1. Chapter 1A Semantic Web Primer, 2nd Edition 1-1 ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ, ΑΠΘ ΠΜΣ «Πληροφορική & Τηλεπικοινωνίες» Κατεύθυνση Πληροφοριακών Συστημάτων ΠΜΣ «Διαδίκτυο και Παγκόσμιος Ιστός» 1ο Εξάμηνο Σημασιολογικός Ιστός lpis.csd.auth.gr/mtpx/sw ή tinyurl.com/SemanticWebAUTH Διδάσκων: Ν. Βασιλειάδης Αναπλ. Καθ. Τμ. Πληροφορικής ΑΠΘ Μάθημα: 1

2. The Semantic Web An extension of the Web through standards – The standards promote common data formats and exchange protocols on the Web – Most important: RDF SW provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. “… a web of data that can be processed by machines”. – Tim Berners-Lee Applications in industry, biology and human sciences have already proven the validity of the original concept Chapter 1A Semantic Web Primer, 2nd Edition 1-2

3. Web of (Linked) Data Current WebSemantic Web Web of DocumentsWeb of Data Hypertext Documents (HTML)Semi-structured Data representation in graphs (RDF) Interconnected documents through URL links Linked data through URIs Human ConsumptionMachine Consumption Use search engines and browsing to explore Use search engines, web (RDF) databases to query and URI links between datasets to explore Chapter 1A Semantic Web Primer, 2nd Edition 1-3 Don't just link the documents, link the things

4. Course Outline XML (the content/data carrier) – DTD, XML Schema, Xpath, XSLT RDF (the content/data model) – RDF Schema, SPARQL, Linked Open Data OWL (the content/data vocabulary / ontologies) SWRL (the content/data processor / rule language) Chapter 1A Semantic Web Primer, 2nd Edition 1-4

5. Assessment 4 projects (55%) – (10%) Modeling in XML (DTD, XML Schema)  XML editor – (15%) “Programming” in XSLT (XML presentation in HTML)  XML editor – (15%) Modeling in RDF / RDF Schema / Querying in SPARQL  Protégé – (15%) Modeling in OWL / Reasoning in SWRL  Protégé Exams (50%) – (alternatively) Full programming project  Java? 1-5

6. Course Book Grigoris Antoniou and Frank van Harmelen, "A Semantic Web Primer", 2nd Edition, MIT Press, 2008, ISBN 978- 0-262-01242-3. – Εισαγωγή στο Σημασιολογικό Ιστό (μεταφρασμένο), Εκδόσεις Κλειδάριθμος, (2009). Grigoris Antoniou, Paul Groth, Frank van Harmelen and Rinke Hoekstra, “A Semantic Web Primer”, Third Edition, MIT Press, 2012, ISBN: 9780262018289 Chapter 1A Semantic Web Primer, 2nd Edition 1-6

7. Chapter 1A Semantic Web Primer, 2nd Edition 1-7 Chapter 1 The Semantic Web Vision Grigoris Antoniou Frank van Harmelen

8. Chapter 1A Semantic Web Primer, 2nd Edition 1-8 Lecture Outline 1. Today’s Web and Semantic Web Vision 2. Semantic Web Technologies 3. A Layered Approach 4. The Semantic Web Today

9. Chapter 1A Semantic Web Primer, 2nd Edition 1-9 Today’s Web 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

10. Chapter 1A Semantic Web Primer, 2nd Edition 1-10 Keyword-Based Search Engines Current Web activities are not particularly well supported by software tools – Except for keyword-based search engines (e.g. Google, Bing, Yahoo) The Web would not have been the huge success it was, were it not for search engines

11. Chapter 1A Semantic Web Primer, 2nd Edition 1-11 Problems of Keyword-Based Search Engines High recall, low precision. Low or no recall Results are highly sensitive to vocabulary Results are single Web pages Human involvement is necessary to interpret and combine results Results of Web searches are not readily accessible by other software tools

12. Chapter 1A Semantic Web Primer, 2nd Edition 1-12 The Key Problem of Today’s Web The meaning of Web content is not machine- accessible: lack of semantics It is simply difficult to distinguish the meaning between these two sentences: I am a professor of computer science. I am a professor of computer science, you may think. Well,...

