- Email: [email protected]
The Semantic Web and Web Services
ARTICLE IN PRESS
Information Systems 31 (2006) 229–231 www.elsevier.com/locate/infosys
Editorial
The Semantic Web and Web Services The Web as we know it represents a large, fragmented reservoir of information composed of haphazardly interrelated repositories associated with particular domains and enterprises. However, the information as it is presently stored usually does not have a clear meaning attached to it that would facilitate its retrieval and manipulation, both automated and manual. The main idea of the Semantic Web is to address this deficiency. Moreover, repositories and software systems are programmatically accessed by business processes that are internal to enterprises or are executed between enterprises in order to perform inter-enterprise cooperation like in the supply chain management domain. The definition of interfaces and their access at runtime is not uniform at all, and it is at a syntactic level only. Every enterprise uses different technology for the programmatic access. Web Services are proposed as a uniform and universal technology for this problem. The concept of Semantic Web has excited researchers in areas ranging from distributed information systems to artificial intelligence. Developers of future distributed information systems are also taking a close interest as many believe that, in some form, the Semantic Web will be a central component of their software constructions. Web Services have entered the research agendas of many research communities and are being proposed as the means for remote interoperable access of components and software systems, within and between organizations. Recently, the combination of both, so-called ‘‘Semantic Web Services,’’ have started to attract many researchers as the combination of the best of the two worlds. This special issue is about the Semantic Web and Semantic Web Services. The articles span a wide spectrum of research topics and research results. Each contributes to a specific aspect of the Semantic Web and Semantic Web Services. In the current (syntactic) Web, as well as in the Semantic Web, information search is still one of the major concerns. When users search for information they would like to see only relevant results. They do not like to retrieve data that are unrelated to their search string. In the current Web the difficulty of finding good search results lies in the correct interpretation of text that is not semantically annotated. Algorithms that interpret the syntactic content in order to return good search results are necessary. These algorithms can decide whether or not to take user context and the search history of users into consideration. In the article ‘Topic-Specific Crawling on the Web with the Measurements of the Relevancy Context Graph’ by Hsu and Wu a new approach is introduced that determines the relevance of a Web page in its context. This approach makes the search results more relevant than previous approaches. In ‘Modeling user Interests by Conceptual Clustering’ by Godoy and Amandi, the search history of users is taken into consideration during the search in order to determine only relevant search results. Personal search agents have access to historical information that is properly clustered in order to be a basis for the ongoing search. Even if data is structurally well represented it does not guarantee good search or query results. This is especially the case when the information space is scattered over several sources that have different formal 0306-4379/$ - see front matter r 2005 Published by Elsevier Ltd. doi:10.1016/j.is.2005.03.001
ARTICLE IN PRESS 230
Editorial / Information Systems 31 (2006) 229–231
schemas or meta-data. In this case, the difficulty lies in the correct integration of the various data sources in order to produce good search results. The notion of community helps since within a specific community the terminology can be expected to be relatively common, although not agreed upon. If the query is for a specific topic and a community exists around that topic (e.g., the community of travel providers) the search can focus on the data provided by this community excluding unrelated ones. The article ‘Towards Semantic-driven, Flexible and Scalable Framework for Peering and Querying e-Catalog Communities’ by Benatallah, Hacid, Paik, Rey and Toumani addresses the problem of querying heterogeneous structured data sources that are grouped by the notion of community. A framework is introduced that shows how the community concept for data integration works in detail. The Web is not only a vast store for heterogeneous general information, but it also stores user data, especially when users are interacting with web sites entering user information. This is typically the case in commercial web sites. A user buying a book might have to type in name, address, payment as well as other information that is user specific and private. Clearly, security conscious users want to be assured that their private data is dealt with properly in terms of privacy. Furthermore, users would like to understand upfront the privacy policies of web site providers. The contribution ‘PeCAN: An Architecture for Users’ Privacyaware Electronic Commerce Contexts of the Semantic Web’ by Jutla, Bodorik and Zhang proposes a privacy management architecture to assure users of the proper dealing of private data. Client-side and server-side aspects of privacy are distinguished in the architecture. Semantic Web Services are an extension of Web Services with an explicit representation of meanings. Efforts like the Web Service Modeling Framework1 (WSMO) propose a formal representation of Web Services that allow access to machine processable semantics. One aspect of the formal definition of Semantic Web Services is the description of the data that are processed by Semantic Web Services. Different application domains have different conceptualizations of their specific data sets. One example is the health care industry where different providers use different standards to describe their application data. The contribution ‘Artemis: Deploying Semantically Enriched Web Services in the Healthcare Domain’ by Dogac, Laleci, Kirbas, Kabak, Sinir, Yildiz and Gurcan discusses how healthcare data defined by healthcare standards can be formally represented and how these formal representations are used for defining Semantic Web Services. In the architecture a clear distinction is made between formally describing the functional Web Service interface and the application data themselves. Another important aspect of Semantic Web Service definition is the composition of Web Services. In many cases a single Web Service is not able to produce the desired result for a client. Instead several Web Services have to be composed in the appropriate way in order to produce the result. The article ‘Composition of Semantic Web Services using Linear Logic Theorem Proving’ by Rao, Kuengas and Matskin is concerned with the automatic composition of Web Services based on a given set of formally described Web Services. This automatic composition is based on Linear Logic and theorem proving. The article introduces the approach and an architecture with examples. In addition, Semantic Web Services have an execution model that defines how they are executed. A life cycle determines how Semantic Web Services are used and executed. When clients are not clear which Web Service to invoke they have to discover appropriate Web Services first. Discovery has as its core problem to determine based on a specific client request an appropriate set of Web Services that can fulfil the client’s requirements. The article ‘Ontology-based methodology for e-Services discovery’ by Bianchini, De Antonellis, Pernici and Plebani introduces a discovery approach based on a three-layer ontology, each layer describing Web Services more specifically in the form of an abstraction hierarchy. In addition, the article is cognisant of the fact that Web Service requester (clients) and Web Service provider have different perspectives.
