Brian R. Gaines and Mildred L. G. Shaw
Knowledge
Science Institute
University of Calgary
Alberta, Canada T2N
1N4
{mildred, gaines}@cpsc.ucalgary.ca
http://ksi.cpsc.ucalgary.ca/KSI/
Abstract: The development of knowledge-based systems involves the
management of a diversity of knowledge sources, computing resources and system
users, often geographically distributed. The knowledge acquisition, modeling
and representation communities have developed a wide range of tools relevant to
the development and management of large-scale knowledge-based systems, but the
majority of these tools run on individual workstations and use specialist data
formats making system integration and knowledge interchange very problematic.
The World Wide Web is a distributed hypermedia system available internationally
through the Internet. It provides general-purpose client-server technology
which supports interaction through documents with embedded graphic user
interfaces. This paper reports on the development of open architecture
knowledge management tools operating through the web to support knowledge
acquisition, representation and inference through semantic networks and
repertory grids. It illustrates how web technology provides a new knowledge
medium in which knowledge-based system methodologies and tools can be
integrated with hypermedia technologies to provide a new generation of
knowledge management facilities.
The development of knowledge-based systems involves knowledge acquisition from a diversity of sources often geographically distributed. The sources include books, papers, manuals, videos of expert performance, transcripts of protocols and interviews, and human and computer interaction with experts. Expert time is usually a scarce resource and experts are often only accessible at different sites, particularly in international projects. Knowledge acquisition methodologies and tools have developed to take account of these issues by using hypermedia systems to manage a large volume of heterogeneous data, interactive graphic interfaces to present knowledge models in a form understandable to experts, rapid prototyping systems to test the models in operation, and model comparison systems to draw attention to anomalous variations between experts.
However, existing knowledge acquisition tools are largely based on personal computers and graphic workstations, and their use in a distributed community involves moving software and often computers from site to site. The process of building and testing knowledge models across a distributed community would be greatly expedited if wide-area networks could be used to coordinate the activities at different sites. The initial objective of the work reported in this paper has been to use the World Wide Web to support distributed knowledge acquisition by porting existing knowledge acquisition tools to operate through the web. A further objective has been to use the web on a continuing basis to support distributed knowledge management in which the acquisition, representation and application of knowledge become an integral part of the activities of an organization.
The next section gives an overview of the web architecture and protocols, and following sections illustrate how knowledge acquisition systems have been ported to the web to operate in a distributed client-server environment.
The World Wide Web was conceived by Berners-Lee in March 1989 as a "hypertext project" to organize documents at CERN (Berners-Lee and Cailliau, 1990). The design involved a simple hypertext markup language that authors could enter through a word processor, distributed servers running on machines anywhere on the network, and access through any terminal. In March 1993 the web was still being presented as primarily a hypermedia retrieval system (Berners-Lee, 1993), but in November that year a development took place that radically changed the nature of the web. Andreessen (1993) issued NCSA Mosaic version 2 using tags to encode definitions of Motif widgets embedded within a hypermedia document, and allowed the state of those widgets within the client to be transmitted to the server. With the addition of such forms providing basic graphic user interface capabilities, the web protocols became the basis of general interactive, distributed, client-server information systems.
The hypertext transfer protocol (HTTP) used between web clients and servers is a generic, stateless, object-oriented protocol that uses MIME (Borenstein and Freed, 1993) content encoding to transmit arbitrary data. The content encoding used for web documents is HTML (Berners-Lee, Connolly and Muldrow, 1994) which is a document type definition in the ISO 8879 Standard Generalized Markup Language (SGML, Goldfarb, 1990). The adoption of an SGML tagged encoding schema allows web documents to include not only text, typographic and multimedia material but also to carry arbitrary additional data. HTML is being standardized at four levels:
Figure 1 shows the client-server architecture of the web. A client accesses servers on the Internet using various protocols. It communicates with various helper applications that extend its functionality. When it accesses a web server using the HTTP protocol that server can also access various helper applications through server gateways.
