Wiki source code of SwSemanticWeb
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1 | 1 The Semantic Web | ||
2 | |||
3 | 1.1 A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities | ||
4 | |||
5 | By Tim Berners-Lee, James Hendler and Ora Lassila | ||
6 | |||
7 | The entertainment system was belting out the Beatles' "We Can Work It Out" when the phone rang. When Pete answered, his phone turned the sound down by sending a message to all the other ~~local~~ devices that had a ~~volume control~~. | ||
8 | His sister, Lucy, was on the line from the doctor's office: "Mom needs to see a specialist and then has to have | ||
9 | a series of physical therapy sessions. Biweekly or something. I'm going | ||
10 | to have my agent set up the appointments." Pete immediately agreed to | ||
11 | share the chauffeuring. | ||
12 | |||
13 | At the doctor's office, Lucy instructed her Semantic Web agent | ||
14 | through her handheld Web browser. The agent promptly retrieved | ||
15 | information about Mom's ~~prescribed treatment~~ from the doctor's agent, looked up several lists of ~~providers~~, and checked for the ones | ||
16 | ~~in-plan~~ for Mom's insurance within a ~~20-mile radius~~ of her ~~home~~ and with a ~~rating~~ of ~~excellent~~ or ~~very good~~ on trusted rating services. It then began trying to find a match between available ~~appointment times~~ | ||
17 | (supplied by the agents of individual providers through their Web | ||
18 | sites) and Pete's and Lucy's busy schedules. (The emphasized keywords | ||
19 | indicate terms whose semantics, or meaning, were defined for the agent | ||
20 | through the Semantic Web.) | ||
21 | |||
22 | In a few minutes the agent presented them with a plan. Pete | ||
23 | didn't like it University Hospital was all the way across town from | ||
24 | Mom's place, and he'd be driving back in the middle of rush hour. He | ||
25 | set his own agent to redo the search with stricter preferences about ~~location~~ and ~~time~~. Lucy's agent, having ~~complete trust~~ | ||
26 | in Pete's agent in the context of the present task, automatically | ||
27 | assisted by supplying access certificates and shortcuts to the data it | ||
28 | had already sorted through. | ||
29 | |||
30 | Almost instantly the new plan was presented: a much closer | ||
31 | clinic and earlier times but there were two warning notes. First, Pete | ||
32 | would have to reschedule a couple of his ~~less important~~ | ||
33 | appointments. He checked what they were not a problem. The other was | ||
34 | something about the insurance company's list failing to include this | ||
35 | provider under ~~physical therapists~~: "Service type and insurance plan status securely verified by other means," the agent reassured him. "(Details?)" | ||
36 | |||
37 | Lucy registered her assent at about the same moment Pete was muttering, | ||
38 | "Spare me the details," and it was all set. (Of course, Pete couldn't | ||
39 | resist the details and later that night had his agent explain how it | ||
40 | had found that provider even though it wasn't on the proper list.) | ||
41 | |||
42 | 1.1 Expressing Meaning | ||
43 | |||
44 | Pete and Lucy could use their agents to carry out all these tasks thanks not to the World Wide Web of today but | ||
45 | rather the Semantic Web that it will evolve into tomorrow. Most of the | ||
46 | Web's content today is designed for humans to read, not for computer | ||
47 | programs to manipulate meaningfully. Computers can adeptly parse Web | ||
48 | pages for layout and routine processing here a header, there a link to | ||
49 | another page but in general, computers have no reliable way to process | ||
50 | the semantics: this is the home page of the Hartman and Strauss Physio | ||
51 | Clinic, this link goes to Dr. Hartman's curriculum vitae. | ||
52 | |||
53 | The Semantic Web will bring structure to the meaningful content of | ||
54 | Web pages, creating an environment where software agents roaming from | ||
55 | page to page can readily carry out sophisticated tasks for users. Such | ||
56 | an agent coming to the clinic's Web page will know not just that the | ||
57 | page has keywords such as "treatment, medicine, physical, therapy" (as | ||
58 | might be encoded today) but also that Dr. Hartman ~~works~~ at this ~~clinic~~ on ~~Mondays~~, ~~Wednesdays~~ and ~~Fridays~~ and that the script takes a ~~date range~~ in ~~yyyy-mm-dd format~~ and returns ~~appointment times~~. | ||
59 | And it will "know" all this without needing artificial intelligence on | ||
60 | the scale of 2001's Hal or Star Wars's C-3PO. Instead these semantics | ||
61 | were encoded into the Web page when the clinic's office manager (who | ||