1
http://www.wsmo.org.
ARTICLE IN PRESS Editorial / Information Systems 31 (2006) 229–231
231
After successful Web Service discovery Web Service requester in general will call one of the discovered Web Services, providing input data as necessary. Web Services, after having been executed successfully, return result data. Post-conditions define the correct set of data for the Web Service to be successful. However, in general there is no proof attached to the result data that clearly states why the result is correct. In the article ‘A Proof Markup Language for Semantic Web Services’ the authors Da Silva, McGuinness and Fikes introduce a proof markup language that allows a Web Service to formally provide a justification of how it derived particular result data. A user interface is introduced, too, that allows users to graphically examine proofs. The articles in this special issue are original work as a direct response to the call for contributions. The reviewers did a great job reviewing the article submissions and we would like to thank them here for their hard work. David Bell The Queen’s University of Belfast, Belfast BT7 1NN, UK E-mail address: [email protected] Christoph Bussler Digital Enterprise Research Institute (DERI), National University of Ireland, Galway, Galway, Ireland E-mail address: [email protected] Jian Yang Department of Computing Macquarrie University, Sydney, Australia E-mail address: [email protected]
Information Systems 31 (2006) 229–231 www.elsevier.com/locate/infosys
Editorial
The Semantic Web and Web Services The Web as we know it represents a large, fragmented reservoir of information composed of haphazardly interrelated repositories associated with particular domains and enterprises. However, the information as it is presently stored usually does not have a clear meaning attached to it that would facilitate its retrieval and manipulation, both automated and manual. The main idea of the Semantic Web is to address this deficiency. Moreover, repositories and software systems are programmatically accessed by business processes that are internal to enterprises or are executed between enterprises in order to perform inter-enterprise cooperation like in the supply chain management domain. The definition of interfaces and their access at runtime is not uniform at all, and it is at a syntactic level only. Every enterprise uses different technology for the programmatic access. Web Services are proposed as a uniform and universal technology for this problem. The concept of Semantic Web has excited researchers in areas ranging from distributed information systems to artificial intelligence. Developers of future distributed information systems are also taking a close interest as many believe that, in some form, the Semantic Web will be a central component of their software constructions. Web Services have entered the research agendas of many research communities and are being proposed as the means for remote interoperable access of components and software systems, within and between organizations. Recently, the combination of both, so-called ‘‘Semantic Web Services,’’ have started to attract many researchers as the combination of the best of the two worlds. This special issue is about the Semantic Web and Semantic Web Services. The articles span a wide spectrum of research topics and research results. Each contributes to a specific aspect of the Semantic Web and Semantic Web Services. In the current (syntactic) Web, as well as in the Semantic Web, information search is still one of the major concerns. When users search for information they would like to see only relevant results. They do not like to retrieve data that are unrelated to their search string. In the current Web the difficulty of finding good search results lies in the correct interpretation of text that is not semantically annotated. Algorithms that interpret the syntactic content in order to return good search results are necessary. These algorithms can decide whether or not to take user context and the search history of users into consideration. In the article ‘Topic-Specific Crawling on the Web with the Measurements of the Relevancy Context Graph’ by Hsu and Wu a new approach is introduced that determines the relevance of a Web page in its context. This approach makes the search results more relevant than previous approaches. In ‘Modeling user Interests by Conceptual Clustering’ by Godoy and Amandi, the search history of users is taken into consideration during the search in order to determine only relevant search results. Personal search agents have access to historical information that is properly clustered in order to be a basis for the ongoing search. Even if data is structurally well represented it does not guarantee good search or query results. This is especially the case when the information space is scattered over several sources that have different formal 0306-4379/$ - see front matter r 2005 Published by Elsevier Ltd. doi:10.1016/j.is.2005.03.001
ARTICLE IN PRESS 230
Editorial / Information Systems 31 (2006) 229–231