Figure 1 Client-server architecture of World Wid Web
Knowledge acquisition systems can be implemented on the web as: client helpers running locally on the same machine as the web browser and communicating with it through its application program interface; or auxiliary servers running remotely on the same machine or network as the web server and communicating with it through its common gateway interface. The advantages of client helper implementation are that existing knowledge acquisition tools can be used, and the full power of the local machine graphic user interface is available. The disadvantages are that the tool needs to be implemented for all platforms, minimally UNIX, PC and Mac, and the tool has to be distributed to all users. The advantages of auxiliary server implementation are that a single implementation for one server platform only needs to be developed and maintained, and the program does not have to be issued to other sites. The disadvantages are the limited capabilities of the user interfaces encoded in HTML forms and the transmission delays of operating through the network.
Figure 2 gives an overview of what is involved in factoring an application to operate in client-server mode through the web using existing web browsers as clients. At the left the client component consists of a web browser providing a graphic-user interface through:
Figure 2 Factoring an application for client-server operation through the web
At the left center, the client communicates through the net with a web server that is either an implementation of the HTTP protocol in the application server component, or a standard HTTP server providing a gateway to the application server component.
At the right the server component splits into 4 parts:
In addition, at the lower left the persistent storage for the application has to be factored to be represented either in hidden fields which can be stored as an HTML document at the client or in files or a database stored at the server, or some combination of both.
The following sections exemplify the implementation of major knowledge acquisition tools as client helpers and auxiliary servers operating through the web.
The World Wide Web has been used in a number of knowledge-based system developments. Rice, Farquhar, Piernot and Gruber (1995, 1996) have developed a web interface to the Ontolingua ontology construction tool, and Farquhar, Fikes, Pratt and Rice (1995) have given examples of its application to collaborative ontology construction for information integration. Eriksson (1995) has made some of the PROTÉGÉ-II knowledge-acquisition tools accessible through the web. The SHADE (SHAred Dependency Engineering) project (McGuire, Kuokka, Weber, Tenenbaum, Gruber and Olsen, 1993) supports reconfigurable exchange of engineering knowledge among computer-aided engineering systems by using agents providing services through the Internet. These agents are generally accessible through the KQML agent-communication language (Finin, Weber, Wiederhold, Genesereth, Fritzson, McKay, McGuire, Shapiro and Beck, 1992) and have been used to support systems operating through the web. For example, Gruber and Gauthier (1993) have used them in providing a Device Modeling Environment on the web, and have demonstrated its use in the simulation of a leak in the Space Shuttle's reaction control system using clickable maps as mimic diagrams.
Gaines and Shaw (1995b, 1995a) have described the use of concept mapping tools as client helpers and auxiliary web servers, and the porting of repertory grid tools to operate as a service on the World Wide Web (Shaw and Gaines, 1995). The following sections describe this research in its application to knowledge modeling through semantics networks and extended repertory grids.
The graphic representation of formal knowledge structures in semantic networks is common in most knowledge acquisition tools because experts find it easier to understand and critique the knowledge model when presented in diagrammatic form. The graphic user interface available through HTML is currently inadequate to support diagrammatic editing, but existing semantic net tools can be interfaced to the web as client helpers. They can then use the web as a multimedia data management system for informal knowledge that is linked to the formal structures developed from it. Figure 3 shows a semantic network editor in the front window communicating with a web browser in the rear window. The web document shown is the specification of the Sisyphus room allocation problem (Linster, 1994). The decision procedures in the document are being translated into formal knowledge structures in a semantic network (Gaines, 1994).
