Semantic web and Social networks Lecture notes

what is semantic web services, what is semantic web application, what is semantic web search and semantic web and information systems, foundations of semantic web xml rdf & ontology
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Second Edition A Semantic Web Primer Antoniou and van Harmelen computer science / Internet Second Edition A Semantic Web Primer Second Edition Grigoris Antoniou and Frank van Harmelen The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this still emerging field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for self-study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials. A Semantic Web Primer provides a systematic treatment of the different languages (XML, RDF, OWL, and rules) and technologies (explicit metadata, ontologies, and logic and inference) that are central to Semantic Web development as well as such crucial related topics as ontology engineering and application scenarios. This substantially revised and updated second edition reflects recent developments in the field, covering new application areas and tools. The new material includes a discussion of such topics as SPARQL as the RDF query language; OWL DLP and its interesting practical and theoretical properties; the SWRL language (in the chapter on rules); OWL-S (on which the discussion of Web services is now based). The new final chapter considers the state of the art of the field today, captures ongoing discussions, and outlines the most challenging issues facing the Semantic Web in the future. Supplementary materials, including slides, online versions of many of the code fragments in the book, and links to further reading, can be found at http://www.semanticwebprimer.org. Grigoris Antoniou is Professor at the Institute for Computer Science, FORTH (Foundation for Research and Technology– A Semantic Web Primer Hellas), Heraklion, Greece. Frank van Harmelen is Professor in the Department of Artificial Intelligence at the Vrije Universiteit, Amsterdam, the Netherlands. Grigoris Antoniou and Frank van Harmelen Cooperative Information Systems series “This book is essential reading for anyone who wishes to learn about the Semantic Web. By gathering the fundamental topics into a single volume, it spares the novice from having to read a dozen dense technical specifications. I have used the first edition in my Semantic Web course with much success.” —Jeff Heflin, Associate Professor, Department of Computer Science and Engineering, Lehigh University “This book provides a solid overview of the various core subjects that constitute the rapidly evolving Semantic Web discipline. While keeping most of the core concepts as presented in the first edition, the second edition contains valuable language updates, such as coverage of SPARQL, OWL DLP, SWRL, and OWL-S. The book truly provides a comprehensive view of the Semantic Web discipline and has all the ingredients that will help an instructor in planning, designing, and delivering the lectures for a graduate course on the subject.” —Isabel Cruz, Department of Computer Science, University of Illinois, Chicago 978-0-262-01242-3 The MIT Press Massachusetts Institute of Technology Cambridge, Massachusetts 02142 http://mitpress.mit.edu1 The Semantic Web Vision 1.1 Today’s Web The World Wide Web has changed the way people communicate with each other and the way business is conducted. It lies at the heart of a revolu- tion that is currently transforming the developed world toward a knowledge economy and, more broadly speaking, to a knowledge society. This development has also changed the way we think of computers. Orig- inally they were used for computing numerical calculations. Currently their predominant use is for information processing, typical applications being database systems, text processing, and games. At present there is a transi- tion of focus toward the view of computers as entry points to the information highways. Most of today’s Web content is suitable for human consumption. Even Web content that is generated automatically from databases is usually presented without the original structural information found in databases. Typical uses of the Web today involve people’s seeking and making use of information, searching for and getting in touch with other people, review- ing catalogs of online stores and ordering products by filling out forms, and viewing adult material. These activities are not particularly well supported by software tools. Apart from the existence of links that establish connections between docu- ments, the main valuable, indeed indispensable, tools are search engines. Keyword-based search engines such as Yahoo and Google are the main tools for using today’s Web. It is clear that the Web would not have become the huge success it is, were it not for search engines. However, there are serious problems associated with their use: • High recall, low precision. Even if the main relevant pages are retrieved, 12 1 The Semantic Web Vision they are of little use if another 28,758 mildly relevant or irrelevant docu- ments are also retrieved. Too much can easily become as bad as too little. • Low or no recall. Often it happens that we don’t get any relevant answer for our request, or that important and relevant pages are not