLEMS FOUND

During the prototype construction we have been discover several problems.

Mapping OpenCyc to OWL

Opencyc owl version lacks the expressiveness needed to satisfy a whole and covered semantic model. It does not uses description logic, and it does not define “domain” and “range” on the ontology properties too.

Analyzing the problem in depth, Opencyc version has been built without a through mapping process between CycL and OWL languages. An OWL Class corresponds to a CycL concept, whereas an OWL “ObjectTypeProperty” corresponds to a CycL predicate. The main relationships defined in the RDFS specification have been mapped. For instance, #$isa and #$genls has been converted into “subClassOf” properties. Also, many “subPropertyOf” relationships have been mapped (figure [12]).

OWL Class: #$Person
(#$arg1Isa ?X #$Person)
Properties: placeOfWork , boyfriend, nativeLanguage, etc..

#$Person --> http://www.cyc.com/2003/04/01/cyc/#Person
boyfriend -->http://www.cyc.com/2003/04/01/cyc/#boyfriend

Figure 12 - #$boyfriend(PERSON MALEHUMAN): CycL predicate converted to OWL property into a Jena Persistent Model.

But, what happen when a predicate has an arity greater than one? A predicate only can correspond to OWL ObjectProperty, but CycL allows a limited number of arguments and provides the mechanisms to define more than one predicate arguments. However, in the OWL file, if a predicate has several arguments, there not exists a formal specification to allocate arguments. Also, the range and domain properties have not been defined. As it can be seen in figure [13], the “BorderOf” predicate does not have these properties.

Figure 13- #$borderOf(BODER REGION) CycL predicate converted to OWL property into a Jena Persistent Model.

Low performance

The tests run on semantic web frameworks have highlighted problems when handling large amount of data. Many of these problems are due to inference execution over the underlying persistent model. In detail, when a query is dispatched the reasoner planning the execution to retrieval all inferred data from the database.

Interface problems

The slor interface needs a hard handle to integrate the semantic concepts and rules within metadata lom records.

In order to solve these problems we had considered new web development trends. AJAX (witch stands for Asynchronous Java Script and XML) is a newfangled technique that provides a new scenario for the interaction between web client and server. AJAX increases the web page’s interactivity, speed and usability. This technology makes web pages more dynamic, disabling the post back actions of the current web technologies (such as ASP.NET or Java Server Faces) and providing, at the same time, a new asynchronous communication based on XML messages between server and client. Hence a web client don’t need to send a dynamic web page and wait to receive it again (by means of XMLHttpRequest object).

AJAX provides a set of encapsulated XML messages over HTTP protocol, Figure [16] shows a comparison between web application models. A detailed discussion about AJAX interaction is out of the scope of these technical notes, however interested readers can consult (Garrett, 2005).

Figure 14 - SLOR web interface


We have developed a simple AJAX component to test the benefits of this technology in SLOR. Similar to Intellisense technology, this component helps to write the semantic expressions within learning object metadata records. It is not necessary to know about an ontology and all its individuals, class or properties. As it can be seen in the figure [15], the AJAX component shows a list that contains all individuals of the country class related with the property “situatedIn” making used of the TGN ontology.


Figure 15 - SLOR AJAX Component

Figure 16 - Comparison between web interaction models. (Source Garret, 2005)

Hard Work

We can not leave the research area of automated metadata creation, if we want to create a usable repository. Each learning object specification provides a big set of fields to describe a learning object in different context. To fill and complete a whole description is a hard job, although there exits several tools to create metadata records. Studies as (Philippe Vidal and Michael Meire, 2005) shows how can uses an automated assistant that auto complete some obvious and usual fields.