Knowledge and Ontological Engineering: Directions for the Semantic Web

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1 Knowledge and Ontological Engineering: Directions for the Semantic Web Dana Vaughn and David J. Russomanno Department of Electrical and Computer Engineering The University of Memphis Memphis, TN { dvaughn3@midsouth.rr.com, d-russomanno@memphis.edu } Abstract. The exponential growth of the World Wide Web (WWW) in recent years has created a vast repository of documents primarily intended for human interpretation and use rather than serving as a formal knowledge and database that can be potentially utilized by machines realized as functional, intelligent agents. The Semantic Web's goal is to improve this situation by augmenting the classical WWW with an infrastructure for automated Web services by embedding semantic content within the web pages themselves, thereby making it possible for artificial, functional agents to perform intelligent tasks. The Semantic Web presents an application domain with a number of interesting research challenges including how to conceptualize, represent, acquire, organize, integrate, and reason about WWW data and knowledge sources. Of particular interest in the evolution of the Semantic Web is the issue of knowledge representation and ontologies. This paper presents an overview of some of the current research for ontological engineering and knowledge representation as applied to the Semantic Web and discusses the benefits and limitations of each, then concludes with a research proposal for one approach to leveraging the Semantic Web. Introduction The World Wide Web consists of several hundred million static and dynamic documents, and it continues to grow exponentially in size. This exponential growth has created a gigantic repository of useful, somewhat useful, and useless data, information, and knowledge from the perspective of an individual user. Complicating matters further is the fact that the majority of the information is represented in a format designed chiefly for human understanding, not machine processing. As this growth continues unabated, it has become obvious that conventional methods of exploring and exploiting the data will no longer be sufficient, and that searching through this unstructured data will only become more and more tedious unless new approaches along with supporting infrastructures are developed. Just such an approach has been proposed: The Semantic Web (Berners-Lee, Hendler, and Lassila, 2001). The Semantic Web is not a new World Wide Web; rather, it is an extension of the existing one. The Semantic Web proposes to add semantic content to the Web, thereby adding structure and meaning to it in a way that will make it more amenable to machine processing. This structure and semantics will be added in two ways: through the use of ontologies and the adaptation of XML or other knowledge representation languages. By embedding semantics and structure within the web pages 1

2 themselves, the meaningful content is more readily extracted by autonomous means. Two mature research areas that will influence both the evolution and applications of the Semantic Web are ontological engineering and knowledge representation. Ontological Engineering and the Semantic Web A good definition of an ontology is "a specification of a conceptualization" (Hendler, 2001). In other words, an ontology defines those types of things that will be reasoned about and represented within a certain domain. Ontologies can serve as metadata schemas, providing a precise vocabulary of concepts, each with explicit semantics. Furthermore, the defining of shared and common domain concepts will help people and machines to communicate more effectively (Maedche and Staab, 2001). Once the ontologies have been created, they can then be utilized by Semantic Web-site developers as well as knowledge engineers who will create the knowledge acquisition and reasoning systems that will exploit the available data, information, and knowledge in the Semantic Web. Figure 1 shows a sample of code from a simple ontology. <HTML> <Body> <ONTOLOGY ID="eece-dept-ontology" VERSION="1.0"> <DEF-CATEGORY NAME="ElectricalEngineering"> <DEF-RELATION NAME="isTeaching"> <DEF-ARG POS=1 TYPE ="Professor"> <DEF-ARG POS=2 TYPE ="Class"> </DEF-RELATION> </ONTOLOGY> Figure 1. A simple ontology The Semantic Web presents new challenges for ontological engineers, as well as knowledge engineers who want to build intelligent systems that exploit this repository. First, the World Wide Web is a highly distributed system. In other words, the number of providers of information is so great that inconsistencies in the information provided will be unavoidable. Second, since the Web is a dynamic environment, ontologies will need to evolve as the Web evolves. Third, the size of the Web creates the added concern of scalability. One way to address these issues is through ontology revision. Ontology revision means changing the ontology as new information becomes available. However, any web pages that depend on the previous ontology must be able to access it in its unmodified form. Otherwise, changes to ontologies will have far-reaching side effects. These are not the only issues concerning ontologies and the Semantic Web. In all likelihood, the Semantic Web will not consist of highly structured ontologies created by experts in their particular fields; rather, it will be composed of numerous small ontologies that leverage off one another. As such, it will be necessary for some level of interoperability and inheritance among ontologies. Also, the success of the Semantic Web will be highly dependent on how quickly domain specific ontologies can be developed or utilized (Maedche and Staab, 2001). 2