retrieved. Al- though low recall is a less frequent problem with current search engines, it does occur. • Results are highly sensitive to vocabulary. Often our initial keywords do not get the results we want; in these cases the relevant documents use dif- ferent terminology from the original query. This is unsatisfactory because semantically similar queries should return similar results. • Results are single Web pages. If we need information that is spread over various documents, we must initiate several queries to collect the relevant documents, and then we must manually extract the partial information and put it together. Interestingly, despite improvements in search engine technology, the diffi- culties remain essentially the same. It seems that the amount of Web content outpaces technological progress. But even if a search is successful, it is the person who must browse selected documents to extract the information he is looking for. That is, there is not much support for retrieving the information, a very time-consuming activ- ity. Therefore, the term information retrieval, used in association with search engines, is somewhat misleading; location finder might be a more appropri- ate term. Also, results of Web searches are not readily accessible by other software tools; search engines are often isolated applications. The main obstacle to providing better support to Web users is that, at present, the meaning of Web content is not machine-accessible. Of course, there are tools that can retrieve texts, split them into parts, check the spelling, count their words. But when it comes tointerpreting sentences and extracting useful information for users, the capabilities of current software are still very limited. It is simply difficult to distinguish the meaning of I am a professor of computer science. from I am a professor of computer science, you may think. Well,...1.2 From Today’s Web to the Semantic Web: Examples 3 Using text processing, how can the current situation be improved? One so- lution is to use the content as it is represented today and to develop increas- ingly sophisticated techniques based on artificial intelligence and computa- tional linguistics. This approach has been followed for some time now, but despite some advances the task still appears too ambitious. An alternative approach is to represent Web content in a form that is more 1 easily machine-processable and to use intelligent techniques to take advan- tage of these representations. We refer to this plan of revolutionizing the Web as the Semantic Web initiative. It is important to understand that the Seman- tic Web will not be a new global information highway parallel to the existing World Wide Web; instead it will gradually evolve out of the existing Web. The Semantic Web is propagated by the World Wide Web Consortium (W3C), an international standardization body for the Web. The driving force of the Semantic Web initiative is Tim Berners-Lee, the very person who in- vented the WWW in the late 1980s. He expects from this initiative the re- alization of his original vision of the Web, a vision where the meaning of information played a far more important role than it does in today’s Web. The development of the Semantic Web has a lot of industry momentum, and governments are investing heavily. The U.S. government has established the DARPA Agent Markup Language (DAML) Project, and the Semantic Web is among the key action lines of the European Union’s Sixth Framework Programme. 1.2 From Today’s Web to the Semantic Web: Examples 1.2.1 Knowledge Management Knowledge management concerns itself with acquiring, accessing, and maintaining knowledge within an organization. It has emerged as a key activity of large businesses because they view internal knowledge as an in- tellectual asset from which they can draw greater productivity, create new value, and increase their competitiveness. Knowledge management is par- ticularly important for international organizations with geographically dis- persed departments. 1. In the literature the termmachine-understandable is used quite often. We believe it is the wrong word because it gives the wrong impression. It is not necessary for intelligent agents to under- stand information; it is sufficient for them to process information effectively, which sometimes causes people to think the machine really understands.4 1 The Semantic Web Vision Most information is currently available in a weakly structured form, for example, text, audio, and video. From the knowledge management perspec- tive, the current technology suffers from limitations in the following areas: • Searching information. Companies usually depend on keyword-based search engines, the limitations of which we have outlined. • Extracting information. Human time and effort are required to browse the retrieved documents for relevant information. Current intelligent agents are unable to carry out this task in a satisfactory fashion. • Maintaining information. Currently there are problems, such as inconsis- tencies in terminology and failure to remove outdated information. • Uncovering information. New knowledge implicitly existing in corpo- rate databases is extracted using data mining. However, this task is still difficult for distributed, weakly structured collections of documents. • Viewing