62 | never took Comp Sci 101) massaged it into shape using off-the-shelf | ||
63 | software for writing Semantic Web pages along with resources listed on | ||
64 | the Physical Therapy Association's site. | ||
65 | |||
66 | The Semantic Web is not a separate Web but an extension of the current | ||
67 | one, in which information is given well-defined meaning, better | ||
68 | enabling computers and people to work in cooperation. The first steps | ||
69 | in weaving the Semantic Web into the structure of the existing Web are | ||
70 | already under way. In the near future, these developments will usher in | ||
71 | significant new functionality as machines become much better able to | ||
72 | process and "understand" the data that they merely display at present. | ||
73 | |||
74 | The essential property of the World Wide Web is its | ||
75 | universality. The power of a hypertext link is that "anything can link | ||
76 | to anything." Web technology, therefore, must not discriminate between | ||
77 | the scribbled draft and the polished performance, between commercial | ||
78 | and academic information, or among cultures, languages, media and so | ||
79 | on. Information varies along many axes. One of these is the difference | ||
80 | between information produced primarily for human consumption and that | ||
81 | produced mainly for machines. At one end of the scale we have | ||
82 | everything from the five-second TV commercial to poetry. At the other | ||
83 | end we have databases, programs and sensor output. To date, the Web has | ||
84 | developed most rapidly as a medium of documents for people rather than | ||
85 | for data and information that can be processed automatically. The | ||
86 | Semantic Web aims to make up for this. | ||
87 | |||
88 | Like the Internet, the Semantic Web will be as decentralized as | ||
89 | possible. Such Web-like systems generate a lot of excitement at every | ||
90 | level, from major corporation to individual user, and provide benefits | ||
91 | that are hard or impossible to predict in advance. Decentralization | ||
92 | requires compromises: the Web had to throw away the ideal of total | ||
93 | consistency of all of its interconnections, ushering in the infamous | ||
94 | message "Error 404: Not Found" but allowing unchecked exponential | ||
95 | growth. | ||
96 | |||
97 | 1.1 Knowledge Representation | ||
98 | |||
99 | For the semantic web to function, computers must have access to structured collections of information and | ||
100 | sets of inference rules that they can use to conduct automated | ||
101 | reasoning. Artificial-intelligence researchers have studied such | ||
102 | systems since long before the Web was developed. Knowledge | ||
103 | representation, as this technology is often called, is currently in a | ||
104 | state comparable to that of hypertext before the advent of the Web: it | ||
105 | is clearly a good idea, and some very nice demonstrations exist, but it | ||
106 | has not yet changed the world. It contains the seeds of important | ||
107 | applications, but to realize its full potential it must be linked into | ||
108 | a single global system. | ||
109 | |||
110 | Traditional knowledge-representation systems typically have been | ||
111 | centralized, requiring everyone to share exactly the same definition of | ||
112 | common concepts such as "parent" or "vehicle." But central control is | ||
113 | stifling, and increasing the size and scope of such a system rapidly | ||
114 | becomes unmanageable. | ||
115 | |||
116 | Moreover, these systems usually carefully limit the questions | ||
117 | that can be asked so that the computer can answer reliably or answer | ||
118 | at all. The problem is reminiscent of Gidel's theorem from mathematics: | ||
119 | any system that is complex enough to be useful also encompasses | ||
120 | unanswerable questions, much like sophisticated versions of the basic | ||
121 | paradox "This sentence is false." To avoid such problems, traditional | ||
122 | knowledge-representation systems generally each had their own narrow | ||
123 | and idiosyncratic set of rules for making inferences about their data. | ||
124 | For example, a genealogy system, acting on a database of family trees, | ||
125 | might include the rule "a wife of an uncle is an aunt." Even if the | ||
126 | data could be transferred from one system to another, the rules, | ||
127 | existing in a completely different form, usually could not. | ||
128 | |||
129 | Semantic Web researchers, in contrast, accept that paradoxes and | ||
130 | unanswerable questions are a price that must be paid to achieve | ||
131 | versatility. We make the language for the rules as expressive as needed | ||