Figure 3 Generation of a semantic net linked to a web document
The knowledge engineer has annotated the document with HTML anchor tags making the significant definitions available through hypertext links. When she clicks on a link in the web browser to access a definition the browser scrolls to that definition and also transmits its URL (uniform resource locator) to the semantic net editor. The knowledge engineer copies text from the document, pastes it to the editor to name a knowledge element and clicks on one of the buttons at the top of the editor to create a graphic representation of that element. The editor automatically stores the URL sent by the browser as data attached to that element, so that the formal structure is linked to the original text and can be used to access it on the web. For example, in Figure 3 the cursor is over the concept "group head without office" and has changed to a button shape to indicate that linked data is available. Clicking on the concept sends a request through the browser to the server to show the relevant text which appears at the top of the browser as shown.
The use of a semantic network editor as a client helper enables formal knowledge structures to be developed but does not in itself make them available through the web for inspection by others at remote sites. This is done by providing a facility in the editor for uploading the knowledge structure from the helper to the web server using the capability of the browser to post data to the server under control of the editor--the knowledge engineer simply selects the "Upload" option in a popup menu that appears when she clicks outside the knowledge structure. The server is then able to transmit the knowledge structure as an image in the Compuserve GIF format which acts as a clickable map in any web browser without requiring access to the editor. Figure 4 shows the semantic network of Figure 3 being used as a clickable map. The cursor is again over the concept "group head without office" and has changed to a hand to indicate that linked data is available. Clicking on the concept sends a request to the browser to show the relevant text which will appear at the top of the browser as before.
Figure 4 Semantic net uploaded to web as a clickable map
Figure 5 shows the HTML generating the document in Figure 4. The line beginning "<IMG SRC" gives the URL of the image and defines it through "ISMAP" as a clickable map. When the browser receives this line it requests that the image be sent, and the server loads the semantic network, converts it to a GIF and returns it to the browser for display. The line beginning "<A HREF" gives the URL to which the coordinates are to be sent when the mouse is clicked with the cursor within the map. The two URL's are identical because the server can distinguish a request for an image from a notification that a map has been clicked. When the server receives the coordinates of the click it again loads the semantic network, computes what node has been clicked, retrieves the hidden data and sends the browser a redirection command indicating the URL that it should retrieve.
<HTML><HEAD><TITLE>Employee Rules</TITLE></HEAD> <BODY><H1 ALIGN="CENTER">Employee Rules</H1> <P ALIGN="CENTER"> <A HREF="http://www.noodle.org/WebMap/users/ferio/Employee%20Rules.k"> <IMG SRC="http://www.noodle.org/WebMap/users/ferio/Employee%20Rules.k" ISMAP> </A> </BODY></HTML>
Figure 5 HTML generating document in Figure 4
The HTML specifications of Figure 5 may be embedded within any document, making active semantic networks available as was done with the KWrite knowledge-based word processor (Gaines and Shaw, 1992; Gaines and Shaw, 1993c). Thus, a knowledge structure developed locally can be made available globally together with its linked derivation so that knowledge acquisition and validation can proceed on a distributed basis. The network uploaded is the full data structure so that it also becomes available for download to those having the semantic network editor, and it can be compiled at the server to run as part of a knowledge base operating through a server gateway and accessible through the web. Hence, a web document containing links to semantic networks as shown in Figure 5 not only serves to display them as active diagrams, but also serves as a knowledge base allowing the networks to be loaded and used for problem solving. Moreover, since the document is available over the Internet, the inference system can be a server agent at a different site from the document that is sent the document URL, uses this to determine the URL's of the knowledge structures, fetches these using the HTTP protocol, and then uses them to manage an interactive problem-solving interaction across the web.
Repertory grids based on personal construct psychology (Kelly, 1955) have been used for knowledge acquisition since the early years of knowledge-based system development (Shaw and Gaines, 1983; Boose, 1984) and have been refined over the years to support increasingly complex knowledge structures (Boose, Bradshaw, Koszarek and Shema, 1993; Ford, Bradshaw, Adams-Webber and Agnew, 1993; Gaines and Shaw, 1993a). Since grid elicitation tools are used directly by experts it would be very useful to have them accessible through any personal computer or workstation with access to the web. This is feasible because the primary data input format is through rating scales, and this can be done effectively by using popup menus which are available through the HTML graphic user interface.
