Ontology-based Semantic Web Services Framework for Knowledge Management System Zaihisma Che Cob Informatics Department College of IT, Universiti Tenaga National (UNITEN)
[email protected]
Abstract The latest Semantic Web developments and insights in knowledge management challenge the new era of semantic-based knowledge-management systems, where lies the new possibilities that Semantic Web affords for improved knowledge management. Connectivity and interoperability of knowledge management systems is the key to the vision of the future. We need a comprehensive framework that addresses main issues related to distributed Knowledge Management. Semantic Web Services (SWS) is the next major generation of the Web in which e-services and business communication become more knowledge-based. It proposes to extend the traditional Web Services technologies with its key enabling technologies of ontologies and semantics; to solve the problem of heterogeneity and interoperability of data across applications. This makes it possible to select, integrate and invocate services dynamically, which enable services to adapt themselves to changes without human intervention. The main purpose of this paper is to present the relevance of SWS technologies to KMS. Further, we discuss about the two major initiatives in SWS research. Later, we will propose ontology based semantic web services framework for KMS. Our focus is in the web service provider layer where we will introduce the three main components; knowledge manager, web service manager and ontology mapping manager.
1. Introduction The evolution of a Web that consisted mainly of huge collections of documents is upgraded to a new concept of Web technology that included data and
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Rusli Abdullah Information System Department, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia rusli @fsktm.upm.edu.my information for computers to manipulate, called the Semantic Web. The power of the Semantic Web comes from the coupling of the knowledge technologies developed by the AI world while the power grid is developed by the web developers [1]. At the heart of the Semantic Web is technology that makes it easier for people to find and correlate the information they need, disregard of their sources and types [2]. This new invention of a smarter web is predicted to have profound effects on the growth of knowledge and innovation. Berners-Lee, the inventor of the Web says that the Semantic Web, which he describes as a "web of data" in contrast to today's "web of documents," has great potential in giving a user the ability to see, understand, and manipulate data. The Semantic Web is expected to provide us with a more efficient way of representing and relating information. In order for this to occur, the Semantic Web relies on structured sets of information and inference rules that allow it to “understand” the relationship between different data resources. While the Semantic Web will ease the problem of finding related information, structuring information in a logical way, and other information headaches, there is one issue: the problem that the Semantic Web is trying to solve is being experienced right now, but the complete solution to the problem is still a work in progress. The most critical issue in intelligent knowledge management is how to represent and extract the semantic meaning from information contents. This problem has been addressed in diverse research areas including artificial intelligence, information retrieval, natural language processing, multimedia, knowledge management, etc. The Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation [3]. As the amount of
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available data continues to grow rapidly on the Web, it’s increasingly difficult for users to find, organize, access, and maintain the information they require. For the Web to reach its full potential, it must evolve into the Semantic Web, which provides a universally accessible platform that allows data to be shared and processed both by automated tools and people [4]. In “The Semantic Web: What, Why, How, and When” the author identify two main motivators for semantic web. The first is data integration, and the second motivator is more intelligent support for end users [5]. This is consistent with the other research, which discuss the objectives of Semantic Web and its potential benefits to web users [6]. In this paper, the authors describe three of the issues solved by Semantic Web; the linking of databases that relates to the integration of data, shared content across applications using schemas, and the critical emerging application of the discovery and combination of web services. Significantly in the aspect of knowledge management the demand for smarter web to assist in knowledge acquisition, knowledge representation, knowledge sharing and distribution of human knowledge through the Web becoming more trivial. The latest Semantic Web developments and insights in knowledge management challenge the new era of semantic-based knowledge-management systems, where lies the new possibilities that Semantic Web affords for improved knowledge management [7]. The Semantic Web is relevant to knowledge management because it has the potential to dramatically accelerate the speed with which information can be synthesized, by automating its aggregation and analysis. Information on the Web now is typically presented in HTML format, and while very beneficial in some respects, the format offers neither structure nor metadata that is useful for effective management. Without structure, elements of content cannot be related to each other, and without metadata, the nature of the elements themselves cannot be known. The Semantic Web is designed to provide the following missing components: structure, through the use of XML tags; metadata descriptors, through the Resource Description Framework (RDF); and relationships, through the Web Ontology Language (OWL). Although the field is still considered to be in its immaturity, many interested and enthusiast researcher and developer are already applying this technology. There are already applications and tools that use this conceptual approach to build semantic Web-based systems. For example, semantic Web services, semantic integration of tourism information
