Chapter 1A Semantic Web Primer, 2nd Edition 1-1 ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ, ΑΠΘ ΜΕΤΑΠΤΥΧΙΑΚΟ ΠΡΟΓΡΑΜΜΑ ΣΠΟΥΔΩΝ Κατεύθυνση Πληροφοριακών

Chapter 1 A Semantic Web Primer, 2nd Edition1-1

ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ, ΑΠΘ ΜΕΤΑΠΤΥΧΙΑΚΟ ΠΡΟΓΡΑΜΜΑ ΣΠΟΥΔΩΝ

Κατεύθυνση Πληροφοριακών Συστημάτων - 1ο Εξάμηνο

Σημασιολογικός Ιστόςlpis.csd.auth.gr/mtpx/sw

ήtinyurl.com/SemanticWebAUTH

Διδάσκων: Ν. Βασιλειάδης

Αναπλ. Καθ. Τμ. Πληροφορικής ΑΠΘ

Μάθημα: 1

Coursework

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 1The Semantic Web Vision

Grigoris Antoniou

Frank van Harmelen


Lecture Outline

1. Today’s Web

2. The Semantic Web Impact

3. Semantic Web Technologies

4. A Layered Approach

5. The Semantic Web Today


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


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


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


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, . . .


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.


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


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.


Lecture Outline

1. Today’s Web






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)


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


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.


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


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


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


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


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


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.



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



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



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.



The agent came back with an alternative solution: – A therapist with an excellent reputation who

had available appointments starting in 2 days



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.



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.



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



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.


Lecture Outline

1. Today’s Web






Semantic Web Technologies

Explicit MetadataOntologiesLogic and InferenceAgents


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


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.


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.


A Better Representation

<company><treatmentOffered>Physiotherapy</treatmentOffered><companyName>Agilitas Physiotherapy Centre</companyName><staff>

<therapist>Lisa Davenport</therapist><therapist>Steve Matthews</therapist><secretary>Kelly Townsend</secretary>

</staff></company>


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


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?


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.


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.


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.


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


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

1-43 A Semantic Web Primer, 2nd EditionChapter 1


Example of a Class Hierarchy


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


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.


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


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)


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


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)


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


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


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


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


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


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)

– Rule from the shop’s terms and conditions:

purchase(X,Item)price(Item, Price)delivered(Item,X)

owes(X, Price)


Software Agents

Software agents work autonomously and proactively – They evolved out of object oriented and component-based

programming

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


Intelligent Personal Agents


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


Semantic Web Agent Technologies (2)

Further technologies (orthogonal to the Semantic Web technologies)– Agent communication languages

– Formal representation of beliefs, desires, and intentions of agents

– Creation and maintenance of user models.


Lecture Outline

1. Today’s Web






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


The Semantic Web Layer Tower


Semantic Web Layers

XML layer– Syntactic basis (send docs across the Web)

RDF layer– RDF basic data model for facts– Does not rely on XML, but has XML-based syntax – RDF Schema simple ontology language

Ontology layer– More expressive languages than RDF Schema– Current Web standard: OWL


Semantic Web Layers (2)

Logic layer – enhance ontology languages further– application-specific declarative knowledge

Proof layer– Proof generation, exchange, validation

Trust layer– Digital signatures– recommendations, rating agencies ….


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






Alternative SW architectures – non TBL [Horrocks et al., 2005]/v1


Alternative SW architectures – non TBL [Horrocks et al., 2005]/v2


Alternative SW architectures – non TBL [Gerber et al., 2008]


Lecture Outline

1. Today’s Web





1-73 Chapter 1 A Semantic Web Primer, 2nd Edition

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


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.


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?


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

Jeopardy challenge1-78 Chapter 1 A Semantic Web Primer, 2nd Edition

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.



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.1-81 Chapter 1 A Semantic Web Primer, 2nd Edition

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


Example of incorporating metadata in a web page


Example of metadata in a web page: XHTML+RDFa code for content & metadata


Example of metadata in a web page: RDF/XML code for metadata


Example of metadata in a web page: Metadata in RDF N-triples format


Example of metadata in a web page: RDF metadata in a graph (semantic net)


Some Semantic Web Applications


Chapter 1 A Semantic Web Primer, 2nd Edition89




SPLISA Rule-Based Semantic Personalized Location Information System



University Rankings


Find unique (?) DBPedia URL


Using the DBPedia lookup service


Using a SPARQL endpoint



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


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.


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.


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.