Figure 6 shows the initial screen of a repertory grid elicitation tool operating through the web using a standard web browser and requiring no client helpers--an example of the client-server architecture shown in Figure 2. The HTML form requests the usual data required to initiate grid elicitation: user name; domain and context; terms for elements and constructs; default rating scale; data types allowed; and a list of initial elements. It also allows the subsequent screens to be customized with an HTML specification of a header and trailer--this capability to include links to multimedia web data is also used to allow annotation, text and pictures, to be attached to elements.
Figure 6 Repertory grid elicitation initial screen
The problem domain is that of a proposed Sisyphus problem on the classification of igneous rocks (Shadbolt, 1996), and the interaction reproduces one of the example datasets. When the user clicks on the "Done" button at the bottom, the browser transmits the data entered to the remote server which passes it through its common gateway interface to a specialist knowledge acquisition auxiliary server. The server processes the data and generates an HTML document that it returns to the browser resulting in the screen shown in Figure 7 eliciting a construct from a triad of elements.
Figure 7 Construct elicitation from a triad
The user clicks on a radio button to select an element which he construes as different from the other two and enters terms characterizing the construct. The pictures of the rocks were entered as HTML annotation of the elements by the knowledge engineer. The lower part of the screen allows the user to specify the data type with the default being a standard grid rating scale. He retains the default option and clicks on "Done". The server generates the screen shown on the left of Figure 8 which places a popup menu rating scale alongside each element enabling the user to rate each one along the new construct as shown on the right. He is also able to change the terms used if they seem inappropriate in the context of all the elements, and to change the ratings of the already entered elements if appropriate.
Figure 8 Rating elements on constructs
Clicking on the "Done" button in Figure 8 sends the ratings back to the server which generates the status screen shown in Figure 9 (shown after 8 elements and 5 constructs have been added). This draws the user's attention to matching constructs, suggesting a new element be added to break the match, to matching elements suggesting a new construct be added to break the match, and also offers the opportunity to elicit another construct from a pair or triad of elements. The elements and constructs are listed in selection boxes allowing selected items to be deleted and edited, new items to be added, matches to be shown, and HTML annotation to be added to elements. Further buttons enable the data elicited to be saved or to be displayed or analyzed in various ways.
Figure 9 Status screen showing matches
Figure 10 shows the output returned when the "FOCUS" button is used to develop a hierarchical knowledge model by sorting the elements and constructs so that similar ones are close together (Shaw, 1980). The results of analysis are usually presented as graphic output. This is generated at the server, converted to GIF format and returned to the client where it can be examined and saved if required. It can be seen that the four elements, gabbro, diorite, granodiorite and granite are very similar indicating that the constructs entered may be inadequate to distinguish them and that further discriminating constructs are required. It can also be seen that the two constructs, low silica content--high silica content and light - leucratic--dark - melanocratic are very similar indicating that they do not provide independent discrimination of the elements entered and that further elements might be elicited.
Figure 10 FOCUS hierarchical cluster analysis of grid
Figure 11 shows the output returned when the "PrinCom" button is used to provide a principal components analysis of the grid by rotating it in vector space to give maximum separation of elements in two dimensions (Slater, 1976; Gaines and Shaw, 1980). The results of analysis are again graphed, converted to GIF format and returned to the client where they can be examined and saved if required. It can be seen that there are two major dimensions, one being grain size and the other being four highly correlated constructs, again indicating that it would be appropriate to enter more elements.