13. Chapter 1A Semantic Web Primer, 2nd Edition 1-13 The Text Processing Approach Use the content as it is represented today Develop increasingly sophisticated techniques based on ΑΙ and computational linguistics. Ηas been followed for some time now – Despite some advances the task is too ambitious.

14. Chapter 1A Semantic Web Primer, 2nd Edition 1-14 The Semantic Web Approach Represent Web content in a form that is more easily machine-processable. Use intelligent techniques to take advantage of these representations. The Semantic Web will gradually evolve out of the existing Web – It is not a competition to the current WWW

15. Chapter 1A Semantic Web Primer, 2nd Edition 1-15 Semantic Web Organization Support SW is propagated by the WWW Consortium (W3C) – International standardization body for the Web. – The driving force is Tim Berners-Lee, the person who invented the WWW in the late 80s. – In his original vision of the Web the meaning of information played a far more important role than it does today.

16. Chapter 1A Semantic Web Primer, 2nd Edition 1-16 The Semantic Web Vision B2C Electronic Commerce A typical scenario: user visits one or several online shops, browses their offers, selects and orders products. Ideally humans would visit all, or all major online stores; but too time consuming Shopbots are a useful tool

17. Chapter 1A Semantic Web Primer, 2nd Edition 1-17 Limitations of Shopbots They rely on wrappers: extensive programming required Wrappers need to be reprogrammed when an online store changes its outfit Wrappers extract information based on textual analysis – Error-prone – Limited information extracted

18. Chapter 1A Semantic Web Primer, 2nd Edition 1-18 Semantic Web Enabled B2C Electronic Commerce Software agents that can interpret the product information and the terms of service. – Pricing and product information, delivery and privacy policies will be interpreted and compared to the user requirements. Information about the reputation of shops Sophisticated shopping agents will be able to conduct automated negotiations

19. Chapter 1A Semantic Web Primer, 2nd Edition 1-19 The Semantic Web Vision B2B Electronic Commerce Greatest economic promise Currently relies mostly on EDI – Isolated technology, understood only by experts – Difficult to program and maintain, error-prone – Each B2B communication requires separate programming Web appears to be perfect infrastructure – But B2B not well supported by Web standards

20. Chapter 1A Semantic Web Primer, 2nd Edition 1-20 Semantic Web Enabled B2B Electronic Commerce Businesses enter partnerships without much overhead Differences in terminology will be resolved using standard abstract domain models Data will be interchanged using translation services. Auctioning, negotiations, and drafting contracts will be carried out automatically (or semi-automatically) by software agents

21. Chapter 1A Semantic Web Primer, 2nd Edition 1-21 The Semantic Web Vision Personal Agents 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. – He asked his Semantic Web agent to work out some possibilities.

22. Chapter 1A Semantic Web Primer, 2nd Edition 1-22 Personal Agents in the Semantic Web The agent: – retrieved details of the recommended therapy from the doctor’s agent – looked up the list of therapists maintained by Michael’s health insurance company – checked for those therapists located within 10 km from Michael’s office or home

23. Chapter 1A Semantic Web Primer, 2nd Edition 1-23 Personal Agents in the Semantic Web The agent (continued): – looked up their reputation according to trusted rating services – tried to match available appointment times with Michael’s calendar – returned two proposals

24. Chapter 1A Semantic Web Primer, 2nd Edition 1-24 Personal Agents in the Semantic Web Michael was not happy with either of the 2 proposals and decided to set stricter time constraints and asked the agent to try again. – One therapist had offered appointments in two weeks’ time – For the other one Michael would have to drive during rush hour.

25. Chapter 1A Semantic Web Primer, 2nd Edition 1-25 Personal Agents in the Semantic Web The agent came back with an alternative solution: – A therapist with an excellent reputation who had available appointments starting in 2 days

26. Chapter 1A Semantic Web Primer, 2nd Edition 1-26 Personal Agents in the Semantic Web However, there were still a few minor problems with the alternative solution – Some of Michael’s less important work appointments would have to be rescheduled. – The agent offered to make arrangements if this solution were adopted. – The therapist was not listed on the insurer’s site because he charged more than the insurer’s maximum coverage.