information. Often it is desirable to restrict access to certain in- formation to certain groups of employees. “Views,” which hide certain information, are known from the area of databases but are hard to realize over an intranet (or the Web). The aim of the Semantic Web is to allow much more advanced knowledge management systems: • Knowledge will be organized in conceptual spaces according to its mean- ing. • Automated tools will support maintenance by checking for inconsisten- cies and extracting new knowledge. • Keyword-based search will be replaced by query answering: requested knowledge will be retrieved, extracted, and presented in a human- friendly way. • Query answering over several documents will be supported. • Defining who may view certain parts of information (even parts of docu- ments) will be possible.1.2 From Today’s Web to the Semantic Web: Examples 5 1.2.2 Business-to-Consumer Electronic Commerce Business-to-consumer (B2C) electronic commerce is the predominant com- mercial experience of Web users. A typical scenario involves a user’s visiting one or several online shops, browsing their offers, selecting and ordering products. Ideally, a user would collect information about prices, terms, and condi- tions (such as availability) of all, or at least all major, online shops and then proceed to select the best offer. But manual browsing is too time-consuming to be conducted on this scale. Typically a user will visit one or a very few online stores before making a decision. To alleviate this situation, tools for shopping around on the Web are avail- able in the form of shopbots, software agents that visit several shops, extract product and price information, and compile a market overview. Their func- tionality is provided by wrappers, programs that extract information from an online store. One wrapper per store must be developed. This approach suffers from several drawbacks. The information is extracted from the online store site through keyword search and other means of textual analysis. This process makes use of as- sumptions about the proximity of certain pieces of information (for example, the price is indicated by the wordprice followed by the symbol followed by a positive number). This heuristic approach is error-prone; it is not always guaranteed to work. Because of these difficulties only limited information is extracted. For example, shipping expenses, delivery times, restrictions on the destination country, level of security, and privacy policies are typically not extracted. But all these factors may be significant for the user’s deci- sion making. In addition, programming wrappers is time-consuming, and changes in the online store outfit require costly reprogramming. The Semantic Web will allow the development of software agents that can interpret the product information and the terms of service: • Pricing and product information will be extracted correctly, and delivery and privacy policies will be interpreted and compared to the user require- ments. • Additional information about the reputation of online shops will be re- trieved from other sources, for example, independent rating agencies or consumer bodies. • The low-level programming of wrappers will become obsolete.6 1 The Semantic Web Vision • More sophisticated shopping agents will be able to conduct automated negotiations, on the buyer’s behalf, with shop agents. 1.2.3 Business-to-Business Electronic Commerce Most users associate the commercial part of the Web with B2C e-commerce, but the greatest economic promise of all online technologies lies in the area of business-to-business (B2B) e-commerce. Traditionally businesses have exchanged their data using the Electronic Data Interchange (EDI) approach. However this technology is complicated and understood only by experts. It is difficult to program and maintain, and it is error-prone. Each B2B communication requires separate programming, so such communications are costly. Finally, EDI is an isolated technology. The interchanged data cannot be easily integrated with other business appli- cations. The Internet appears to be an ideal infrastructure for business-to-business communication. Businesses have increasingly been looking at Internet-based solutions, and new business models such as B2B portals have emerged. Still, B2B e-commerce is hampered by the lack of standards. HTML (hypertext markup language) is too weak to support the outlined activities effectively: it provides neither the structure nor the semantics of information. The new standard of XML is a big improvement but can still support communications only in cases where there is a priori agreement on the vocabulary to be used and on its meaning. The realization of the Semantic Web will allow businesses to enter partner- ships without much overhead. Differences in terminology will be resolved using standard abstract domain models, and data will be interchanged using translation services. Auctioning, negotiations, and drafting contracts will be carried out automatically (or semiautomatically) by software agents. 