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sources, semantic digital libraries and semantic Bioinformatic systems. Semantic Web Services (SWS) research aims to automate the development of Web Service based applications through Semantic Web technology. By providing formal representations based on ontologies we can facilitate the machine interpretation of Web Service descriptions. Thus, business organizations can view Semantic Web Services as the basic mechanism for integrating data and processes across applications on the Web. The relatively new concept of Web services is important to knowledge management applications that may exchange functionality and information over the Internet. Web services provide a service-oriented approach to system specification, enable the componentization, wrapping and reuse of traditional applications, thereby allowing them to participate as an integrated component to knowledge management activity. This base of Semantic Web technologies involves Resource Description Framework (RDF) [8], DARPA Agent Markup Language (DAML) [9], Ontology Inference Layer (OIL) [10, 11], DAML+OIL [12], and Web Ontology Language (OWL) [13]. OWL-S [14] (formerly DAML-S [15] and an extension of OWL) is a leading specification for the semantic description of Web services in order to facilitate their automation. An interesting question is: How do Web services relate to the Semantic Web? The Semantic Web is “data integration across application, organizational boundaries”, and Web services are “program integration across application and organizational boundaries” [16]. Tim BernersLee stated that Web services are an actualization of the Semantic Web vision because the semantic markup of Web services makes them computerinterpretable, use-apparent, and agent-ready [17, 18].
2. Semantic Web Services for Knowledge Management System Sustaining enterprise development and retaining competitive advantage in the knowledge-based economy necessitates that firms focus on their knowledge assets. Importing knowledge from sources lying outside the organizational boundaries and harnessing knowledge across cross-organizational networks is critical. Related business models, together with emerging technologies like Web services and the Semantic Web, provide ample opportunities to develop appropriate infrastructures for online trading of knowledge goods and associated services.
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Although the challenges are significant, the case is very strong for knowledge management to make use of the Semantic Web. Searching unstructured data on the Web is a tedious and often unrewarding task, producing a large number of false hits and an even larger number of possible matches that need to be reviewed. In knowledge-intensive economy, the amount of available knowledge stored, (i.e digital libraries and other knowledge repositories) increases ever more rapidly, as does our reliance on being able to locate and exploit relevant information. Data interoperability is a key element in the goal of information superiority. The semantic part of data interoperability is therefore a knowledge management problem: How do we arrange for the right people to have the right (shared) knowledge about what the data means? Automated tools can help solve this problem [19]. Many researches have been done in relation to the issues of data integration and interoperability across the network. Organizations that extend their KM agenda support this issues can reap significant benefits and realize first-mover advantages in the coming knowledge commerce era. The use of technology in knowledge management is not new, and considerable experience has been built up by the early pioneers. Even before the availability of solutions such as Lotus Notes on which many contemporary knowledge management solutions are based, companies were deploying intranets, based on early generations of networking and computer technology that improved access to knowledge “on line.” Collaboration and knowledge sharing solutions also arose from the development of on-line conferencing and forums using mainframe computer technology. Today, of course, intranets and the Internet are ubiquitous, and we are rapidly approaching the situation where all the written information needed by a person to do his or her job is available on line. However, that is not to say that it can be used effectively with the tools currently available. KMS on the Internet provides a high level of flexibility and openness, but it still has many drawbacks due to the heterogeneity of the exchanged information, as such happen in bioinformatics field and other fields, which deals with massive amount of data from different sources and applications. Developments in the field of Semantic Web Services (SWS) show the opportunity of adding higher semantic levels to the existing frameworks, to improve their usage and ease scalability. In this paper, we propose a Semantic Web services framework for knowledge management system, in which data sources and services are made available
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through SWS, described by ontologies, allowing interoperability to create a comprehensive response adapted to services requested by users. Connectivity and interoperability of knowledge management systems is key to the vision of the future. Different systems and applications will be able to exchange and share data without the need for manual translation or re-entry. Connectivity throughout the extended applications will enable rapid integration of partners to form teams for new projects.