3 Several options will be available for those that want to build semantically rich web sites: creating an ontology from the outset or leveraging off an existing ontology. Creating a detailed and extensive ontology takes time and effort, so this will probably not be the approach used by the majority of people wishing to embrace the Semantic Web. Instead, users will turn to previously created ontologies and modify them to suit their individual needs. Several researchers have done considerable work in ontologies (Gruber, 1993; Hendler, 2001) and example ontologies can be found at a Web ontology repository. Of particular note is Cyc s ontology, an enormous knowledge base containing literally tens of thousands of terms about the range of human experiences. While Cyc s ontology is more than likely overkill for most users, it illustrates an important concept. There are already ontologies in existence, and more are being created by those best suited to represent the knowledge in a particular domain. Table 1. Representative Ontologies Name Description Website SENSUS Terminology taxonomy Cyc Multi-contextual knowledge base WordNet Online lexical reference Table 1 lists three ontologies, including Cyc. WordNet is an online lexical reference system wherein English nouns, verbs, adjectives, and adverbs are organized into synonym sets, each representing one underlying lexicalized concept. Different relations link the synonym sets. SENSUS is an extension of WordNet that rearranges the branches and includes other ontologies. Cyc, as mentioned above, is a comprehensive ontology covering the range of human experience. While Cyc has the advantage of covering a broader spectrum of ideas, the domain specific approaches of the other two may make them more powerful in their respective domain. Knowledge Representation The traditional Artificial Intelligence (AI) perspective is that the objective of knowledge representation is to express knowledge in a computer-tractable form (Russell and Norvig, 1995). A knowledge representation system in AI has traditionally been centralized, which requires all users to share definitions of common concepts. This idea of central control is very limiting, and it has a direct impact on the scalability of the knowledge representation system. Furthermore, knowledge representation systems typically limit the types of questions that can be asked of them to ones that can be answered reliably. A Semantic Web may change this traditional AI perspective. Extensible Markup Language (XML) is emerging as the de facto standard language of the Semantic Web. Since XML allows for user-defined tags, the meaning of the text between the tags can be represented in the tags themselves. The benefit of user-defined tags is that there is no limitation on what the tags mean; however, this flexibility is also its primary drawback. As an example, consider the following example: Let user A create a web page with the 3

4 tag <address> with intended meaning mailing address, while user B assigns the same <address> tag to his web pages. The corollary to that is when user A creates a tag, say <contact>, which means the same as < recipient> on user B s site. This use of the same tags, which mean different things, or different tags that mean the same thing, highlight one of the drawbacks to a purely XML-based Semantic Web. While the semantics may be obvious to a human interpreter, it will not necessarily be so obvious to an automated agent, which is why ontologies must be used in conjunction with XML. Another research language that has been proposed is SHOE, a web-based knowledge representation language that supports multiple ontologies (Heflin and Hender, 2000). SHOE, which stands for Simple HTML Ontology Extensions, allows web page designers to add ontology-based knowledge to web pages. SHOE associates meaning with the content by making each web page commit to one or more ontologies. These ontologies then allow for the discovery of implicit knowledge through taxonomies and inference rules. SHOE has the added benefit of promoting interoperability through its sharing and reuse of ontologies. At the core of all Semantic Web research and development is DAML, the DARPA Agent Markup Language program. DAML seeks to move beyond the simple semantics inherent in XML to a more fully realized semantic language that can be employed by knowledge engineers. With DAML, the goal is to develop a language that unifies the information available from a variety of sources. Whatever languages emerge for the Semantic Web, they will have to allow for ontology extension and revision, as well as provide semantic interoperability. Research Proposal The traditional role of knowledge engineering has been to investigate application domains, determine what the important concepts and ideas are within those domains, and create the formal representation of the objects and relations as they pertain to the domains, as well as the control and reasoning strategies that will be employed (Russell and Norvig, 1995). Knowledge engineers are usually not experts within a particular application domain, but are skilled in the representation of ideas. To gain knowledge about the domain requires interviews with the application experts within a field of study, an often costly and time-consuming process. But what if this process could be automated, or at least partially automated by artificial intelligent agents leveraging the Semantic Web rather than human application domain experts? We propose an architecture that supports the following problem-solving activity: a Knowledge Acquisition Agent (KAA) will extract data and knowledge from a semantically-enriched Web, using pre-existing ontologies and a goal of building a domain-specific knowledge base as its focus of attention as highlighted in Figure 2. Instead of focusing on the creation of the ontologies from scratch, the ontologies will use consist of the online repositories when searching the semantically-enriched Web. This approach has the advantage of efficiency, and will also more closely match the process the average user will go through when using the web. The ontologies may be extended as needed by the KAA to accommodate the specific application domain, but it must be stressed that ontologies will not be created by the KAA. 4