27. Chapter 1A Semantic Web Primer, 2nd Edition 1-27 Personal Agents in the Semantic Web The agent had: – found his name from an independent list of therapists – checked that Michael was entitled to the insurer’s maximum coverage, according to the insurer’s policy – 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.

28. Chapter 1A Semantic Web Primer, 2nd Edition 1-28 Personal Agents in the Semantic Web 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 assertions: – 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

29. Chapter 1A Semantic Web Primer, 2nd Edition 1-29 Personal Agents in the Semantic Web 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.

30. Chapter 1A Semantic Web Primer, 2nd Edition 1-30 Lecture Outline 1. Today’s Web and the Semantic Web Vision 2. Semantic Web Technologies 3. A Layered Approach 4. The Semantic Web Today

31. Chapter 1A Semantic Web Primer, 2nd Edition 1-31 Semantic Web Technologies Explicit Metadata Ontologies Logic and Inference Agents

32. Chapter 1A Semantic Web Primer, 2nd Edition 1-32 On HTML Web content is currently formatted for human readers rather than programs HTML is the predominant language in which Web pages are written (directly or using tools) Vocabulary of HTML describes presentation

33. Chapter 1A Semantic Web Primer, 2nd Edition 1-33 An HTML Example Agilitas Physiotherapy Centre Welcome to the home page of the Agilitas Physiotherapy Centre. 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. Consultation hours Mon 11am - 7pm Tue 11am - 7pm Wed 3pm - 7pm Thu 11am - 7pm Fri 11am - 3pm But note that we do not offer consultation during the weeks of the State Of Origin games.

34. Chapter 1A Semantic Web Primer, 2nd Edition 1-34 Problems with HTML Humans have no problem with this Machines (software agents) do: – How distinguish therapists from the secretary – How determine exact consultation hours – They would have to follow the link to the State Of Origin games to find when they take place.

35. Chapter 1A Semantic Web Primer, 2nd Edition 1-35 A Better Representation Physiotherapy Agilitas Physiotherapy Centre Lisa Davenport Steve Matthews Kelly Townsend

36. Chapter 1A Semantic Web Primer, 2nd Edition 1-36 Explicit Metadata This representation is far more easily processable by machines Metadata: data about data – Metadata capture part of the meaning of data Semantic Web does not rely on text-based manipulation, but rather on machine- processable metadata

37. But … What do the meta-data mean? Which words (tags) are allowed to be used? … and what do they mean? How different words/tags (used by different people) are related to each other? Answer: – use known vocabularies with pre-defined semantics, or – use vocabularies with semantics that can be linked to known vocabularies A Semantic Web Primer, 2nd Edition 1-37 Chapter 1

38. A Semantic Web Primer, 2nd Edition 1-38 Ontologies The term ontology originates from philosophy The study of the nature of existence Different meaning from computer science An ontology is an explicit and formal specification of a conceptualization

39. Chapter 1A Semantic Web Primer, 2nd Edition 1-39 Typical Components of Ontologies Terms denote important concepts (classes of objects) of the domain – e.g. professors, staff, students, courses, departments Relationships between these terms: typically class hierarchies – a class C to be a subclass of another class C' if every object in C is also included in C' – e.g. all professors are staff members

40. Further Components of Ontologies Properties: – X’s phone number is 98713 Relationships – X teaches Y Value restrictions – only faculty members can teach courses Disjointness statements – faculty and general staff are disjoint Logical relationships between objects – every department must include at least 10 faculty 1-40 A Semantic Web Primer, 2nd EditionChapter 1

41. A Semantic Web Primer, 2nd Edition 1-41 Example of a Class Hierarchy

42. Chapter 1A Semantic Web Primer, 2nd Edition 1-42 The Role of Ontologies on the Web Ontologies provide a shared understanding of a domain: – semantic interoperability – overcome differences in terminology – mappings between ontologies Ontologies are useful for the organization and navigation of Web sites

43. Chapter 1A Semantic Web Primer, 2nd Edition 1-43 The Role of Ontologies in Web Search Ontologies are useful for improving the accuracy of Web searches – search engines can look for pages that refer to a precise concept in an ontology Web searches can exploit generalization/ specialization information – If a query fails to find any relevant documents, the search engine may suggest to the user a more general query. – If too many answers are retrieved, the search engine may suggest to the user some specializations.