1.2.4 Wikis Currently, the use of the WWW is expanded by tools that enable the active participation of Web users. Some consider this development revolutionary and have given it a name: Web 2.0. Part of this direction involves wikis, collections of Web pages that allow users to add content (usually structured text and hypertext links) via a browser interface. Wiki systems allow for collaborative knowledge creation because they give users almost complete freedom to add and change infor-1.2 From Today’s Web to the Semantic Web: Examples 7 mation without ownership of content, access restrictions, or rigid workflows. Wiki systems are used for a variety of purposes, including the following: • Development of bodies of knowledge in a community effort, with contri- butions from a wide range of users. The best-known result is the general- purpose Wikipedia. • Knowledge management of an activity or a project. Examples are brain- storming and exchanging ideas, coordinating activities, and exchanging records of meetings. While it is still early to talk about drawbacks and limitations of this technol- ogy, wiki systems can definitely benefit from the use of semantic technolo- gies. The main idea is to make the inherent structure of a wiki, given by the linking between pages, accessible to machines beyond mere navigation. This can be done by enriching structured text and untyped hyperlinks with semantic annotations referring to an underlying model of the knowledge captured by the wiki. For example, a hyperlink from Knossos to Heraklion could be annotated with information is located in. This information could then be used for context-specific presentation of pages, advanced querying, and consistency verification. 1.2.5 Personal Agents: A Future Scenario The following scenario illustrates functionalities that can be implemented based on Semantic Web technologies. Michael had just had a minor car accident and was feeling some neck pain. His primary care physician suggested a series of physical therapy sessions. Michael asked his Semantic Web agent to work out some possibilities. The agent retrieved details of the recommended therapy from the doctor’s agent and looked up the list of therapists maintained by Michael’s health insurancecompany. Theagentcheckedforthoselocatedwithinaradiusof10 km from Michael’s office or home, and looked up their reputation according to trusted rating services. Then it tried to match available appointment times with Michael’s calendar. In a few minutes the agent returned two proposals. Unfortunately, Michael was not happy with either of them. One therapist had offered appointments in two weeks’ time; for the other Michael would have to drive during rush hour. Therefore, Michael decided to set stricter time constraints and asked the agent to try again.8 1 The Semantic Web Vision A few minutes later the agent came back with an alternative: a therapist withagoodreputationwhohadavailableappointmentsstartingintwodays. However, there were a few minor problems. Some of Michael’s less impor- tant work appointments would have to be rescheduled. The agent offered to make arrangements if this solution were adopted. Also, the therapist was not listed on the insurer’s site because he charged more than the insurer’s maximum coverage. The agent had found his name from an independent list of therapists and had already checked that Michael was entitled to the insurer’s maximum coverage, according to the insurer’s policy. It had also negotiated with the therapist’s agent a special discount. The therapist had only recently decided to charge more than average and was keen to find new patients. Michael was happy with the recommendation because he would have to pay only a few dollars extra. However, because he had installed the Semantic Web agent a few days ago, he asked it for explanations of some of its asser- tions: how was the therapist’s reputation established, why was it necessary for Michael to reschedule some of his work appointments, how was the price negotiation conducted? The agent provided appropriate information. Michael was satisfied. His new Semantic Web agent was going to make his busy life easier. He asked the agent to take all necessary steps to finalize the task. 1.3 Semantic Web Technologies The scenarios outlined in section 1.2 are not science fiction; they do not re- quire revolutionary scientific progress to be achieved. We can reasonably claim that the challenge is an engineering and technology adoption rather than a scientific one: partial solutions to all important parts of the problem exist. At present, the greatest needs are in the areas of integration, standard- ization, development of tools, and adoption by users. But, of course, further technological progress will lead to a more advanced Semantic Web than can, in principle, be achieved today. In the following sections we outline a few technologies that are necessary for achieving the functionalities previously outlined. 