Pages: pp. 50-53
From the walls of caves to the pages of books, human knowledge has found its way onto discrete surfaces as mostly printed matter. For the most part, the Internet has merely extended this display, albeit with added dimensions such as multimedia, virtual worlds, and hyperlinks—a "Version 0.8 (beta)" of Vannevar Bush's memex. 1
As the network transforms our work and facilitates our interactions with others, human knowledge is increasingly authored in the network and augmented by it. Researchers in business-process engineering, workflow management, and even architecture and interior design have long realized that organizational structures significantly influence workplace dynamics. The network is now the most flexible of structures, capable of adapting instantaneously to different projects and needs—allowing human knowledge to be connected and augmented in astoundingly complex ways.
Knowledge networking has been the subject of numerous research programs (Grid Computing, Knowledge and Distributed Intelligence, and others). The fundamental idea behind this research is the need for substantially new ways of thinking and working to leverage the Internet revolution in the advancement of human knowledge. We need new tools for capturing human knowledge as it evolves over the network, as well as tools for indexing and retrieving it for later reuse.
Consider the problem of capturing and reusing design rationale in the design of a large software system, where component development is outsourced, system integrators create custom solutions to enable code interoperability, and so on. Where does the reasoning behind design decisions and implementation solutions go? It exists in the ether of the interaction webs created by e-mail and collaborative work tools, in the software code and documentation, and in the minds of the developers and managers involved in the project. Our technologies and tools for capturing and reusing this knowledge are in their infancy, and draw on ideas from disciplines as diverse as distributed AI, computer-supported collaborative work (CSCW), psychology, and information science.
Knowledge networks are networks of people and computer systems that work on collaborative, knowledge-intensive tasks in a distributed—often virtual—enterprise. These networks will ensure that knowledge becomes an integral part of business practices, readily accessible for the task at hand—either proactively or passively. Knowledge needs to be gathered, filtered, and kept up-to-date. It must also be protected from unauthorized access.
From a business perspective, knowledge is pretty useless unless it affects action. If your decision is independent of a piece of information, there is no point in collecting the information. Hence, several approaches tie access to knowledge to the tasks of a business process (for example, see Maurer and Holz 2 and Kühn and Abecker 3). The major problem here is how to make sure that these knowledge-enriched process descriptions evolve according to the changing business environment: Maintenance is the big issue.
Knowledge networks utilize the Web as their basic means of communication. Distributed or even virtual enterprises can integrate their business processes and exchange data over the Internet. The Web makes knowledge accessible to humans. They can use a browser to read information that is made available elsewhere by a knowledge provider. As a knowledge seeker, the core problem is how to find the few diamonds for a task in the vast rough of information that is accessible on the Web. For finding information, we currently rely on tips, portals, and search engines:
How can we improve the precision (all information found for a query is useful) and recall (all useful information is found) of knowledge on the Web? One way is to use a knowledge representation language to annotate the information available and use an inference engine to overcome the limitations of keyword annotations. This would allow search engines to formally reason about the meaning of a specific page (that is, determine its semantics).
The article by Decker et al. ("The Semantic Web: The Roles of XML and RDF") in this issue describes one possible approach toward a semantic Web. They use RDF to represent ontological knowledge. (For other approaches that are based, for example, on XML representations, see the special issue of IEEE Intelligent Systems on Knowledge Management and the Internet. 4)
Using declarative AI-like representations and general-purpose reasoning engines will not answer all the questions regarding support for knowledge networks.
First, there is the question of effort. Who annotates all the Web pages with formal knowledge representations, who maintains it, and who makes sure that all the knowledge represented stays compatible so that you can post a query to a "semantic" search engine and actually get back results from several different Web sites?
Second, will these representations really improve precision and recall on an Internet scale? So far, there is no empirical data to answer this question.
Third, is the future of knowledge networks to provide services instead of information? Objects may exist on the Web and offer their standardized services to interested parties. Instead of defining semantics declaratively, a service defines semantics operationally. There are several trends in this direction, such as application servers based on component models like Enterprise JavaBeans and Microsoft COM, or the CORBA approach.
The article by Lu and Cai ("STARS: A Socio-Technical Framework for Integrating Design Knowledge over the Internet") shows what can be built on top of a service model. It examines knowledge networking in the context of distributed engineering design and manufacturing. Traditional design environments have focused on tools, integrating data so different tools can work together. Cai and Lu propose creating tools that address how people work together to resolve conflicts and move toward a final product. They call this a socio-technical framework for design and introduce us to some ideas that cross engineering, computing, and social science.
The theme articles in this issue of IEEE Internet Computing touch on some recent work in knowledge networking. We hope readers find them useful in understanding some of the fundamental research issues and enabling technologies by which we can all extend our own personal and professional knowledge networks.
Stephen C-Y. Lu and Jian Cai
Information technologies, including the Internet, are providing an infrastructure for global information sharing, but effective support of distributed collaborative work may require mechanisms to represent the expertise, intentions, and points of view of individual team members. This prototype system records social and technical aspects of individual perspectives on a collaborative project, and integrates this knowledge into a design process by reconciling different perspectives on the design data.
Stefan Decker, Sergey Melnik, Frank Van Harmelen, Dieter Fensel, Michel Klein, Jeen Broekstra, Michael Erdmann, and Ian Horrocks
The next generation of the World Wide Web has been referred to as the "Semantic Web," where information will be machine-processible in ways that support intelligent network services such as information brokers and search agents. The Semantic Web requires interoperability standards that address not only the syntactic form of documents but also the semantic content. Standardization efforts aiming at semantic interoperability include the World Wide Web Consortium's XML/XML Schema and RDF/RDF Schema. In this article, the authors propose a general method for encoding ontology representation and inference languages into RDF/RDF Schema, and illustrate it through an application to Ontology Interchange Language (OIL).