44. Chapter 1A Semantic Web Primer, 2nd Edition 1-44 Web Ontology Languages RDF Schema RDF is a data model for objects and relations between them RDF Schema is a vocabulary description language Describes properties and classes of RDF resources Provides semantics for generalization hierarchies of properties and classes

45. Chapter 1A Semantic Web Primer, 2nd Edition 1-45 Web Ontology Languages (2) OWL A richer ontology language relations between classes – e.g., disjointness cardinality – e.g. “exactly one” richer typing of properties characteristics of properties (e.g., symmetry)

46. But… Can ontologies express every piece of knowledge needed in the Semantic Web? They can express static information (e.g. data about the world) They have some reasoning capabilities, but… They cannot combine information from several different individuals in order to come to a complex conclusion about the world They cannot reason about which actions should be performed at each situation 1-46 Chapter 1A Semantic Web Primer, 2nd Edition

47. Chapter 1A Semantic Web Primer, 2nd Edition 1-47 Logic, Rules and Inference Logic is the discipline that studies the principles of reasoning Formal languages for expressing knowledge Well-understood formal semantics – Declarative knowledge: we describe what holds without caring about how it can be deduced Automated reasoners can deduce (infer) conclusions from the given knowledge

48. Chapter 1A Semantic Web Primer, 2nd Edition 1-48 An Inference Example prof(X)  faculty(X) faculty(X)  staff(X) prof(michael) We can deduce the following conclusions: faculty(michael) staff(michael) prof(X)  staff(X)

49. Chapter 1A Semantic Web Primer, 2nd Edition 1-49 Logic rules versus Ontologies The previous example involves knowledge typically found in ontologies – Logic can be used to uncover ontological knowledge that is implicitly given – It can also help uncover unexpected relationships and inconsistencies Logic is more general than ontologies – It can also be used by intelligent agents for making decisions and selecting courses of action

50. Logic rule versus Ontologies It is impossible to express in ontologies the following piece of knowledge: – Persons who study and live in the same city are “home students” Using logic rules it is possible: studies(X,Y), lives(X,Z), loc(Y,U), loc(Z,U)  homeStudent(X) It is impossible to express using logic rules that a person is either a man or a woman – It can be expressed easily using ontologies 6-50 Chapter 1A Semantic Web Primer, 2nd Edition

51. Chapter 1A Semantic Web Primer, 2nd Edition 1-51 Tradeoff between Expressive Power and Computational Complexity The more expressive a logic is, the more computationally expensive it becomes to draw conclusions – Drawing certain conclusions may become impossible if non-computability barriers are encountered. Previous examples involved rules “If conditions, then conclusion,” and only finitely many objects – This subset of logic is tractable and is supported by efficient reasoning tools

52. Chapter 1A Semantic Web Primer, 2nd Edition 1-52 Inference and Explanations Explanations: the series of inference steps can be retraced They increase users’ confidence in Semantic Web agents: – “Oh yeah?” button Activities between agents: create or validate proofs

53. Chapter 1A Semantic Web Primer, 2nd Edition 1-53 Typical Explanation Procedure Facts will typically be traced to some Web addresses – The trust of the Web address will be verifiable by agents Rules may be a part of a shared commerce ontology or the policy of the online shop

54. Chapter 1A Semantic Web Primer, 2nd Edition 1-54 Example of Explanation between Agents Agent 1 (online shop) sends a message “You owe me 80€” to agent 2 (person). – Not in natural language, but in a formal, machine-processable language

55. Chapter 1A Semantic Web Primer, 2nd Edition 1-55 Example of Explanation between Agents Then agent 2 asks for an explanation, and agent 1 responds with a sequence: – Web log of a purchase over $80 (on-line catalog) – Proof of delivery (e.g., tracking number of UPS) – Rule from the shop’s terms and conditions: purchase(X,Item)  price(Item, Price)  delivered(Item,X)  owes(X, Price)