1.3.1 Explicit Metadata Currently, Webcontentisformattedforhumanreadersratherthanprograms. HTML is the predominant language in which Web pages are written (directly1.3 Semantic Web Technologies 9 or using tools). A portion of a typical Web page of a physical therapist might look like this: h1Agilitas Physiotherapy Centre/h1 Welcome to the Agilitas Physiotherapy Centre home page. Do you feel pain? Have you had an injury? Let our staff Lisa Davenport, Kelly Townsend (our lovely secretary) and Steve Matthews take care of your body and soul. h2Consultation hours/h2 Mon 11am - 7pmbr Tue 11am - 7pmbr Wed 3pm - 7pmbr Thu 11am - 7pmbr Fri 11am - 3pmp But note that we do not offer consultation during the weeks of the a href=". . ."State Of Origin/a games. For people the information is presented in a satisfactory way, but machines will have their problems. Keyword-based searches will identify the words physiotherapy and consultation hours. And an intelligent agent might even be able to identify the personnel of the center. But it will have trouble distin- guishing the therapists from the secretary, and even more trouble finding the exact consultation hours (for which it would have to follow the link to the State Of Origin games to find when they take place). The Semantic Web approach to solving these problems is not the devel- opment of superintelligent agents. Instead it proposes to attack the problem from theWeb page side. IfHTML is replacedby more appropriate languages, then the Web pages could carry their content on their sleeve. In addition to containing formatting information aimed at producing a document for human readers, they could contain information about their content. In our example, there might be information such as company treatmentOfferedPhysiotherapy/treatmentOffered companyNameAgilitas Physiotherapy Centre/companyName staff therapistLisa Davenport/therapist therapistSteve Matthews/therapist secretaryKelly Townsend/secretary10 1 The Semantic Web Vision /staff /company This representation is far more easily processable by machines. The term metadata refers to such information: data about data. Metadata capture part of the meaning of data, thus the term semantic in Semantic Web. In our example scenarios in section 1.2 there seemed to be no barriers in the access to information in Web pages: therapy details, calendars and appoint- ments, prices and product descriptions, it seemed like all this information could be directly retrieved from existing Web content. But, as we explained, this will not happen using text-based manipulation of information but rather by taking advantage of machine-processable metadata. As with the current development of Web pages, users will not have to be computer science experts to develop Web pages; they will be able to use tools for this purpose. Still, the question remains why users should care, why they should abandon HTML for Semantic Web languages. Perhaps we can give an optimistic answer if we compare the situation today to the beginnings of the Web. The first users decided to adopt HTML because it had been adopted as a standard and they were expecting benefits from being early adopters. Others followed when more and better Web tools became available. And soon HTML was a universally accepted standard. Similarly, we are currently observing the early adoption of XML. While not sufficient in itself for the realization of the Semantic Web vision, XML is an important first step. Early users, perhaps some large organizations interested in knowledge management and B2B e-commerce, will adopt XML and RDF, the current Semantic Web-related W3C standards. And the momentum will lead to more and more tool vendors’ and end users’ adopting the technology. This will be a decisive step in the Semantic Web venture, but it is also a challenge. As we mentioned, the greatest current challenge is not scientific but rather one of technology adoption. 1.3.2 Ontologies The term ontology originates from philosophy. In that context, it is used as the name of a subfield of philosophy, namely, the study of the nature of ex- istence (the literal translation of the Greek word Oντoλoγiα), the branch of metaphysics concerned with identifying, in the most general terms, the kinds of things that actually exist, and how to describe them. For example, the ob- servation that the world is made up of specific objects that can be grouped1.3 Semantic Web Technologies 11 university people staff students technical academic administration undergraduate postgraduate support staff staff staff regular research visiting faculty staff staff staff Figure 1.1 A hierarchy into abstract classes based on shared properties is a typical ontological com- mitment. However, in more recent years, ontology has become one of the many words hijacked by computer science and given a specific technical meaning that is rather different from the original one. Instead of “ontology” we now speak of “an ontology.” For our purposes, we will use T. R. Gruber’s defini- tion, later refined by R. Studer: Anontologyisanexplicitandformalspecification of a conceptualization. In general, an ontology describes formally a domain of discourse. Typi- cally, an ontology consists of a finite list of terms and the relationships be- tween these terms. The terms denote important concepts (classes of objects) of the domain. For example, in a university setting, staff members, students, courses, lecture theaters, and disciplines are some important concepts. The relationships typically include hierarchies of classes. A hierarchy spec-  ifies a class C to be a subclass of another class C if every object in C is also  included in C . For example, all faculty are staff members. Figure 1.1 shows a hierarchy for the university domain. Apart from subclass relationships, ontologies may include information such as12 1 The Semantic Web Vision • properties (X teaches