3. Ontologies Mapping One of the major components of the Semantic Web is ontology, and it becomes one fundamental difficulty of any Semantic Web Services [4]. For each domain of human knowledge, an ontology must be constructed, partly by hand and partly with the aid of automation tools. The use of ontologies in the escience community determines ultimate success for the Semantic Web [3]. Researchers in artificial intelligence first developed ontologies to facilitate knowledge sharing and reuse. Currently the topic becomes the talk of researchers in other fields such as database, Knowledge Representation, Bioinformatics, Semantic Web and so on. Even though ontology spurred interests in many people, the meaning of this concept still generates a lot of controversy in discussions. Ontology is the term referring to the shared understanding of some domains of interest, which is often conceived as a set of classes, relations, functions, axioms and instances [3]. In knowledge representation, ontology is a description of the concepts and relationships in an application domain. However, no matter how they define ontology, such a description must be understandable by humans and/or by software agents. Ontologies are not knowledge nor are they information. They are meta-information or information about information. In the context of the Semantic Web, they encode, using an ontology language, the relationships between the various terms within the information. The standard used of ontology for the Web has taxonomy and a set off inference rules. As such they are useful representations for knowledge on the semantic. Ontologies are widely regarded as one of the foundational technologies for the Semantic Web: when annotating web documents with machineinterpretable information concerning their content, the meaning of the terms used in such an annotation should be fixed in a (shared) ontology. Research in
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the Semantic Web has led to the standardization of specific web ontology languages. On the Semantic Web, data is envisioned to be annotated using ontologies. Ontologies convey background information which enriches the description of the data and which makes the context of the information more explicit. Because ontologies are shared specifications, the same ontologies can be used for the annotation of multiple data sources, not only Web pages, but also collections of XML documents, relational databases, etc. The use of such shared terminologies enables a certain degree of inter-operation between these data sources. This, however, does not solve the integration problem completely, because it cannot be expected that all individuals and organizations on the Semantic Web will ever agree on using one common terminology or ontology. It can be expected that many different ontologies will appear and, in order to enable interoperation, mediation is required between these ontologies. Semi automated discovery of Correspondence ontologies
Onto logy
Correspondence
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Union
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Figure 1: Ontology Mediation Ontology mediation enables reuse of data across applications on the Semantic Web and cooperation between different organizations. In the context of semantic knowledge management, ontology mediation is especially important to enable sharing of data between heterogeneous knowledge bases and to allow applications to reuse data from different knowledge bases. Another important application area for ontology mediation is Semantic Web Services. In general, it cannot be assumed that the requester and the provider of a service use the same terminology in their communication and thus mediation is required in order to enable communication between heterogeneous business partners [21]. In [21], the authors distinguish two principled kinds of ontology mediation: ontology mapping and ontology merging (Figure 1). With ontology mapping, the correspondences between two ontologies are stored separately from the Ontologies and thus are not part of the ontologies themselves. The correspondences can be used for, for example,
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4. Research Methodology
between
Ontol ogy
Ontol ogy
querying heterogeneous knowledge bases using a common interface or transforming data between different representations. The (semi-) automated discovery of such correspondences is called ontology alignment. When performing ontology merging, a new ontology is created which is the union of the source ontologies. The merged ontology captures all the knowledge from the original ontologies. The challenge in ontology merging is to ensure that all correspondences and differences between the ontologies are reflected in the merged ontology. Summarizing, ontology mapping is mostly concerned with the representation of correspondences between ontologies; ontology alignment is concerned with the discovery of these correspondences; and ontology merging is concerned with creating the union of ontologies, based on correspondences between the ontologies.