132 | to allow the Web to reason as widely as desired. This philosophy is | ||
133 | similar to that of the conventional Web: early in the Web's | ||
134 | development, detractors pointed out that it could never be a | ||
135 | well-organized library; without a central database and tree structure, | ||
136 | one would never be sure of finding everything. They were right. But the | ||
137 | expressive power of the system made vast amounts of information | ||
138 | available, and search engines (which would have seemed quite | ||
139 | impractical a decade ago) now produce remarkably complete indices of a | ||
140 | lot of the material out there. | ||
141 | The challenge of the Semantic Web, therefore, is to provide a language | ||
142 | that expresses both data and rules for reasoning about the data and | ||
143 | that allows rules from any existing knowledge-representation system to | ||
144 | be exported onto the Web. | ||
145 | |||
146 | Adding logic to the Web the means to use rules to make inferences, | ||
147 | choose courses of action and answer questions is the task before the | ||
148 | Semantic Web community at the moment. A mixture of mathematical and | ||
149 | engineering decisions complicate this task. The logic must be powerful | ||
150 | enough to describe complex properties of objects but not so powerful | ||
151 | that agents can be tricked by being asked to consider a paradox. | ||
152 | Fortunately, a large majority of the information we want to express is | ||
153 | along the lines of "a hex-head bolt is a type of machine bolt," which | ||
154 | is readily written in existing languages with a little extra | ||
155 | vocabulary. | ||
156 | |||
157 | Two important technologies for developing the Semantic Web are | ||
158 | already in place: eXtensible Markup Language (XML) and the Resource | ||
159 | Description Framework (RDF). XML lets everyone create their own | ||
160 | tags hidden labels such as or | ||
161 | that annotate Web pages or sections of text on a page. Scripts, or | ||
162 | programs, can make use of these tags in sophisticated ways, but the | ||
163 | script writer has to know what the page writer uses each tag for. In | ||
164 | short, XML allows users to add arbitrary structure to their documents | ||
165 | but says nothing about what the structures mean. | ||
166 | |||
167 | ---- | ||
168 | |||
169 | The Semantic Web will enable machines to COMPREHEND semantic documents and data, not human speech and writings. | ||
170 | |||
171 | ---- | ||
172 | |||
173 | Meaning is expressed by RDF, which encodes it in sets of triples, | ||
174 | each triple being rather like the subject, verb and object of an | ||
175 | elementary sentence. These triples can be written using XML tags. In | ||
176 | RDF, a document makes assertions that particular things (people, Web | ||
177 | pages or whatever) have properties (such as "is a sister of," "is the | ||
178 | author of") with certain values (another person, another Web page). | ||
179 | This structure turns out to be a natural way to describe the vast | ||
180 | majority of the data processed by machines. Subject and object are each | ||
181 | identified by a Universal Resource Identifier (URI), just as used in a | ||
182 | link on a Web page. (URLs, Uniform Resource Locators, are the most | ||
183 | common type of URI.) The verbs are also identified by URIs, which | ||
184 | enables anyone to define a new concept, a new verb, just by defining a | ||
185 | URI for it somewhere on the Web. | ||
186 | |||
187 | Human language thrives when using the same term to mean | ||
188 | somewhat different things, but automation does not. Imagine that I hire | ||
189 | a clown messenger service to deliver balloons to my customers on their | ||
190 | birthdays. Unfortunately, the service transfers the addresses from my | ||
191 | database to its database, not knowing that the "addresses" in mine are | ||
192 | where bills are sent and that many of them are post office boxes. My | ||
193 | hired clowns end up entertaining a number of postal workers not | ||
194 | necessarily a bad thing but certainly not the intended effect. Using a | ||
195 | different URI for each specific concept solves that problem. An address | ||
196 | that is a mailing address can be distinguished from one that is a | ||
197 | street address, and both can be distinguished from an address that is a | ||
198 | speech. | ||
199 | |||
200 | The triples of RDF form webs of information about related things. | ||
201 | Because RDF uses URIs to encode this information in a document, the | ||
202 | URIs ensure that concepts are not just words in a document but are tied | ||
