The Key issues and challenges in Semantic Web Technologies

The World Wide Web changed the way we communicate, the way we do business, the way we seek information and entertainment – the very way most of us live our daily lives. Calling it the next step in Web evolution, Berners-Lee defines the Semantic Web as “a web of data that can be processed directly and indirectly by machines.”

A. Adham Sheriff, R. Senthil Kumar , A. Abdul Rahman

The semantic Web data itself becomes part of the Web and is able to be processed independently of application, platform, or domain. This is in contrast to the World Wide Web (W3C) as we know it today, which contains virtually boundless information in the form of documents. The Semantic Web, on the other hand, is about having data as well as documents on the Web so that machines can process, transform, assemble, and even act on the data in useful ways.

This article discusses the current state of the Semantic Web, and the challenges involved in developing such a semantic web sites. It introduces Tim Berners-Lee's initial vision for the Semantic Web, briefly discussing the technology and tools now available to support it, taking a look at the 'layer-cake' diagram of the Semantic Web architecture. 

The Technologies

The third common use of the term Semantic Web is to identify a set of technologies, tools and standards which form the basic building blocks of a system that could support the vision of a Web imbued with meaning. The Semantic Web has been developing a layered architecture, which is often represented using a diagram first proposed by Tim Berners-Lee, with many variations since the following figure gives a typical representation of this diagram.



Key issues in Semantic Web

The term 'Semantic Web' is one, which is widely used, often without much care or understanding of its origins and meaning. The major key issues are,
1) Extracting hidden knowledge from unstructured and structured data
2) Knowledge fusion over heterogeneous data sources
3) Ontology sharing.
4) Building user-friendly Semantic Web applications
5). Security, privacy, and reputation in semantic Web services
6). Quality of Service (QoS)
7). Query and optimization models for semantic Web.

Vision of Semantic Web

The vision of the Semantic Web is a “web of data” that not only harnesses the seemingly endless amount of data on the World Wide Web, but also connects that information with data in relational databases and other non-interoperable information repositories,

Semantic Web Agent

The agent had to find, interpret, combine, and act on information from multiple sources; it is a long-term vision for applying the Semantic Web. It’s one that may or may not come to fruition, and only the future will tell. However, the vision itself is important for understanding the potential of Semantic Web technologies.

The Semantic Web agent does not include artificial intelligence – rather, it relies on structured sets of information and inference rules that allow it to “understand” the relationship between different data resources. The computer doesn’t really understand information the way a human can, but it has enough information to make logical connections and decisions.

The basis for the augmented functionality of the Semantic Web is
· A global naming scheme (URIs); 
· A standard syntax for describing data (RDF); 
· A standard means of describing the properties of that data (rdf-schema); 
· A standard means of describing relationships between data items (ontologies); 
· The means to support trust and security. 
· Global naming scheme 

If any Semantic Web application is to be able to access and use data from any other such application, every data object and every data schema/model must have a unique and universal means of identification. These identifiers are called URIs (Universal Resource Identifiers).

RDF

The semantic web vision proposes to represent machine processable information using RDF (Resource Description Framework), which extends XML. RDF defines a general common data model that adheres to web principles. RDF provides a consistent, standardised way of describing and querying internet resources, from text pages and graphics to audio files and video clips. It gives syntactic interoperability, and provides the base layer for building a Semantic Web. RDF defines a directed graph of relationships.

RDF is an XML-based standard for describing resources that exist on the Web, intranets, and extranets. RDF builds on existing XML and URI (Uniform Resource Identifier) technologies, using a URI to identify every resource, and using URIs to make statements about resources. RDF statements describe a resource (identified by a URI), the resource’s properties, and the values of those properties. RDF statements are often referred to as “triples” that consist of a subject, predicate, and object, which correspond to a resource (subject) a property (predicate), and a property value (object). Below is an example of an RDF statement in plain English:

RDF triples can be written with XML tags, and they are often conceptualized graphically as shown below:

After creating this triple, we can go on to create other triples to associate the agent with an email address, image, etc. 



Once triples are defined graphically, they can be coded in either RDF/XML or n-Triples formats to be accessed programmatically. 

By creating triples with subjects, predicates, and objects, RDF allows machines to make logical assertions based on the associations between subjects and objects. And since RDF uses URIs to identify resources, each resource is tied to a unique definition available on the Web. However, while RDF provides a model and syntax (the rules that specify the elements of a sentence) for describing resources, it does not specify the semantics (the meaning) of the resources. To truly define semantics, we need RDFS and OWL. 