The methodology of this research is mostly inspired from several previous related researches. This research will apply the survey method in order to validate the proposed framework. It began with comprehensive reviews of literature about Knowledge Management, Semantic and web services technology. Based on the literature review, a conceptual model of ontology based framework for semantic web services for KMS was proposed. To provide support for the framework, we will use a SWS development environment, in which users can design the conceptual model of SWS through a graphical interface. Once finished, the model must be checked to guarantee its consistency and correctness. Then the SWS for KMS model can be converted into a specification, which will be complemented with Web service standard languages. Then, we will design a set of semantic web services Ontologies for KMS based on the framework proposed using existing language such as OWL-S, which is compatible with WSMO specification. Later, the execution environment to test the proposed framework will be developed. Figure 2 illustrates our research approach for this study.
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Conceptual Model of SWS for KMS (SWSKMS)
SWSKMS Ontology
Execution environment
Our approach is based on WSMO1 conceptual model which identifies four top level elements at the key aspects to define semantic web services; Ontologies, web services, goals and mediators as shown in Figure 3.
Figure 2: Research Approach
5. Related Work Semantic Web services (SWS) research is related to automating the development of Web service based applications through semantic Web technology. There are two major initiatives that work on developing a worldwide standard for the semantic description of Web services: OWL-S [22] and the Web Service Modeling Ontology1 (WSMO) [23]. OWL-S [22] is an OWL-based Web Service ontology, where service descriptions define what the service provides for the seeking agent, describe how the service works and what happens when it is carried out and specify how the service should be used in terms of the communication protocol, the message format etc. WSMO is a formal ontology for describing various aspects related to Semantic Web Services. The objective of WSMO and its surrounding efforts is to define a coherent technology for Semantic Web Services by providing the means for semi automated discovery, composition and execution of Web Services which are based on logical inferencemechanisms. The main difference in the conceptual model between WSMO and OWL-S is that WSMO differs between the service requester and the provider and it introduces the concept of mediators to enable the requester of services to use different terminologies than the provider. An extensive comparison of WSMO and OWL-S can be found in [24]. We adopt WSMO- a promising framework and one of the most significant Semantic Web Service frameworks proposed to date [25]. Further discussion about our proposed framework can be found in the next section.
6. Proposed Framework Our framework consisted of four main layers; User Interaction, Interface, Mediator, and Ontology. All these four layers will communicate with other layers for specific roles and responsibilities of each layer. The layers architecture enables each layer to behave independently.
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Figure 3: WSMO core elements The mapping of our proposed framework in relation to WSMO conceptual model is shown by the different texture pattern as illustrated in Figure 3 and Figure 4. Here is a brief description of the four layers in our proposed framework as shown in Figure 4:
6.1. User Interaction Layer: As we know, distributed KMS will enable several knowledge providers (individuals, groups, and organizations) to trade or share the knowledge that is stored in various forms in distributed repositories within their organizations. Our framework employs a Web services infrastructure and ontologically enhances it so the life cycle of a knowledge transaction is supported. Besides knowledge providers, another type of users is the knowledge service requestor in requesting for services from the KMS. This layer will describe the goals of different users of KMS so that each goal will be clearly defined and communicated to other layers to ensure that each services provided or requested will be completely achieved by the system.
6.2. Interfaces Layer An interface describes how the functionality of the Web service can be achieved by providing the operational competence of the Web service in terms of interaction that defines the communication and cooperation with other web services from different service providers. The Web service interface is meant primarily for behavioral description purposes of Web services and is presented in a way that is suitable for software agents to determine the behavior of the Web service and reason about it; it might be also useful for discovery and selection purposes and in this description the connection to some existing Web services specifications.
