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3 modeling projects (10% each)– XML (DTD, XML Schema) XML editor– RDF / RDF Schema Protégé– OWL Protégé
1 “programming” project (20%)– XSLT (XML presentation in HTML) XML editor– (alternatively) linked data & SPARQL related project
1 bonus project in rules (SWRL) (5%) Exams (50%)
– (alternatively) Full programming project Java?1-2
Chapter 1 A Semantic Web Primer, 2nd Edition1-3
Chapter 1The Semantic Web Vision
Grigoris Antoniou
Frank van Harmelen
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Lecture Outline
1. Today’s Web
2. The Semantic Web Impact
3. Semantic Web Technologies
4. A Layered Approach
5. The Semantic Web Today
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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
Typical Web uses– seeking and making use of information, searching for
and getting in touch with other people, reviewing catalogs of online stores, ordering products by filling out forms
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Keyword-Based Search Engines
Current Web activities are not particularly
well supported by software tools– Except for keyword-based search engines (e.g.
Google, AltaVista, Yahoo)
The Web would not have been the huge
success it was, were it not for search
engines
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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
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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, . . .
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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.
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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
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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.
The development of the SW has a lot of industry momentum, and governments are investing heavily.
– The U.S. government has established the DARPA Agent Markup Language (DAML) Project
– EU’s 6th FP has the Semantic Web among the key action lines.
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Lecture Outline
1. Today’s Web
2. The Semantic Web Impact
3. Semantic Web Technologies
4. A Layered Approach
5. The Semantic Web Today
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The Semantic Web Impact – Knowledge Management
Knowledge management concerns itself with acquiring, accessing, and maintaining knowledge within an organization
Key activity of large businesses: internal knowledge as an intellectual asset
It is particularly important for international, geographically dispersed organizations
Most information is currently available in a weakly structured form (e.g. text, audio, video)
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Limitations of Current Knowledge Management Technologies
Searching information – Keyword-based search engines
Extracting information– human involvement necessary for browsing, retrieving,
interpreting, combining
Maintaining information– inconsistencies in terminology, outdated information.
Viewing information – Impossible to define views on Web knowledge
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Semantic Web Enabled Knowledge Management
Knowledge will be organized in conceptual spaces according to its meaning.
Automated tools for maintenance and knowledge discovery
Semantic query answering Query answering over several documents Defining who may view certain parts of information
(even parts of documents) will be possible.
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The Semantic Web Impact – 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
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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
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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
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The Semantic Web Impact – 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
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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
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Personal Agents in the Semantic Web
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.
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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
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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
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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.
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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
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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.
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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.
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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
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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.
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Lecture Outline
1. Today’s Web
2. The Semantic Web Impact
3. Semantic Web Technologies
4. A Layered Approach
5. The Semantic Web Today
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Semantic Web Technologies
Explicit MetadataOntologiesLogic and InferenceAgents
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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
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An HTML Example
<h1>Agilitas Physiotherapy Centre</h1>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 careof your body and soul.<h2>Consultation hours</h2>Mon 11am - 7pm<br>Tue 11am - 7pm<br>Wed 3pm - 7pm<br>Thu 11am - 7pm<br>Fri 11am - 3pm<p>But note that we do not offer consultation during the weeks of the <a href=". . .">State Of Origin</a> games.
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Problems with HTML
Humans have no problem with thisMachines (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.
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
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User Adoption
Users will not have to be computer science experts to develop Web pages with metadata– They will be able to use tools for this
purpose. Why users should abandon HTML for
Semantic Web languages?
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User Adoption
Compare 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.
– Soon HTML was a universally accepted standard.
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User Adoption
Similarly, we are currently observing the early adoption of XML.
– Provides a surface syntax for structured documents – Imposes no semantic constraints on the meaning of these
documents.– XML Schema is a language for restricting the structure of XML
documents.
While not sufficient in itself for the realization of the Semantic Web vision, XML is an important first step.
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User Adoption
Early users (e.g. large organizations interested in knowledge management and B2B e-commerce) will adopt XML and RDF, the current SW standards.
The momentum will lead to 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.
– The greatest current challenge is not scientific but rather one of technology adoption.
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Ontologies
The term ontology originates from philosophy
The study of the nature of existence
Different meaning from computer scienceAn ontology is an explicit and formal
specification of a conceptualization
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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
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
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Example of a Class Hierarchy
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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
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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.
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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
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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)
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Logic 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
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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)
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Logic 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
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Tradeoff between Expressive Power and Computational Complexity
The more expressive a logic is, the more com-putationally expensive it becomes to draw conclusions– Drawing certain conclusions may become impos-
sible 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
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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
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Typical Explanation Procedure
Facts will typically be traced to some Web addresses – The trust of the Web address will be
verifiable by agentsRules may be a part of a shared
commerce ontology or the policy of the online shop
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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
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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
– Proof of delivery (e.g., tracking number of UPS)
Trust layer– Digital signatures– recommendations, rating agencies ….
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Web of Trust
Trust will be organized in the same distributed and chaotic way as the WWW
Trust is a high-level and crucial concept (top of the pyramid)
The Web will only achieve its full potential when users have trust in its operations (security) and in the quality of information provided.
Newer SW Architectures – v2
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Newer SW Architectures – v3
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Newer SW Architectures – v4
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Alternative SW architectures – non TBL [Horrocks et al., 2005]/v1
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Alternative SW architectures – non TBL [Horrocks et al., 2005]/v2
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Alternative SW architectures – non TBL [Gerber et al., 2008]
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Lecture Outline
1. Today’s Web
2. The Semantic Web Impact
3. Semantic Web Technologies
4. A Layered Approach
5. The Semantic Web Today
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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
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DBPedia
DBpedia is a community-run project to build a free, open-source knowledge graph since 2006.
– Effort to extract structured information from Wikipedia and make this information available on the Web.
DBpedia allows you to ask sophisticated queries against Wikipedia, and to link the different data sets on the Web to Wikipedia data.
– It will be easier for the huge amount of information in Wikipedia to be used in some interesting new ways.
DBpedia currently exists in 125 different languages, and is interlinked with many other databases
– e.g. Freebase, GeoNames, New York Times, Wikidata1-75 Chapter 1 A Semantic Web Primer, 2nd Edition
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.
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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?
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Linked Data benefits
New Web of interlinked databases provides useful knowledge that can complement textual Web
E.g. bloggers tag their 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
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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.
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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
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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
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Example of incorporating metadata in a web page
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Example of metadata in a web page: XHTML+RDFa code for content & metadata
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Example of metadata in a web page: RDF/XML code for metadata
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Example of metadata in a web page: Metadata in RDF N-triples format
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Example of metadata in a web page: RDF metadata in a graph (semantic net)
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Some Semantic Web Applications
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SPLISA Rule-Based Semantic Personalized Location Information System
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University Rankings
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Find unique (?) DBPedia URL
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Using the DBPedia lookup service
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Using a SPARQL endpoint
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Book Outline
2. Structured Web Documents in XML
3. Describing Web Resources in RDF
4. Web Ontology Language: OWL
5. Logic and Inference: Rules
6. Applications
7. Ontology Engineering
8. Conclusion and Outlook
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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.
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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.
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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.