203 | to a unique definition that everyone can find on the Web. For example, | ||
204 | imagine that we have access to a variety of databases with information | ||
205 | about people, including their addresses. If we want to find people | ||
206 | living in a specific zip code, we need to know which fields in each | ||
207 | database represent names and which represent zip codes. RDF can specify | ||
208 | that "(field 5 in database A) (is a field of type) (zip code)," using | ||
209 | URIs rather than phrases for each term. | ||
210 | |||
211 | 1.1 Ontologies | ||
212 | |||
213 | Of course, this is not the end of the story, because two databases may use different identifiers for what is in fact | ||
214 | the same concept, such as ~~zip code~~. | ||
215 | A program that wants to compare or combine information across the two | ||
216 | databases has to know that these two terms are being used to mean the | ||
217 | same thing. Ideally, the program must have a way to discover such | ||
218 | common meanings for whatever databases it encounters. | ||
219 | A solution to this problem is provided by the third basic component of | ||
220 | the Semantic Web, collections of information called ontologies. In | ||
221 | philosophy, an ontology is a theory about the nature of existence, of | ||
222 | what types of things exist; ontology as a discipline studies such | ||
223 | theories. Artificial-intelligence and Web researchers have co-opted the | ||
224 | term for their own jargon, and for them an ontology is a document or | ||
225 | file that formally defines the relations among terms. The most typical | ||
226 | kind of ontology for the Web has a taxonomy and a set of inference | ||
227 | rules. | ||
228 | |||
229 | The taxonomy defines classes of objects and relations among them. For example, an ~~address~~ may be defined as a type of ~~location~~, and ~~city codes~~ may be defined to apply only to ~~locations~~, | ||
230 | and so on. Classes, subclasses and relations among entities are a very | ||
231 | powerful tool for Web use. We can express a large number of relations | ||
232 | among entities by assigning properties to classes and allowing | ||
233 | subclasses to inherit such properties. If ~~city codes~~ must be of type ~~city~~ and cities generally have Web sites, we can discuss the Web site associated with a ~~city code~~ even if no database links a city code directly to a Web site. | ||
234 | |||
235 | Inference rules in ontologies supply further power. An ontology may | ||
236 | express the rule "If a city code is associated with a state code, and | ||
237 | an address uses that city code, then that address has the associated | ||
238 | state code." A program could then readily deduce, for instance, that a | ||
239 | Cornell University address, being in Ithaca, must be in New York State, | ||
240 | which is in the U.S., and therefore should be formatted to U.S. | ||
241 | standards. The computer doesn't truly "understand" any of this | ||
242 | information, but it can now manipulate the terms much more effectively | ||
243 | in ways that are useful and meaningful to the human user. | ||
244 | |||
245 | With ontology pages on the Web, solutions to terminology (and | ||
246 | other) problems begin to emerge. The meaning of terms or XML codes used | ||
247 | on a Web page can be defined by pointers from the page to an ontology. | ||
248 | Of course, the same problems as before now arise if I point to an | ||
249 | ontology that defines ~~addresses~~ as containing a ~~zip code~~ and you point to one that uses ~~postal code~~. | ||
250 | This kind of confusion can be resolved if ontologies (or other Web | ||
251 | services) provide equivalence relations: one or both of our ontologies | ||
252 | may contain the information that my zip code is equivalent to your | ||
253 | postal code. | ||
254 | |||
255 | Our scheme for sending in the clowns to entertain my customers | ||
256 | is partially solved when the two databases point to different | ||
257 | definitions of ~~address~~. | ||
258 | The program, using distinct URIs for different concepts of address, | ||
259 | will not confuse them and in fact will need to discover that the | ||
260 | concepts are related at all. The program could then use a service that | ||
261 | takes a list of ~~postal addresses~~ (defined in the first ontology) and converts it into a list of physical ~~addresses~~ | ||
262 | (the second ontology) by recognizing and removing post office boxes and | ||
263 | other unsuitable addresses. The structure and semantics provided by | ||
264 | ontologies make it easier for an entrepreneur to provide such a service | ||
265 | and can make its use completely transparent. | ||
266 | |||