schemas or meta-data. In this case, the difficulty lies in the correct integration of the various data sources in order to produce good search results. The notion of community helps since within a specific community the terminology can be expected to be relatively common, although not agreed upon. If the query is for a specific topic and a community exists around that topic (e.g., the community of travel providers) the search can focus on the data provided by this community excluding unrelated ones. The article ‘Towards Semantic-driven, Flexible and Scalable Framework for Peering and Querying e-Catalog Communities’ by Benatallah, Hacid, Paik, Rey and Toumani addresses the problem of querying heterogeneous structured data sources that are grouped by the notion of community. A framework is introduced that shows how the community concept for data integration works in detail. The Web is not only a vast store for heterogeneous general information, but it also stores user data, especially when users are interacting with web sites entering user information. This is typically the case in commercial web sites. A user buying a book might have to type in name, address, payment as well as other information that is user specific and private. Clearly, security conscious users want to be assured that their private data is dealt with properly in terms of privacy. Furthermore, users would like to understand upfront the privacy policies of web site providers. The contribution ‘PeCAN: An Architecture for Users’ Privacyaware Electronic Commerce Contexts of the Semantic Web’ by Jutla, Bodorik and Zhang proposes a privacy management architecture to assure users of the proper dealing of private data. Client-side and server-side aspects of privacy are distinguished in the architecture. Semantic Web Services are an extension of Web Services with an explicit representation of meanings. Efforts like the Web Service Modeling Framework1 (WSMO) propose a formal representation of Web Services that allow access to machine processable semantics. One aspect of the formal definition of Semantic Web Services is the description of the data that are processed by Semantic Web Services. Different application domains have different conceptualizations of their specific data sets. One example is the health care industry where different providers use different standards to describe their application data. The contribution ‘Artemis: Deploying Semantically Enriched Web Services in the Healthcare Domain’ by Dogac, Laleci, Kirbas, Kabak, Sinir, Yildiz and Gurcan discusses how healthcare data defined by healthcare standards can be formally represented and how these formal representations are used for defining Semantic Web Services. In the architecture a clear distinction is made between formally describing the functional Web Service interface and the application data themselves. Another important aspect of Semantic Web Service definition is the composition of Web Services. In many cases a single Web Service is not able to produce the desired result for a client. Instead several Web Services have to be composed in the appropriate way in order to produce the result. The article ‘Composition of Semantic Web Services using Linear Logic Theorem Proving’ by Rao, Kuengas and Matskin is concerned with the automatic composition of Web Services based on a given set of formally described Web Services. This automatic composition is based on Linear Logic and theorem proving. The article introduces the approach and an architecture with examples. In addition, Semantic Web Services have an execution model that defines how they are executed. A life cycle determines how Semantic Web Services are used and executed. When clients are not clear which Web Service to invoke they have to discover appropriate Web Services first. Discovery has as its core problem to determine based on a specific client request an appropriate set of Web Services that can fulfil the client’s requirements. The article ‘Ontology-based methodology for e-Services discovery’ by Bianchini, De Antonellis, Pernici and Plebani introduces a discovery approach based on a three-layer ontology, each layer describing Web Services more specifically in the form of an abstraction hierarchy. In addition, the article is cognisant of the fact that Web Service requester (clients) and Web Service provider have different perspectives.
1
http://www.wsmo.org.
ARTICLE IN PRESS Editorial / Information Systems 31 (2006) 229–231
231
After successful Web Service discovery Web Service requester in general will call one of the discovered Web Services, providing input data as necessary. Web Services, after having been executed successfully, return result data. Post-conditions define the correct set of data for the Web Service to be successful. However, in general there is no proof attached to the result data that clearly states why the result is correct. In the article ‘A Proof Markup Language for Semantic Web Services’ the authors Da Silva, McGuinness and Fikes introduce a proof markup language that allows a Web Service to formally provide a justification of how it derived particular result data. A user interface is introduced, too, that allows users to graphically examine proofs. The articles in this special issue are original work as a direct response to the call for contributions. The reviewers did a great job reviewing the article submissions and we would like to thank them here for their hard work. David Bell The Queen’s University of Belfast, Belfast BT7 1NN, UK E-mail address: [email protected] Christoph Bussler Digital Enterprise Research Institute (DERI), National University of Ireland, Galway, Galway, Ireland E-mail address: [email protected] Jian Yang Department of Computing Macquarrie University, Sydney, Australia E-mail address: [email protected]