56. Chapter 1A Semantic Web Primer, 2nd Edition 1-56 Software Agents Software agents work autonomously and proactively A personal agent on the Semantic Web will: – receive some tasks and preferences from the person – seek information from Web sources, communicate with other agents – compare information about user requirements and preferences, make certain choices – give answers to the user

57. Chapter 1A Semantic Web Primer, 2nd Edition 1-57 Semantic Web Agent Technologies Metadata – Identify and extract information from Web sources Ontologies – Web searches, interpret retrieved information – Communicate with other agents Logic – Process retrieved information, draw conclusions

58. Chapter 1A Semantic Web Primer, 2nd Edition 1-58 Lecture Outline 1. Today’s Web and the Semantic Web Vision 2. Semantic Web Technologies 3. A Layered Approach 4. The Semantic Web Today

59. Chapter 1A Semantic Web Primer, 2nd Edition 1-59 A Layered Approach The development of the Semantic Web proceeds in steps – Each step building a layer on top of another Principles: Downward compatibility Upward partial understanding

60. Chapter 1A Semantic Web Primer, 2nd Edition 1-60 The Semantic Web Layer Tower

61. Semantic Web Layers (1) Unicode – How characters are represented URI – Addressing docs / objects across the web XML layer – Metadata – Syntactic basis (send docs across the Web) 1-61 Chapter 1A Semantic Web Primer, 2nd Edition

62. Semantic Web Layers (2) RDF layer – Metadata – semantic layer – RDF basic data model for facts – Does not rely on XML, but has XML-based syntax – RDF Schema simple ontology language (metadata vocabulary) Ontology layer – More expressive languages than RDF Schema – Current Web standard: OWL 1-62 Chapter 1A Semantic Web Primer, 2nd Edition

63. Semantic Web Layers (3) Logic layer – enhance ontology languages further – application-specific declarative knowledge Proof layer – Proof generation, exchange, validation Trust layer – Digital signatures – recommendations, rating agencies …. 1-63 Chapter 1A Semantic Web Primer, 2nd Edition

64. Newer SW Architectures 1-64 Chapter 1A Semantic Web Primer, 2nd Edition

65. 1-65 This course! Chapter 1A Semantic Web Primer, 2nd Edition

66. Lecture Outline 1. Today’s Web and the Semantic Web Vision 2. Semantic Web Technologies 3. A Layered Approach 4. The Semantic Web Today 1-66 Chapter 1A Semantic Web Primer, 2nd Edition

67. Could Semantic Web become a reality? Almost every major Web company has now announced their work on a knowledge graph – Google Knowledge Graph – Yahoo! Web of Objects – Walmart Lab Social Genome – Microsoft Satori Graph / Bing Snapshots – Facebook Entity Graph 1-67 Chapter 1A Semantic Web Primer, 2nd Edition

68. DBPedia Community-run project to build a free, open-source knowledge graph since 2006. – Extract structured information from Wikipedia and make it available on the Web. The huge amount of information in Wikipedia can be used in some interesting new ways – Ask sophisticated queries against Wikipedia – Link different data sets on the Web to Wikipedia data Currently exists in 125 different languages and is interlinked with many other databases – e.g. Freebase, GeoNames, New York Times, Wikidata 1-68 Chapter 1A Semantic Web Primer, 2nd Edition

69. Linked Data The knowledge in DBpedia is exposed through a set of technologies called Linked Data. Linked Data has been revolutionizing the way applications interact with the Web. Web 2.0 technologies allowed websites to be re-used from third-parties and to repurpose data on the Web, – Still require that developers create one client per target API With Linked Data, all APIs are interconnected via standard Web protocols and languages. 1-69 Chapter 1A Semantic Web Primer, 2nd Edition

70. Linked Data benefits One can navigate this Web of facts (or Web of Data) with standard Web browsers, automated crawlers or pose complex queries with SQL-like query languages (e.g. SPARQL). – Have you thought of asking the Web about all cities with low criminality, warm weather and open jobs? 1-70 Chapter 1A Semantic Web Primer, 2nd Edition