Y), • value restrictions (only faculty members may teach courses), • disjointness statements (faculty and general staff are disjoint), • specifications of logical relationships between objects (every department must include at least ten faculty members). In the context of the Web, ontologies provide a shared understanding of a do- main. Such a shared understanding is necessary to overcome differences in terminology. One application’s zip code may be the same as another applica- tion’s area code. Another problem is that two applications may use the same term with different meanings. In university A, a course may refer to a degree (like computer science), while in university B it may mean a single subject (CS 101). Such differences can be overcome by mapping the particular ter- minology to a shared ontology or by defining direct mappings between the ontologies. In either case, it is easy to see that ontologies support semantic interoperability . Ontologies are useful for the organization and navigation of Web sites. Many Web sites today expose on the left-hand side of the page the top levels of a concept hierarchy of terms. The user may click on one of them to expand the subcategories. Also, ontologies are useful for improving the accuracy of Web searches. The search engines can look for pages that refer to a precise concept in an on- tology instead of collecting all pages in which certain, generally ambiguous, keywords occur. In this way, differences in terminology between Web pages and the queries can be overcome. In addition, Web searches can exploit generalization/specialization infor- mation. If a query fails to find any relevant documents, the search engine may suggest to the user a more general query. It is even conceivable for the engine to run such queries proactively to reduce the reaction time in case the user adopts a suggestion. Or if too many answers are retrieved, the search engine may suggest to the user some specializations. In Artificial Intelligence (AI) there is a long tradition of developing and us- ing ontology languages. It is a foundation Semantic Web research can build upon. At present, the most important ontology languages for the Web are the following: • RDF is a data model for objects (“resources”) and relations between them;1.3 Semantic Web Technologies 13 it provides a simple semantics for this data model; and these data models can be represented in an XML syntax. • RDF Schema is a vocabulary description language for describing prop- erties and classes of RDF resources, with a semantics for generalization hierarchies of such properties and classes. • OWL is a richer vocabulary description language for describing proper- ties and classes, such as relations between classes (e.g., disjointness), car- dinality (e.g., “exactly one”), equality, richer typing of properties, charac- teristics of properties (e.g., symmetry), and enumerated classes. 1.3.3 Logic Logic is the discipline that studies the principles of reasoning; it goes back to Aristotle. In general, logic offers, first, formal languages for expressing know- ledge. Second, logic provides us with well-understood formal semantics:in most logics, the meaning of sentences is defined without the need to oper- ationalize the knowledge. Often we speak of declarative knowledge: we describe what holds without caring about how it can be deduced. And third, automated reasoners can deduce (infer) conclusions from the given knowledge, thus making implicit knowledge explicit. Such reason- ers have been studied extensively in AI. Here is an example of an inference. Suppose we know that all professors are faculty members, that all faculty members are staff members, and that Michael is a professor. In predicate logic the information is expressed as follows: prof(X)→ faculty(X) faculty(X)→ staff(X) prof(michael) Then we can deduce the following: faculty(michael) staff(michael) prof(X)→ staff(X) Note that this example involves knowledge typically found in ontologies. Thus logic can be used to uncover ontological knowledge that is implicitly14 1 The Semantic Web Vision given. By doing so, it can also help uncover unexpected relationships and inconsistencies. But logic is more general than ontologies. It can also be used by intelligent agents for making decisions and selecting courses of action. For example, a shop agent may decide to grant a discount to a customer based on the rule loyalCustomer(X)→ discount(X,5%) where the loyalty of customers is determined from data stored in the cor- porate database. Generally there is a trade-off between expressive power and computational efficiency. The more expressive a logic is, the more com- putationally expensive it becomes to draw conclusions. And drawing cer- tain conclusions may become impossible if noncomputability barriers are encountered. Luckily, most knowledge relevant to the Semantic Web seems to be of a relatively restricted form. For example, our previous examples in- volved rules of the form, “If conditions, then conclusion,” where conditions and conclusion are simple statements, and only finitely many objects needed to be considered. This subset of logic, called Horn logic, is tractable and supported by efficient reasoning tools. An important advantage of logic is that it can provide explanations for conclusions: the