267 | Ontologies can enhance the functioning of the Web in many ways. They | ||
268 | can be used in a simple fashion to improve the accuracy of Web | ||
269 | searches the search program can look for only those pages that refer to | ||
270 | a precise concept instead of all the ones using ambiguous keywords. | ||
271 | More advanced applications will use ontologies to relate the | ||
272 | information on a page to the associated knowledge structures and | ||
273 | inference rules. An example of a page marked up for such use is online | ||
274 | at [http://www.cs.umd.edu/~hendler>http://www.cs.umd.edu/~hendler>_blank]. If you send your Web browser to that | ||
275 | page, you will see the normal Web page entitled "Dr. James A. Hendler." | ||
276 | As a human, you can readily find the link to a short biographical note | ||
277 | and read there that Hendler received his Ph.D. from Brown University. A | ||
278 | computer program trying to find such information, however, would have | ||
279 | to be very complex to guess that this information might be in a | ||
280 | biography and to understand the English language used there. | ||
281 | |||
282 | For computers, the page is linked to an ontology page that defines | ||
283 | information about computer science departments. For instance, | ||
284 | professors work at universities and they generally have doctorates. | ||
285 | Further markup on the page (not displayed by the typical Web browser) | ||
286 | uses the ontology's concepts to specify that Hendler received his Ph.D. | ||
287 | from the entity described at the URI [http://www. brown.edu>http://www. brown.edu>_blank] the Web page for Brown. Computers can also find that | ||
288 | Hendler is a member of a particular research project, has a particular | ||
289 | e-mail address, and so on. All that information is readily processed by | ||
290 | a computer and could be used to answer queries (such as where Dr. | ||
291 | Hendler received his degree) that currently would require a human to | ||
292 | sift through the content of various pages turned up by a search engine. | ||
293 | |||
294 | In addition, this markup makes it much easier to develop | ||
295 | programs that can tackle complicated questions whose answers do not | ||
296 | reside on a single Web page. Suppose you wish to find the Ms. Cook you | ||
297 | met at a trade conference last year. You don't remember her first name, | ||
298 | but you remember that she worked for one of your clients and that her | ||
299 | son was a student at your alma mater. An intelligent search program can | ||
300 | sift through all the pages of people whose name is "Cook" (sidestepping | ||
301 | all the pages relating to cooks, cooking, the Cook Islands and so | ||
302 | forth), find the ones that mention working for a company that's on your | ||
303 | list of clients and follow links to Web pages of their children to | ||
304 | track down if any are in school at the right place. | ||
305 | |||
306 | 1.1 Agents | ||
307 | |||
308 | The real power of the Semantic Web will be realized when people create many programs that collect Web content from diverse | ||
309 | sources, process the information and exchange the results with other | ||
310 | programs. The effectiveness of such software agents will increase | ||
311 | exponentially as more machine-readable Web content and automated | ||
312 | services (including other agents) become available. The Semantic Web | ||
313 | promotes this synergy: even agents that were not expressly designed to | ||
314 | work together can transfer data among themselves when the data come | ||
315 | with semantics. | ||
316 | |||
317 | An important facet of agents' functioning will be the exchange of | ||
318 | "proofs" written in the Semantic Web's unifying language (the language | ||
319 | that expresses logical inferences made using rules and information such | ||
320 | as those specified by ontologies). For example, suppose Ms. Cook's | ||
321 | contact information has been located by an online service, and to your | ||
322 | great surprise it places her in Johannesburg. Naturally, you want to | ||
323 | check this, so your computer asks the service for a proof of its | ||
324 | answer, which it promptly provides by translating its internal | ||
325 | reasoning into the Semantic Web's unifying language. An inference | ||
326 | engine in your computer readily verifies that this Ms. Cook indeed | ||
327 | matches the one you were seeking, and it can show you the relevant Web | ||
328 | pages if you still have doubts. Although they are still far from | ||
329 | plumbing the depths of the Semantic Web's potential, some programs can | ||
