information systems adoption of semantic web ...

INFORMATION SYSTEMS ADOPTION OF SEMANTIC WEB TECHNOLOGIES Karim Ahmed School of Engineering and Information Sciences Middlesex University, UK [email protected]

Abstract Inspired by how the World Wide Web transformed information systems paradigm, it is clear that semantic web concepts will introduce revolutionary advances to the next generation information systems. In this context the purpose of this paper is to examine the need, possible solutions, potential applications, and challenges of Semantic Web adoption and to analyse the enhancements it brings to different types of information systems particularly in areas like Decision Support Knowledge-Based and Executive Support systems. Keywords: Semantic Web, Semantic Information Systems, Ontology, OWL, RDF

1. Introduction to Semantic Web Technologies 1.1 What is Semantic Web? Semantic Web is widely described as the Web of Data; such description comes from the original vision of Semantic Web to extend principles of the current World Wide Web (WWW) from being document oriented to be data oriented. But what is wrong with the current Web of documents and what is the idea of having a Web of data instead? The answer is in the fact that the current Web content is only suitable for human consumption even for content generated routinely from databases as it loses its structural information when it gets presented. On the other hand; creation of the Web of data will enable machines to consume these data in many ways and not only for presentation purposes but also for interoperability and integration between systems and applications. (Antoniou and Harmelen, 2008; Cardoso et al, 2008; Herman, 2009) This leads to the key idea of Semantic Web to bring machine-understandable descriptions to current Web content, and for this to be accomplished; Semantic Web technologies define ways to create meaningful structured collections of data and specify data relationships. (Berners-Lee et al, 2001; Cardoso et al, 2008) The potential benefits are unlimited as Berners-Lee et al (2001) explain that “the real power of the Semantic Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs. The effectiveness of such software agents will increase exponentially as more machine-readable Web content and automated services (including other agents) become available.” Semantic Web is based on a collection of key technologies and building blocks which are disseminated by World Wide Web Consortium (W3C) representing the main supporter of Semantic Web. Industry leaders and governments

1 BIS3340 Coursework Part 1, Autumn 2009/2010

put profound investments in such technologies knowing the revolutionary advances and capabilities it will bring. (Antoniou and Harmelen, 2008) It is clear now that Semantic Web aims to create a Web of machine-processable data (Antoniou and Harmelen, 2008), but how can we achieve this? The need of a paradigm shift in the way we think about data and its importance and to utilize available Web technologies and new Semantic Web technologies to make data smarter (Daconta et al, 2003) The following section describes the key Semantic Web technologies that will make the existence of smarter data possible.

1.2 Semantic Web Technologies – Making Data Smarter Semantic Web technologies enable us to fulfil the need of smarter data by describing and defining available data sources and relationships, in other words to add meanings to currently available resources and links. Adding meanings to data utilizes two key technologies: Ontologies and Resource Description Framework (RDF). It is clear from the Semantic Web layered approach presented by Berners-Lee (2000) that Ontologies and RDF lies at the heart of Semantic Web, we can also see that the Semantic Web builds up on the existing WWW technologies and especially XML based technologies. Ontologies are considered to be the cornerstone of the Semantic Web; as it is the fundamental technology for implementing semantics by establishing common conceptual description and a shared terminology between members of same fields of interest. Ontologies carefully define the terms used to describe and represent areas of knowledge allowing interactions between data held in different formats. (Geroimenko, 2006; Shadbolt et al, 2006).

Figure 1: Semantic Web Layered Architecture (Berners-Lee, 2000)

The Resource Description Framework (RDF) is an XML-based data model that provides simple semantics for resources and relation between them. The RDF Schema is the RDFs vocabulary description language that describes properties and classes of resources. (Antoniou and Harmelen, 2008) The Web Ontology Language (OWL) is the language use to build Ontologies. The OWL core idea is to enable well-organised representation of Ontologies by utilizing linking provided by RDF to allow ontologies to be distributed across systems. (Shadbolt et al, 2006) Unicode: standard computer character representation, Uniform Resource Identifier URI: standard for identifying and locating resources, together with Unicode provide a baseline for representing characters used in most of the languages in the world, and for identifying resources. Extensible Mark-up Language: XML and its related standards, such as Namespaces, and Schemas, form a common means for structuring data on the Web. Logic: used for ontology language enhancement. Proof: concerned with the deductive process as well as the representation of proofs in Web languages and proof validation. Trust: addresses issues of trust that the Semantic Web can support.