series of inference steps can be retraced. Moreover AI re- searchers have developed ways of presenting an explanation in a human- friendly way, by organizing a proof as a natural deduction and by grouping a number of low-level inference steps into metasteps that a person will typ- ically consider a single proof step. Ultimately an explanation will trace an answer back to a given set of facts and the inference rules used. Explanations are important for the Semantic Web because they increase users’ confidence in Semantic Web agents (see the physiotherapy example in section 1.2.5). Tim Berners-Lee speaks of an “Oh yeah?” button that would ask for an explanation. Explanations will also be necessary for activities between agents. While some agents will be able to draw logical conclusions, others will only have the capability to validate proofs, that is, to check whether a claim made by another agent is substantiated. Here is a simple example. Suppose agent 1, representing an online shop, sends a message “You owe me 80” (not in natural language, of course, but in a formal, machine-processable language) to agent 2, representing a person. Then agent 2 might ask for an explanation, and agent 1 might respond with a sequence of the form Web log of a purchase over 801.3 Semantic Web Technologies 15 Proof of delivery (for example, tracking number of UPS) Rule from the shop’s terms and conditions: purchase(X,Item)∧ price(Item,Price)∧ delivered(Item,X) → owes(X,Price) Thus facts will typically be traced to some Web addresses (the trust of which will be verifiable by agents), and the rules may be a part of a shared com- merce ontology or the policy of the online shop. For logic to be useful on the Web it must be usable in conjunction with other data, and it must be machine-processable as well. Therefore, there is ongoing work on representing logical knowledge and proofs in Web lan- guages. Initial approaches work at the level of XML, but in the future rules and proofs will need to be represented at the level of RDF and ontology lan- guages, such as DAML+OIL and OWL. 1.3.4 Agents Agents are pieces of software that work autonomously and proactively. Con- ceptually they evolved out of the concepts of object-oriented programming and component-based software development. A personal agent on the Semantic Web (figure 1.2) will receive some tasks and preferences from the person, seek information from Web sources, com- municate with other agents, compare information about user requirements and preferences, select certain choices, and give answers to the user. An example of such an agent is Michael’s private agent in the physiotherapy example of section 1.2.5. It should be noted that agents will not replace human users on the Seman- tic Web, nor will they necessarily make decisions. In many, if not most, cases their role will be to collect and organize information, and present choices for the users to select from, as Michael’s personal agent did in offering a selec- tion between the two best solutions it could find, or as a travel agent does that looks for travel offers to fit a person’s given preferences. Semantic Web agents will make use of all the technologies we have out- lined: • Metadata will be used to identify and extract information from Web sources. • Ontologies will be used to assist in Web searches, to interpret retrieved information, and to communicate with other agents.16 1 The Semantic Web Vision Today In the future User User Personal agent Present in Search Web browser engine Intelligent infrastructure services www docs www docs Figure 1.2 Intelligent personal agents • Logic will be used for processing retrieved information and for drawing conclusions. Further technologies will also be needed, such as agent communication lan- guages. Also, for advanced applications it will be useful to represent for- mally the beliefs, desires, and intentions of agents, and to create and main- tain user models. However, these points are somewhat orthogonal to the Semantic Web technologies. Therefore they are not discussed further in this book. 1.3.5 The Semantic Web versus Artificial Intelligence As we have said, most of the technologies needed for the realization of the Semantic Web build upon work in the area of artificial intelligence. Given that AI has a long history, not always commercially successful, one might worry that, in the worst case, the Semantic Web will repeat AI’s errors: big promises that raise too high expectations, which turn out not to be fulfilled (at least not in the promised time frame).1.4 A Layered Approach 17 This worry is unjustified. The realization of the Semantic Web vision does not rely on human-level intelligence; in fact, as we have tried to explain, the challenges are approached in a different way. The full problem of AI is a deep scientific one, perhaps comparable to the central problems of physics (explain the physical world) or biology (explain the living world). So seen, the difficulties in achieving human-level Artificial Intelligence within ten or twenty years, as promised at some points in the past, should not have come as a surprise. But on the Semantic Web partial solutions will work. Even if an intelligent agent is not able to come to all conclusions that a human user might draw, the agent will still contribute to a Web much superior to the current Web. This brings us to another difference. If the ultimate goal of AI is to build an intel- ligent agent exhibiting human-level intelligence (and higher), the goal of the Semantic Web is to assist human users in their day-to-day online activities. It is clear that the Semantic Web will make extensive use of current AI tech- nology and that advances in that technology will lead to a better Semantic Web. But there is no need to wait until AI reaches a higher level of achieve- ment; current AI technology is already sufficient to go a long way toward realizing the Semantic Web vision. 