Figure 11 PrinCom principal components analysis of grid
The knowledge acquisition server is designed to operate on a stateless basis in which data is not normally stored at the server unless it is specifically uploaded. This is achieved through the server storing all the grid data in hidden fields in the HTML document which do not display but are returned to the server along with the entered data. Figure 12 shows the first part of the HTML generating the document of Figure 9 where the hidden fields encoding the grid are apparent. The user can store the data at the client machine by saving the source of the document in a file, and can continue the elicitation and analysis at any time by loading this file.
<HTML><HEAD><TITLE>WebGrid Elicitation</TITLE></HEAD>
<BODY BGCOLOR="FFFFDD" TEXT="000066"><IMG SRC="http://tiger.cpsc.ucalgary.ca/WebGrid/WebGrid.gif" ALIGN="left" ALT="WebGrid Icon">
<H1 ALIGN="center">WebGrid
<I>Elicitation</I></H1><IMG SRC="/IMG/RAINBOWSEP.GIF"><BR>
<FORM ACTION="http://tiger.cpsc.ucalgary.ca/WebGrid/Main.k" METHOD=POST>
<INPUT TYPE="hidden" NAME="WebGrid" VALUE="11|-1377813985|6/9/96|9:39:55 AM">
<INPUT TYPE="hidden" NAME="Body" VALUE="{BODY BGCOLOR=\FFFFDD\ TEXT=\000066\}">
<INPUT TYPE="hidden" NAME="Line" VALUE="{IMG SRC=\/IMG/RAINBOWSEP.GIF\}{BR}">
<INPUT TYPE="hidden" NAME="Head" VALUE="2|0|3|9|5|8|R1 9|">
<INPUT TYPE="hidden" NAME="Labels" VALUE="RJP|igneous classifier|classification of igneous rocks|RJP||characteristic|characteristics|rock|rocks">
<INPUT TYPE="hidden" NAME="C0" VALUE="R1 8 0 10 10 1 9||fine grain size|coarse grain size">
<INPUT TYPE="hidden" NAME="C1" VALUE="R1 9 1 10 10 1 9||high silica content|low silica content">
<INPUT TYPE="hidden" NAME="C2" VALUE="R1 8 2 10 10 1 9||alkali feldspar rich|alkali feldspar poor">
<INPUT TYPE="hidden" NAME="C3" VALUE="R1 8 3 10 10 1 9||light - leucocratic|dark - melanocratic">
<INPUT TYPE="hidden" NAME="C4" VALUE="R1 8 4 10 10 1 9||olivine poor|olivine rich">
<INPUT TYPE="hidden" NAME="E0" VALUE="|0|1|2|1|0|rhyolite|{IMG SRC=\/rocky/rhyolite.jpg\}">
<INPUT TYPE="hidden" NAME="E1" VALUE="|0|4|0|4|1|andesite">
<INPUT TYPE="hidden" NAME="E2" VALUE="|0|6|8|8|6|basalt">
<INPUT TYPE="hidden" NAME="E3" VALUE="|8|0|2|0|0|granite|{IMG SRC=\/rocky/granite.jpg\}">
<INPUT TYPE="hidden" NAME="E4" VALUE="|8|1|1|2|0|granodiorite">
<INPUT TYPE="hidden" NAME="E5" VALUE="|8|3|0|3|0|diorite">
<INPUT TYPE="hidden" NAME="E6" VALUE="|8|5|0|5|0|gabbro|{IMG SRC=\/rocky/gabbro.jpg\}">
<INPUT TYPE="hidden" NAME="E7" VALUE="|8|6|8|7|3|peridotite">
You are considering <B>8</B> rocks and <B>5</B>
characteristics in the context of <B>classification of igneous
rocks</B>
<INPUT TYPE="Submit" NAME="Continue" VALUE="Continue">
<INPUT TYPE="hidden" NAME="CPair" VALUE="1|3">
<IMG SRC="/IMG/RAINBOWSEP.GIF"><BR>The characteristics
<B>high silica content--low silica content</B> and <B>light -
leucocratic--dark - melanocratic</B> are very similar -
click here if you want to enter another rock to distinguish them
<INPUT TYPE="Submit" NAME="CMatch" VALUE="Distinguish">
Figure 12 Initial section of HTML showing hidden fields in document in Figure 9
The embedded data in Figure 12 is readily extracted for use in other applications, and WebGrid also provides facilities to export the data in other interchange formats for use in offline programs such as RepGrid and StatView.