2. The need for Semantic Web Technologies The need for information systems to adopt Semantic Web technologies emerges from the current problems facing information systems utilizing traditional data sources. In this section I will summarise some of these problems that could be solved by adopting Semantic Web technologies: From knowledge management perspective, the current poorly structured information which represents the majority available causes a lot of limitations in searching, extracting, maintaining, uncovering, and viewing information. (Antoniou and Harmelen, 2008). In the Electronic Commerce (EC) field technical barriers exist and delay the development of EC, these barriers could be represented in the need for applications to meaningfully share information, and the existence of appropriate standard. Business-to-Consumer (B2C) and Business-to-Business (B2B) faces some limiting problems, for the B2C, the use of wrappers in online shopping-bots is very time consuming to program and always requires reprogramming. Also for the B2B, the lack of suitable standards to support B2B operations prevents it from having its full capacity even with the fact that the internet is the ideal infrastructure for B2B communications.( Antoniou and Harmelen, 2008; Leger et al, 2005) Information overload is one of the major problems that have to be solved; the rapid rate of information and information sources growth is becoming unmanageable. It is also undeniable that the revolution of Internet and Mobile technologies makes it even worse (Daconta et al, 2003). Organisations employees spend a lot of their work time each day trying to collect, organise and make use of data from their emails, from their local hard drives, from company’s data store, from their cell phones, and maybe from a couple of applications which are not yet integrated and so on. A lot of these overloaded information sources are not even structured. A lot of industries and business firms require the utilization of multiple data sources; one example is the risk and threat assessment solutions which have to make use of heterogeneous data sources internal and external to organisations. This need represents a challenge of how to effectively collect this information in a timely manner and how to make it available consistently. The poor traditional search techniques does not support this kind of applications because of the lack of context, conventional search always return too much information that is inadequately ranked and sometimes irrelevant(Sheth, 2005) The need for data integration and shared semantics is growing rapidly, and is still limited by the use of traditional knowledge-representation systems. Shadbolt et al (2006) give an example of life sciences research need for integration of diverse and varied data sources that originate from different communities and subfields. Another limitation caused by traditional knowledge-representation comes in the questions that can be asked and answered reliably by existing information systems which rely on conventional meaningless data sources. (BernersLee et al, 2001) Business intelligence software suffers from lack of extension flexibility or exploratory features and the inability to define business rules to get pro-active information and advises and also the lack of user-based presentation support. (Sell et al, 2005)

3. Potential Solutions and Enhancements Semantic Web technologies come with a lot of potential to solve currently existing problems and to propose effective enhancements to existing information systems. In this section I will present Semantic Web related solutions to the previously demonstrated problems, and I will also illustrate candidate information systems areas that will be significantly enhanced by the adoption of Semantic Web.

3.1 Potential Solutions Semantic Web aims to overcome knowledge management problems by enabling: meaning-based knowledge organisation and semantic query answering that is not limited to a single data source or document. (Antoniou and Harmelen, 2008). The availability of automatic knowledge base maintenance and indexing automation tools the Semantic Web can provide will boost the productivity by increasing the search efficiency. (Leger et al, 2005) Electronic commerce B2C could be improved by the adoption of Semantic Web to extract product information and pricing with higher efficiency, and more personalization could be offered according to user requirements. Also the


user will be able to see additional information extracted from other semantically linked resources like independent rating agencies, information like reputation of the online shop or the product will be easily available. From the other side the costly continuous programming of wrappers will become obsolete. While in the B2B market, using standard abstract domain models will resolve terminology differences and minimize the overhead of partnership, empowering B2B transactions like online automated auctioning and negotiation (Antoniou and Harmelen, 2008). The information overload problem needs a revolutionary solution just like the Semantic Web, which naturally tends to eliminate such problem by classification of data into well described categories, and linking to eliminate redundancy. Relationships and linked data are essential for semantics and Semantic Web technologies, and for this it is the perfect solution for integrating multiple sources of data, add to this the fact that the semantic approach is necessary to integrate heterogeneous information, as it is well known that a syntactic approach fails to make such integrations. For the risk and compliance applications adoption of Semantic Web technologies is a must to overcome existing problems and to enable a more efficient search and analysis. (Sheth, 2005) The proper knowledge representation will enable much easier and seamless data sources integrations, and with the proper creation of ontologies populated with domain knowledge possibilities are unlimited. This will replace the keyword-based search by semantic question answering, saving hundreds of hours for researchers and making them always up-to-date. The ability to ask questions and to retrieve data semantically will certainly enhance the existing business intelligence tools.