71. Linked Data benefits New Web of interlinked databases provides useful knowledge that can complement the textual Web Bloggers tag posts or assign them to categories in order to organize and interconnect their blog posts. – This is a very simple way to connect “unstructured” text to a structure (hierarchy of tags). – Identifiers and data provided by DBpedia were greatly involved in creating this knowledge graph. IBM's Watson used DBpedia data to win the Jeopardy challenge 1-71 Chapter 1A Semantic Web Primer, 2nd Edition

72. Schema.org Using Ontologies in practice Schema.org provides a collection of schemas, i.e., html tags, that webmasters can use to markup their pages in ways recognized by major search providers. – Search engines including Bing, Google, Yahoo! and Yandex rely on this markup to improve the display of search results, making it easier for people to find the right web pages. Many sites are generated from structured data, which is often stored in databases. – When this data is formatted into HTML, it becomes very difficult to recover the original structured data. – Many applications, especially search engines, can benefit greatly from direct access to this structured data. 1-72 Chapter 1A Semantic Web Primer, 2nd Edition

73. Schema.org Using Ontologies in practice On-page markup enables search engines to understand the information on web pages and provide richer search results to make it easier for users to find relevant information on the web. – Markup can also enable new tools and applications that make use of the structure. A shared markup vocabulary makes it easier for webmasters to decide on a markup schema and get the maximum benefit for their efforts. – Search engines have come together to provide a shared collection of schemas that webmasters can use. 1-73 Chapter 1A Semantic Web Primer, 2nd Edition

74. Schema.org and Linked Data Actually, information in tagged HTML pages can become linked data (RDF) – Using extraction tools Schema.RDFS.org provides a common ontology that these linked data conform to There exist mappings from schema.org terms to the DBPedia ontology 1-74 Chapter 1A Semantic Web Primer, 2nd Edition

75. Example of incorporating metadata in a web page Chapter 1A Semantic Web Primer, 2nd Edition 1-75

76. Example of metadata in a web page: XHTML+RDFa code for content & metadata Chapter 1A Semantic Web Primer, 2nd Edition 1-76

77. Example of metadata in a web page: RDF/XML code for metadata Chapter 1A Semantic Web Primer, 2nd Edition 1-77

78. Example of metadata in a web page: Metadata in RDF N-triples format Chapter 1A Semantic Web Primer, 2nd Edition 1-78

79. Example of metadata in a web page: RDF metadata in a graph (semantic net) Chapter 1A Semantic Web Primer, 2nd Edition 1-79

80. Some Semantic Web Applications Chapter 1A Semantic Web Primer, 2nd Edition 1-80

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84. Chapter 1A Semantic Web Primer, 2nd Edition 1-84

85. SPLIS A Rule-Based Semantic Personalized Location Information System Chapter 1A Semantic Web Primer, 2nd Edition 1-85

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87. University Rankings 1-87 A Semantic Web Primer, 2nd Edition

88. Find unique (?) DBPedia URL 1-88 Chapter 1A Semantic Web Primer, 2nd Edition

89. Using the DBPedia lookup service 1-89 Chapter 1A Semantic Web Primer, 2nd Edition

90. Using a SPARQL endpoint 1-90 Chapter 1A Semantic Web Primer, 2nd Edition

91. Chapter 1A Semantic Web Primer, 2nd Edition 1-91 SW vs. AI Most SW technologies build upon work in the area of AI – AI has a long history, not always commercially successful. – People worry that the SW will repeat AI’s errors: Big promises that raise too high expectations, which turn out not to be fulfilled. The realization of the SW vision does not rely on human-level intelligence – The challenges are approached in a different way.

92. Chapter 1A Semantic Web Primer, 2nd Edition 1-92 SW vs. AI Τhe ultimate goal of AI is to build an intelligent agent exhibiting human-level intelligence (and higher) – Τhe goal of SW is to assist human users in their day-to-day online activities – 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.

93. Chapter 1A Semantic Web Primer, 2nd Edition 1-93 SW vs. AI Semantic Web will make extensive use of current AI technology – Advances in AI technology will lead to a better Semantic Web. – There is no need to wait until AI reaches a higher level of achievement – Current AI technology is already sufficient to go a long way toward realizing the Semantic Web vision.