330 | already exchange proofs in this way, using the current preliminary | ||
331 | versions of the unifying language. | ||
332 | |||
333 | Another vital feature will be digital signatures, which are encrypted | ||
334 | blocks of data that computers and agents can use to verify that the | ||
335 | attached information has been provided by a specific trusted source. | ||
336 | You want to be quite sure that a statement sent to your accounting | ||
337 | |||
338 | program that you owe money to an online retailer is not a forgery | ||
339 | generated by the computer-savvy teenager next door. Agents should be | ||
340 | skeptical of assertions that they read on the Semantic Web until they | ||
341 | have checked the sources of information. (We wish more ~~people~~ would learn to do this on the Web as it is!) | ||
342 | |||
343 | Many automated Web-based services already exist without semantics, but | ||
344 | other programs such as agents have no way to locate one that will | ||
345 | perform a specific function. This process, called service discovery, | ||
346 | can happen only when there is a common language to describe a service | ||
347 | in a way that lets other agents "understand" both the function offered | ||
348 | and how to take advantage of it. Services and agents can advertise | ||
349 | their function by, for example, depositing such descriptions in | ||
350 | directories analogous to the Yellow Pages. | ||
351 | |||
352 | Some low-level service-discovery schemes are currently available, such | ||
353 | as Microsoft's Universal Plug and Play, which focuses on connecting | ||
354 | different types of devices, and Sun Microsystems's Jini, which aims to | ||
355 | connect services. These initiatives, however, attack the problem at a | ||
356 | structural or syntactic level and rely heavily on standardization of a | ||
357 | predetermined set of functionality descriptions. Standardization can | ||
358 | only go so far, because we can't anticipate all possible future needs. | ||
359 | |||
360 | ---- | ||
361 | |||
362 | Properly designed, the Semantic Web can assist the evolution of human knowledge as a whole. | ||
363 | |||
364 | ---- | ||
365 | |||
366 | The Semantic Web, in contrast, is more flexible. The consumer and | ||
367 | producer agents can reach a shared understanding by exchanging | ||
368 | ontologies, which provide the vocabulary needed for discussion. Agents | ||
369 | can even "bootstrap" new reasoning capabilities when they discover new | ||
370 | ontologies. Semantics also makes it easier to take advantage of a | ||
371 | service that only partially matches a request. | ||
372 | |||
373 | A typical process will involve the creation of a "value chain" | ||
374 | in which subassemblies of information are passed from one agent to | ||
375 | another, each one "adding value," to construct the final product | ||
376 | requested by the end user. Make no mistake: to create complicated value | ||
377 | chains automatically on demand, some agents will exploit | ||
378 | artificial-intelligence technologies in addition to the Semantic Web. | ||
379 | But the Semantic Web will provide the foundations and the framework to | ||
380 | make such technologies more feasible. | ||
381 | |||
382 | Putting all these features together results in the abilities | ||
383 | exhibited by Pete's and Lucy's agents in the scenario that opened this | ||
384 | article. Their agents would have delegated the task in piecemeal | ||
385 | fashion to other services and agents discovered through service | ||
386 | advertisements. For example, they could have used a ~~trusted~~ service to take a list of ~~providers~~ and determine which of them are ~~in-plan~~ for a specified ~~insurance plan~~ and ~~course of treatment~~. | ||
387 | The list of providers would have been supplied by another search | ||
388 | service, et cetera. These activities formed chains in which a large | ||
389 | amount of data distributed across the Web (and almost worthless in that | ||
390 | form) was progressively reduced to the small amount of data of high | ||
391 | value to Pete and Lucy a plan of appointments to fit their schedules | ||
392 | and other requirements. | ||
393 | |||
394 | In the next step, the Semantic Web will break out of the | ||
395 | virtual realm and extend into our physical world. URIs can point to | ||
396 | anything, including physical entities, which means we can use the RDF | ||
397 | language to describe devices such as cell phones and TVs. Such devices | ||
398 | can advertise their functionality what they can do and how they are | ||
399 | controlled much like software agents. Being much more flexible than | ||
400 | low-level schemes such as Universal Plug and Play, such a semantic | ||