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User Interaction Layer
Knowledge Provider Community A
Community B
K-Repository A
Community C
K-Repository B K-Provider A
K-Repository C K-Provider B
K-Provider C
KS Requestor
Publish Semantic Web KMS Interface Semantic Web Services Provider K- Manager
Ontologies
User Ontology
SWS Manager SWS Manager
Domain Ontology
Ontologies Ontology Mapping Mapping Manager Manager
Mediator Layer
WS Ontology Ontology Layer
Figure 4: Proposed Ontology based Semantic Web Services Framework for KMS
6.3. Mediator Layer Mediation is concerned with handling heterogeneity, i.e., resolving possibly occurring mismatches between resources that ought to be interoperable. Heterogeneity naturally arises in open and distributed environments, and thus in the application areas of Semantic Web Services, WSMO1 defines the concept of Mediators as a top-level notion. In this layer, we identifies 3 main components; Knowledge Manager (K-Manager), Semantic Web Services Manager (SWS Manager) and Ontologies Mapping Manager. K- Manager- responsible for managing the knowledge provided by knowledge providers and describes the user’s profile which will be then linked to user ontology description for later reference. SWS Manager- responsible to manage the services offered by KMS. Knowledge service can be conceptualized as a means to provide access to a collection of knowledge objects, and is accompanied by a set of operations that can be performed on this resource. These operations include discovery, navigation, retrieval, and interaction. Since more than one knowledge repository (by various service providers) may be published, a means of discovering
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the proper knowledge service is required. Whenever a new service is available at this layer, it will be semantically described and properly linked to existing semantic descriptions in the WS ontology. Given a goal request this layer, it will (i) discover a candidate set of Web services, (ii) select the most appropriate, (iii) mediate any mismatches at the data, or ontological, and (iv) invoke the selected Web services whilst adhering to any data, control flow and Web service invocation requirements. To achieve this, the WS descriptions need to be supported by domain ontology from the ontology layer. Ontologies Mapping Manager- When importing ontologies in real world applications, some steps for aligning, merging and mapping imported ontologies in order to resolve ontology mismatches are needed. For this reason, Ontologies Mapping Manager is used when an alignment, merging and mapping of the imported ontology is necessary. Such an alignment can be for example the renaming of concepts, attributes or similar.
6.4. Ontology Layer This layer will provide support required by all the three components in mediator layer. It provides the
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semantic descriptions in three ontologies; user, domain and web services ontologies. User ontologies is composed of user-oriented concepts. It allows personalization for different user. Meanwhile, the domains ontologies are specialized for description of parameters of the knowledge objects. It specifies and describes the sub-class and super-class relationships for the relevant entities and properties of knowledge objects. Like domain ontologies, WS ontology describes services provide by KMS. It describes service hierarchies and a subclass of a service inherits the properties and functionality of its super-class service and extends its own attributes.
7. Results and Discussion Ontology is the key enabling power in realizing the full potential of SWS technology. It plays important roles in supporting the tasks of knowledge management. In the context of semantic knowledge management, ontology especially important to enable sharing of data between heterogeneous knowledge bases and to allow applications to reuse data from different knowledge bases which solve the interoperability problem across applications. Therefore, a framework that focuses on ontology to develop KMS on semantic web services platform is needed. Then only we can implement an intelligent KMS, which provides efficient and effective knowledge management services for users. Figure 5 shows the ontological flows of SWS for KMS. Knowledge providers and knowledge service requestors interact with the semantic KM Portal, which provides semantic knowledge services to the users. The knowledge providers are the primary knowledge source. This knowledge will be stored in the knowledge base repositories organized in a way that enable efficient storing and access. The highlighted circle in the figure becomes the main focus in our proposed framework. This is where the central focus on ontology resides to realize the SWS potential for KMS.
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Knowledge Provider Semantic KM Portal
Knowledge Base
Semantic Knowledge Services Provider K-Acquisition
K-Storing
K-Usage
K-Dissemination WS Ontologies
Service Requestor
Figure 5: Ontological Flow of Semantic Web Services for KMS The design of the framework has been guided by a set of requirements that establish the conditions to define an open and extensible framework to develop SWSs defined in [26]. The three important requirements are; 1. SWS conceptual modeling which allow users to develop SWSs in language independent manner 2. Integration of SWS with Web Service standards to enable the use the current infrastructure that supports these standards and 3. Modular design, which achieved through the layered architecture that are composed of a set of independent, but related, modules, which contain knowledge about different views of the SWS development process.
8. Conclusion Semantic Web Services constitute one of the most promising research directions to improve the integration of applications within and across enterprise boundaries. Hence, a fully-fledged framework needs to be provided to glue all the KMS components for performing various tasks that to realize the automation of KM services. In this paper we presented an ontology-based Semantic Web Services framework for KMS. The proposed framework adopts WSMO and its conceptual model laid the foundation for our work. Our focus is in the web service provider layer where we will introduce the three main components; knowledge manager, web service manager and ontology mapping manager.
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9. References
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