RDF Schema

RDF schema allows a designer to define and publish the vocabulary used by an RDF data model, i.e define the data objects and their attributes. Both RDF and RDF-Schema are based on XML and XML-Schema. The existence of standards for describing data (RDF) and data attributes (RDF Schema) enables the development of a set of readily available tools to read and exploit data from multiple sources.
In an RDFS vocabulary, resources are defined as instances of classes. A class is a resource too, and any class can be a subclass of another. This hierarchical semantic information is what allows machines to determine the meanings of resources based on their properties and classes. 

Below is a graphical example of an RDFS that shows a resource and its associated properties, values, and classes.

Overall, RDFS is a simple vocabulary language for expressing the relationships between resources. Building upon RFDS is OWL, which is a much richer, more expressive vocabulary for defining Semantic Web ontologies.

. Ontologies

Semantic interoperability requires mapping between terms, which in turn requires content analysis. This requires formal and explicit specifications of domain models, which define the terms used and their relationships. Such formal domain models are sometimes called ontologies. Ontologies define data models in terms of classes, subclasses, and properties.

OWL is a third W3C specification for creating Semantic Web applications. Building upon RDF and RDFS, OWL defines the types of relationships that can be expressed in RDF using an XML vocabulary to indicate the hierarchies and relationships between different resources. In fact, this is the very definition of “ontology” in the context of the Semantic Web: a schema that formally defines the hierarchies and relationships between different resources. Semantic Web ontologies consist of a taxonomy and a set of inference rules from which machines can make logical conclusions. 

A taxonomy in this context is system of classification, such as the scientific kingdom/phylum/class/order/etc. system for classifying plants and animals that groups resources into classes and sub-classes based on their relationships and shared properties. OWL also utilizes the XML Schema datatypes and supports class axioms such as subClassOf, disjointWith, etc., and class descriptions such as unionOf, intersectionOf, etc. Many other advanced concepts are included in OWL, making it the richest standard ontology description language available today. 

A graphical example of an OWL ontology is below.




It’s important to note that OWL has three sub languages, each with increasing complexity: OWL Lite, OWL DL, and OWL Full. OWL DL includes OWL Lite, and OWL Full includes OWL DL and OWL Lite. Developers choose which OWL dialect to use based on the level of complexity and level of detail required by their semantic model.

Proof and Security

If the Semantic Web is indeed to become a global database, and if its development is evolutionary and distributed, then there are issues of accessibility, trust and credibility. Not all data sources will have universal access, so there needs to be a robust and extensible security model. At the very least, derived facts could be attributed to a source, and over time applications could be developed which rate sources as to their integrity etc. These upper layers of the stack are the least researched and present some of the most difficult technical challenges faced by the Semantic Web venture.

Challenges in Semantic Web Technology:

The Semantic Web activities recently attracted a large, significant and specialized research community consisting of computer scientists, computational linguists, logicians, knowledge and ontology specialists, programmers, e-commerce, etc. The major challenges in Semantic web technology are,

1). Semantic web needs human language technology and human language technology will highly benefit from the Semantic Web.
2). The use of the new semantic web technologies for improvement of natural language applications was neglected.
3). Development of ontologies plays a vital role. Various effort has been devoted to the research of different aspects of ontologies, including ontology representation languages, ontology development, ontology learning approaches, and ontology library systems, which manage, adapt, and standardize ontologies.

Conclusion

In this paper, we reported a descriptive study on Semantic Web key issues and the challenges. In particular, we presented the challenges that we are facing during the development of the Semantic Web. We hope that this paper will shed some light on the direction of future work.
The Semantic Web is still a vision. We believe that the Web will grow towards this vision in a way like the development of the real world: Semantic Web communities will appear and grow first, and then the interaction and interoperation among different communities will finally interweave them into the Semantic Web. 


The authors are lectures with Department of Computer Applications, Vellammal College, Chenna and can be reached on arun_adams@yahoo.com, abd_ul_rahman@rediffmail.com, rsk_ramachandran@yahoo.co.in respectively.

References

[1].http://www.ilrt.bris.ac.uk/discovery/rdf/resources/ T. Berners-Lee (1989). Information Management: A Proposal. CERN. Available at:
http://www.w3.org/History/1989/proposal.html 
[2]. T. Berners-Lee (1998). Semantic Web Road Map. W3C. Available at:
http://www.w3.org/DesignIssues/Semantic.html 
[3]. T. Berners-Lee, J. Hendler, and O. Lassila (2001). The Semantic Web. Scientific American. Available at: http://www.sciam.com/print_version.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21
[4]. Business Process Execution Language for Web Services (BPEL4WS) homepage: http://www-128.ibm.com/developerworks/library/ws-bpel/ [last accessed 25/04/05]
[5]. The Cultivate interactive issue: Challenges for a semantic web: Homepage: http://www.cultivate-int.org/issue7/semanticweb/
[6]. What is semantic web? Homepage: http://www.altova.com/semantic_web.html