One major objective of many of our previous studies with knowledge elicitation tools has been to integrate them with hypermedia and inference engines to support all phases of knowledge-based system development within a unified framework. In particular KSS0 has been integrated with HyperCard and Nexpert (Gaines, Rappaport and Shaw, 1992) or Babylon (Gaines and Linster, 1990) in such a way that informal knowledge in hypermedia is made available as annotation to the knowledge acquisition and inference system, and cases tested in the inference system can be edited and posted back to KSS0 to revize the knowledge model.
The hypermedia annotation facilities of WebGrid have already been illustrated. It also supports inductive knowledge modeling and inference in a unified framework. Figure 13 shows the main screen of WebGrid replicating the KSS0 study of the Cendrowska contact lens dataset (Gaines and Shaw, 1993b).
Figure 13 Status screen for Cendrowska lens data set
When the "Induct" button in Figure 13 is clicked the inference rules shown in Figure 14 are returned, in this example as a pair of EDAG's (Gaines, 1995), one each for the two conclusion constructs specified.
Figure 14 Inference rules returned by Induct
When the "Test" button in Figure 13 is clicked the test case screen shown in Figure 15 is returned. This is familiar to the user because it is very similar to the screen for entering a new element, except that the conclusion constructs have been separated from the data entry ones. Inference has already been done using the rules developed by Induct, and has inferred, as one might expect, that it is open whether "not soft", "soft", "not hard" or "hard" may be inferred.
Figure 15 Test case entry
Figure 16 shows the screen returned when the user enters that the person is "not astigmatic" and clicks on the "Infer" button. The inference system has inferred that "not hard" is true but that "not soft" and "soft" are still open. The system uses proper open-class inference and will, for example, infer "not soft" and "not hard" if "reduced" is entered for tear production. If the user notes that the result of inference is incorrect, he or she may change the conclusions, enter more data and annotation and click on the "Add" button to add a new case to the grid. Running Induct or Test again generates a new knowledge model taking into account the additional case. Thus, inference with test cases is an integrated facility in knowledge elicitation.
In practice, the capability to iterate through a range of test cases using the screens shown in Figures 15 and 16 is generally highly instructive in understanding the knowledge modeling process. It also provides a simple "expert system" capability within WebGrid.
Figure 16 Inference with partial data entered for the test case
Once knowledge acquisition tools are available on the web it becomes possible to support distributed knowledge acquisition in which experts and knowledge engineers at different sites collaborate in developing a knowledge-based system. The repertory grid system described above has been extended to support the comparison of knowledge structures using an extension of the consensus/conflict/correspondence/contrast methodology described by Gaines and Shaw (1989). When a grid has been developed by an expert at one site, another expert at another site can develop a grid based on it, either attempting to rate the elements on the other expert's constructs to evaluate consensus and conflict, or developing her own constructs to evaluate correspondence and contrast.
Figure 17 shows the initial screen for an expert eliciting a grid based on the elements in another expert's grid--the soft machines study from an international intelligent manufacturing systems project (Gaines and Shaw, 1994). When the expert has entered their name and clicked on "Done" the server generates screens for triadic elicitation and rating similar to those of Figures 7 and 8, and elicitation proceeds as before.
Figure 17 Another expert starting a grid with same elements
Figure 18 shows the status screen after six constructs have been elicited, and it will be noted that a new "Compare" button is available underneath the analysis buttons in the lower part of the screen.