5 User Ontology A Ontology B... Ontology N Inference Engine KAA Knowledge Base Database WWW Domain Specific Expert System Figure 2. A proposed architecture for building application-specific expert systems Once the KAA has a particular set of ontologies in place that will serve as the basis for its knowledge acquisition, it will search the Web for the information that pertains to its goal: building a domain-specific knowledge base from the Semantic Web using a set of a prior ontologies. Based on the information it finds and its own set of inference rules, it will construct a knowledge base for a specific application domain as conceived by its underlying ontologies. This approach will not completely remove the need for evaluation by experts, but it should greatly enhance the creation of a knowledgebased system by taking advantage of the fact that an enormous amount of information, albeit unstructured or semi-structure, will be readily available on the World Wide Web and the evolving Semantic Web. This goal of this research is the creation of a domain independent Knowledge Acquisition Agent (KAA) capable of building domain specific knowledge bases from the Web. The KAA is domain independent in the sense that the underlying ontologies used will determine the concepts that will be employed to build an application specific knowledge base for a particular use. By changing the guiding ontologies, the associated knowledge gathered is changed as well; hence, supporting a means of building application specific knowledge bases. One ultimate application of KAA is that it would build the application specific knowledge bases in a variety of domains that users would subsequently interact with to seek advice, etc., rather than interacting with the Web directly. As an example, consider the case of a parent with a sick child in the middle of the night. The immediate question becomes, do the child's symptoms merit a trip to the emergency room or can the child wait until morning and a trip to the pediatrician? The information needed to make a diagnosis is readily available on the Web, but finding it when time is of the essence can be an impossible task. The KAA could change that. By simply giving the KAA a medical ontology of childhood illnesses and symptoms, the KAA could construct a knowledge base in its own time. Then, when the parent needs to ask a specific question, instead of fruitlessly combing the Web, the expert system, created by the KAA partly from the Web, is queried. This would greatly minimize the time spent searching, and would pay off in knowing whether a costly visit to the emergency room was warranted. 5

6 The same issues that arise when constructing a conventional knowledge base system will also have to be considered when constructing domain-specific, KB systems using KAA from the Web. Namely, deciding the domain, deciding the vocabulary, predicates, functions, and constants, encoding knowledge about the domain (how the information is taken from the Web and added into the KB), encoding descriptions of the specific problem instance, and deciding how to pose queries to the KB, and finally the quality and reliability of the system with respect to some metric. Fortunately, the use of ontologies will address some of these issues. The domain of choice (e.g., medicine) and the appropriate choice of ontologies will help address the first two issues. Furthermore, a well-thought out ontology should make posing queries easily implemented, since the vocabulary needed to pose queries will already be a part of the ontology. References Berners-Lee, T; Hendler, J; and Lassila, O. (2001) The Semantic Web, Scientific American, May 2001; Fensel, D; van Harmelen, F; Horrocks, I; McGuinness, D; and Patel-Schneider, P. (2001) OIL: An Ontology Infrastrucuture for the Semantic Web, IEEE Intelligent Systems, March/April 2001; Gruber, T. (1993) Toward Principles for the Design of Ontologies Used for Knowledge Sharing, Presented at the Padua workshop on Formal Ontology, Padova, Italy, Heflin, J. and Hendler, J. (2000) Dynamic Ontologies on the Web, In: Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-2000), AAAI/MIT Press, Menlo Park, CA; Hendler, J. (2001) Agents and the Semantic Web, IEEE Intelligent Systems, March/April 2001; Maedche, A. and Staab, S. (2001) Ontology Learning for the Semantic Web, IEEE Intelligent Systems, March/April 2001; Russell, S. and Norvig, P. (1995) Artificial Intelligence A Modern Approach, Prentice- Hall, Englewood Cliffs, NJ. 6

Development of an Ontology-Based Portal for Digital Archive Services

Development of an Ontology-Based Portal for Digital Archive Services Ching-Long Yeh Department of Computer Science and Engineering Tatung University 40 Chungshan N. Rd. 3rd Sec. Taipei, 104, Taiwan chingyeh@cse.ttu.edu.tw