3.2 Information Systems Enhancements Solutions presented above addressed some of the main benefits of adopting Semantic Web technologies, it is clear that these technologies will add effective enhancements on many types of information systems, this section will conclude some of the potential information systems enhancements that Semantic Web technologies adoption will create. One of the major areas that Semantic Web technologies will enhance is the Decision Support Systems (DSS), as it will gain access to much richer sources of structured knowledge, not just the ordinary meaningless unstructured or semi-structured data. The implementation of Enterprise-Wide DSS will be much easier with the new integration and interoperability options Semantic Web offers. For the Knowledge-Based subsystems like Knowledge-Based Decision Support Systems (KBDSS), the way knowledge bases are enhanced by adopting Semantic Web technologies will be reflected on Knowledge-Based subsystems like KBDSS, which will be able to implement new rules, and answer questions more effectively and more over, it will not be limited to a restricted set of documents nor needs constant maintenance and update to provide desired support. Executive Support Systems (ESS) which depends on aggregations from other supporting data sources will work more efficiently if these data sources are semantic data sources, as this will enable ESS to carry on more complex analysis and create more complex aggregates, and the ease of integration with other systems inside and outside the organisation will enable ESS to automatically consume more data sources that it can’t use without costly and lengthy integrations. The creation of populated domain ontologies and proper linked described data will create a new wave of Expert systems, with domain specific knowledge that is easier to implement, maintain and update. The new systems will even have more question answering capabilities.

4. Application Areas From a business perspective, the real motivation for adopting Semantic Web Technologies comes from current business and industrial drivers which create in turn the potential areas of applications for Semantic Information Systems. Organisations realized how information technology and information systems can provide a strategic advantage in responding to changing business drivers (Stephens, 2008). Some of the potential areas of applying Semantic Web technologies which are derived from growing business and industrial motivations are: the enhancement of recruitment services effectiveness, digital libraries, electronic government, healthcare, the agile manufacturing improvement in food industry, the identification of patterns and


insights in data for governmental agencies, the integration of heterogeneous scientific data which represents one of the major application areas, the enterprise search and navigation optimization, risk analysis, and compliance. (Herman, 2009; Stephens, 2008)

5. Challenges Adoption of Semantic Web technologies by information systems faces some challenges, and in this section we briefly summarise some of these challenges. As ontology is the heart of the semantic evolution, a lot of challenges facing Semantic Web adoption revolves around it, one of the biggest challenges is the domain ontology development and evolution, other challenges could be summarised as: saleability of web content, annotation management, ontology-based information retrieval, multilingualism, and the technologies standardisation. (Benjamins et al, 2002; Chebotko et al, 2004)

6. Conclusion In this paper I introduced a summary of how information systems adoption of emerging Semantic Web technologies could present some vital solutions for existing and rising problems such as the information overload and also for fulfilling growing business demand of integration and collaboration. The main goals of Semantic Web will lead to a vast improvement for the way data sources are accessed and information is extracted, such improvement will be naturally reflected to information systems which rely mainly on data, information and knowledge sources and extraction. The enhancement Semantic Web technologies presents will facilitate remarkable advances in intelligent decision support, positively affecting many existing types of information systems like DSS, KBDSS, and ESS.

References Antoniou, G. and Harmelen, F. (2008) A Semantic Web Primer, 2nd ed. Cambridge, Massachusetts: The MIT Press Berners-Lee, T. (2000) Semantic Web – XML2000. [online]. Available from: http://www.w3.org/2000/Talks/1206xml2k-tbl/Overview.html [Accessed 20 December 2009] Berners-Lee, T., Hendler, J. and Lassila, O. (2001) The Semantic Web, Scientific American Magazine, 284 (May 2001), pp. 34-43. Benjamins, V.R., Contreras, J., Oscar, C. and Gómez-Pérez, A.(2002) Six Challenges for the Semantic Web. [online]. Available from: http://www.dia.fi.upm.es/~ocorcho/documents/KRR2002WS_BenjaminsEtAl.pdf. [Accessed: 20 December 2009] Cardoso, J., Lytras, M. and Hepp, M. (2008) THE FUTURE OF THE SEMANTIC WEB FOR ENTERPRISES in The Semantic Web: Real world applications from industry, New York: Springer Chebotko, A., Lu, S. and Fotouhi, F.(2004) Challenges for information systems towards the Semantic Web. [online]. Available from: http://en.scientificcommons.org/50067527 . [Accessed: 21 December 2009] Daconta, M. C., Obrst, L. J., Smith, K. T. (2003) The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management, Indiana: Wiley Publishing Geroimenko, V. (2006) The Concept and Architecture of the Semantic Web, in Geroimenko, V., Chen, C. (ed.) Visualizing the Semantic Web: XML-Based Interent and Information Visualization, 2nd ed. New York: Springer Herman, I. (2009)Semantic Web Adoption and Applications. [Online]. Available http://www.w3.org/People/Ivan/CorePresentations/Applications. [Accessed 20 December 2009]

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