401 | approach opens up a world of exciting possibilities. | ||
402 | |||
403 | For instance, what today is called home automation requires careful | ||
404 | configuration for appliances to work together. Semantic descriptions of | ||
405 | device capabilities and functionality will let us achieve such | ||
406 | automation with minimal human intervention. A trivial example occurs | ||
407 | when Pete answers his phone and the stereo sound is turned down. | ||
408 | Instead of having to program each specific appliance, he could program | ||
409 | such a function once and for all to cover every ~~local~~ device that advertises having a ~~volume control~~ the TV, the DVD player and even the media players on the laptop that he brought home from work this one evening. | ||
410 | |||
411 | The first concrete steps have already been taken in this area, with | ||
412 | work on developing a standard for describing functional capabilities of | ||
413 | devices (such as screen sizes) and user preferences. Built on RDF, this | ||
414 | standard is called Composite Capability/Preference Profile (CC/PP). | ||
415 | Initially it will let cell phones and other nonstandard Web clients | ||
416 | describe their characteristics so that Web content can be tailored for | ||
417 | them on the fly. Later, when we add the full versatility of languages | ||
418 | for handling ontologies and logic, devices could automatically seek out | ||
419 | and employ services and other devices for added information or | ||
420 | functionality. It is not hard to imagine your Web-enabled microwave | ||
421 | oven consulting the frozen-food manufacturer's Web site for optimal | ||
422 | cooking parameters. | ||
423 | |||
424 | 1.1 Evolution of Knowledge | ||
425 | |||
426 | The semantic web is not "merely" the tool for conducting individual tasks that we have discussed so far. | ||
427 | In addition, if properly designed, the Semantic Web can assist the | ||
428 | evolution of human knowledge as a whole. | ||
429 | Human endeavor is caught in an eternal tension between the | ||
430 | effectiveness of small groups acting independently and the need to mesh | ||
431 | with the wider community. A small group can innovate rapidly and | ||
432 | efficiently, but this produces a subculture whose concepts are not | ||
433 | understood by others. Coordinating actions across a large group, | ||
434 | however, is painfully slow and takes an enormous amount of | ||
435 | communication. The world works across the spectrum between these | ||
436 | extremes, with a tendency to start small from the personal idea and | ||
437 | move toward a wider understanding over time. | ||
438 | |||
439 | An essential process is the joining together of subcultures | ||
440 | when a wider common language is needed. Often two groups independently | ||
441 | develop very similar concepts, and describing the relation between them | ||
442 | brings great benefits. Like a Finnish-English dictionary, or a | ||
443 | weights-and-measures conversion table, the relations allow | ||
444 | communication and collaboration even when the commonality of concept | ||
445 | has not (yet) led to a commonality of terms. | ||
446 | |||
447 | The Semantic Web, in naming every concept simply by a URI, lets | ||
448 | anyone express new concepts that they invent with minimal effort. Its | ||
449 | unifying logical language will enable these concepts to be | ||
450 | progressively linked into a universal Web. This structure will open up | ||
451 | the knowledge and workings of humankind to meaningful analysis by | ||
452 | software agents, providing a new class of tools by which we can live, | ||
453 | work and learn together. | ||
454 | |||
455 | ---- | ||
456 | |||
457 | *Further Information:* | ||
458 | |||
459 | *Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor.* \\ | ||
460 | Tim Berners-Lee, with Mark Fischetti. Harper San Francisco, 1999.\\ | ||
461 | An enhanced version of this article is on the Scientific American Web site, with additional material and links. | ||
462 | |||
463 | World Wide Web Consortium (W3C): [www.w3.org/>http://www.w3.org/>_blank] | ||
464 | |||
465 | W3C Semantic Web Activity: [www.w3.org/2001/sw/>http://www.w3.org/2001/sw/>_blank] | ||
466 | |||
467 | An introduction to ontologies: [www.semanticweb.org/knowmarkup.html>http://www.semanticweb.org/knowmarkup.html>_blank] | ||
468 | |||
469 | Simple HTML Ontology Extensions Frequently Asked Questions (SHOE FAQ): [www.cs.umd.edu/projects/plus/SHOE/faq.html>http://www.cs.umd.edu/projects/plus/SHOE/faq.html>_blank] | ||
470 | |||
471 | DARPA Agent Markup Language (DAML) home page: [www.daml.org/>http://www.daml.org/>_blank] |