Figure 18 Status screen with compare option
Clicking on the "Compare" button generates the analysis shown in Figure 19 where each construct in the new grid has been matched with the closest corresponding construct in the original grid and the degree of match has been graphed on the right. This enables the experts to see where they have corresponding constructs either providing different terms for the same distinction or indicating some underlying relation, for example a causal link. They can also see the constructs and elements where they do not have correspondence and about which they may have difficulty in communicating.
The results can be discussed through email, Internet audio and videophone, or through more conventional means. In some cases the discussion may lead to a consensus on terminology and knowledge structures, in other cases to systems combining different perspectives and in others to systems that offer advice based on different and potentially conflicting sources of expertise (Boose and Bradshaw, 1987).
Figure 19 Comparison of grids from two experts
The wide range of technologies developed in artificial intelligence research have the potential to support knowledge-based system development across many disciplines, but access to them is currently not widely available. The technologies, tools and protocols developed for the World Wide Web make it possible to provide knowledge acquisition, representation and inference systems internationally to anyone with access to the Internet. Such knowledge-based technologies integrate naturally with other applications and hypermedia systems to provide a distributed knowledge medium capable of supporting the knowledge processes of professional communities.
This paper has illustrated what is possible through examples of the development of knowledge modeling tools operating through the web to provide knowledge acquisition, representation and inference through semantic networks and repertory grids. These systems were developed primarily for locally managed projects, and their availability has only been advertised through specialist list servers for the knowledge acquisition and personal construct psychology communities. However, in the period July-December 1995 WebGrid was accessed from 674 different sites in 30 countries. The web is essentially an anonymous medium and we do not track the activities of outside users. However, occasionally users contact us to discuss their applications and, if the local network goes down, we receive mail from serious users reporting the problem and requesting notification of when the knowledge acquisition programs will be available again. An interesting example of serendipitous use was a masters student in Holland studying operators' models of nuclear reactors who came across the grid elicitation system on the web in September, used it with his experts to elicit their conceptual models, and included these in an additional chapter in his thesis for examination in October--the web certainly accelerates research processes.
Web technology is improving rapidly, and many of the limitations of the current HTML graphic user interface will disappear when customized downloadable widgets become available through Sun's (1995) Java and Microsoft's (1994) OLE technologies. The scope for developing new systems through the integration of knowledge-based and other technologies from many different research groups will greatly increase as the web becomes used increasingly to make those technologies widely accessible. The social nature of knowledge processes will also become a significant factor in system development when it is possible to make those systems an integral part of the operation of major professional communities.
In conclusion, we note that the results presented in this paper demonstrate that the World Wide Web client server model operating through the Internet can effectively support the porting of existing knowledge acquisition, modeling and representation tools to operate in a distributed, open architecture environment. This opens up new possibilities for collaborative research in which the methodologies and tools developed by one group are made available on the network for use by other research groups world-wide. It enables past studies of the integration of hypermedia, knowledge acquisition and inference tools within a local environment (Gaines and Linster, 1990; Gaines et al., 1992) to be generalized to integration on a distributed basis across wide area networks. A major advantage of this approach is that tools can be operated at the site of the originators responsible for them, and their maintenance, upgrading and monitoring can be done very much more effectively than when the software is distributed to other sites.
We envision a Sisyphus collaborative research program internationally in which the first objective is to make tools available across the Internet in interoperable forms, and the second objective is to develop some integrated knowledge management systems that use these tools in a variety of configurations to provide a new generation of knowledge-based systems.
Financial assistance for this work has been made available by the Natural Sciences and Engineering Research Council of Canada.
WebGrid can be accessed at http://tiger.cpsc.ucalgary.ca/WebGrid/
WebMap can be obtained from ftp://ksi.cpsc.ucalgary.ca/KSI/Demos/KMap.sea.hqx
Related papers on WebGrid, WebMap and World Wide Web can be accessed through http://ksi.cpsc.ucalgary.ca/articles/
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