1.4 A Layered Approach The development of the Semantic Web proceeds in steps, each step building a layer on top of another. The pragmatic justification for this approach is that it is easier to achieve consensus on small steps, whereas it is much harder to get everyone on board if too much is attempted. Usually there are sev- eral research groups moving in different directions; this competition of ideas is a major driving force for scientific progress. However, from an engineer- ing perspective there is a need to standardize. So, if most researchers agree on certain issues and disagree on others, it makes sense to fix the points of agreement. This way, even if the more ambitious research efforts should fail, there will be at least partial positive outcomes. Once a standard has been established, many more groups and companies will adopt it, instead of waiting to see which of the alternative research lines will be successful in the end. The nature of the Semantic Web is such that companies and single users must build tools, add content, and use that con- tent. We cannot wait until the full Semantic Web vision materializes — it may take another ten years for it to be realized to its full extent (as envisioned18 1 The Semantic Web Vision today, of course). In building one layer of the Semantic Web on top of another, two principles should be followed: • Downward compatibility. Agents fully aware of a layer should also be able to interpret and use information written at lower levels. For exam- ple, agents aware of the semantics of OWL can take full advantage of information written in RDF and RDF Schema. • Upward partial understanding. The design should be such that agents fully aware of a layer should be able to take at least partial advantage of information at higher levels. For example, an agent aware only of the RDF and RDF Schema semantics might interpret knowledge written in OWL partly, by disregarding those elements that go beyond RDF and RDF Schema. Of course, there is no requirement for all tools to provide this functionality; the point is that this option should be enabled. While these ideas are theoretically appealing and have been used as guiding principles for the development of the Semantic Web, their realization in prac- tice turned out to be difficult, and some compromises needed to be made. This will become clear in chapter 4, where the layering of RDF and OWL is discussed. Figure 1.3 shows the “layer cake” of the Semantic Web (due to Tim Berners- Lee), which describes the main layers of the Semantic Web design and vision. At the bottom we find XML, a language that lets one write structured Web documents with a user-defined vocabulary. XML is particularly suitable for sending documents across the Web. RDF is a basic data model, like the entity-relationship model, for writing simple statements about Web objects (resources). The RDF data model does not rely on XML, but RDF has an XML-based syntax. Therefore, in figure 1.3, it is located on top of the XML layer. RDF Schema provides modeling primitives for organizing Web objects into hierarchies. Key primitives are classes and properties, subclass and subprop- erty relationships, and domain and range restrictions. RDF Schema is based on RDF. RDF Schema can be viewed as a primitive language for writing ontolo- gies. But there is a need for more powerful ontology languages that expand RDF Schema and allow the representations of more complex relationships between Web objects. The Logic layer is used to enhance the ontology lan-1.4 A Layered Approach 19 Figure 1.3 A layered approach to the Semantic Web guage further and to allow the writing of application-specific declarative knowledge. The Proof layer involves the actual deductive process as well as the repre- sentation of proofs in Web languages (from lower levels) and proof valida- tion. Finally, the Trust layer will emerge through the use of digital signatures and other kinds of knowledge, based on recommendations by trusted agents or on rating and certification agencies and consumer bodies. Sometimes “Web of Trust” is used to indicate that trust will be organized in the same dis- tributed and chaotic way as the WWW itself. Being located at the top of the pyramid, trust is a high-level and crucial concept: the Web will only achieve its full potential when users have trust in its operations (security) and in the quality of information provided. This classical layer stack is currently being debated. Figure 1.4 shows an alternative layer stack that takes recent developments into account. The main differences, compared to the stack in figure 1.3, are the following:

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