What will I talk about?

• The history of the Semantic Web goes back to several years now
• It is worth looking at what has been achieved, where we are, and where we might be going…

Let us look at some results first!

The basics: RDF(S)

• We have a solid specification since 2004: well defined (formal) semantics, clear RDF/XML syntax
• Lots of tools are available. Are listed on W3C’s wiki:
  • RDF programming environment for 14+ languages, including C, C++, Python, Java, Javascript, Ruby, PHP,… (no Cobol or Ada yet !)
  • 13+ Triple Stores, ie, database systems to store (sometimes huge!) datasets
  • converters to and from RDF
  • etc
• Some of the tools are Open Source, some are not; some are very mature, some are not: it is the usual picture of software tools, nothing special any more!
• Anybody can start developing RDF-based applications today

The basics: RDF(S) (cont.)

• There are lots of tutorials, overviews, and books around
• Active developers’ communities
• Large datasets are accumulating
  • IngentaConnect bibliographic metadata storage: over 200 million triplets
  • RDF access to Wikipedia: more than 27 million triplets
  • tracking the US Congress: data stored in RDF (around 25 million triplets)
  • RDFS/OWL Representation of Wordnet: also downloadable as 150MB of RDF/XML
  • “Département/canton/commune” structure of France published by the French Statistical Institute
  • Geonames Ontology and associated RDF data: 6 million (and growing) geographical features
  • RDF Book Mashup, integrating book data from Amazon, Google, and Yahoo
  • “dbpedia”: get categorial data of Wikipedia into RDF
• Some mesaures claim that there are over 10⁷ Semantic Web documents… (ready to be integrated…)

Ontologies: OWL

• This is also a stable specification since 2004
• Separate layers have been defined, balancing expressibility vs. implementability (OWL-Lite, OWL-DL, OWL-Full)
• Looking at the tool list on W3C’s wiki again:
  • a number programming environments (in Java, Prolog, …) include OWL reasoners
  • there are also stand-alone reasoners (downloadable or on the Web)
  • ontology editors come to the fore
• OWL-DL and OWL-Lite relies on Description Logic, ie, can use a large body of accumulated research knowledge

Ontologies

• Large ontologies are being developed (converted from other formats or defined in OWL)
  • eClassOwl: eBusiness ontology for products and services, 75,000 classes and 5,500 properties
  • the Gene Ontology: to describe gene and gene product attributes in any organism
  • BioPAX, for biological pathway data
  • UniProt: protein sequence and annotation terminology and data

Vocabularies

• There are also a number “core vocabularies” (not necessarily OWL based)
  • SKOS Core: about knowledge systems, thesauri, glossaries
  • Dublin Core: about information resources, digital libraries, with extensions for rights, permissions, digital right management
  • FOAF: about people and their organizations
  • DOAP: on the descriptions of software projects
  • Music Ontology: on the description of CDs, music tracks, …
  • SIOC: Semantically-Interlinked Online Communities
  • vCard in RDF
  • …
• One should never forget: ontologies/vocabularies must be shared and reused!

Querying RDF: SPARQL

• Querying RDF graphs becomes essential
• SPARQL is almost here
  • query language based on graph patterns
  • there is also a protocol layer to use SPARQL over, eg, HTTP
  • hopefully a Recommendation end 2007
• There are a number of implementations already
• There are also SPARQL “endpoints” on the Web:
  • send a query and a reference to data over HTTP GET, receive the result in XML or JSON
  • applications may not need any direct RDF programming any more, just a SPARQL endpoint
• SPARQL can also be used to construct graphs!

Of course, not everything is so rosy…

• There are a number of issues, problems
  • how to get RDF data
  • missing functionalities: rules, “light” ontologies, fuzzy reasoning, necessity to review RDF and OWL,…
  • misconceptions, messaging problems
  • need for more applications, deployment, acceptance
  • etc

How to get RDF data?

• Of course, one could create RDF data manually…
• … but that is unrealistic on a large scale
• Goal is to generate RDF data automatically when possible and “fill in” by hand only when necessary

Data may be around already…

• Part of the (meta)data information is present in tools … but thrown away at output
  • e.g., a business chart can be generated by a tool: it “knows” the structure, the classification, etc. of the chart, but, usually, this information is lost
• storing it in web data would be easy!
• “SW-aware” tools are around (even if you do not know it…), though more would be good:
  • Photoshop CS stores metadata in RDF in, say, jpg files (using XMP)
  • RSS1.0 feeds are generated by (almost) all blogging systems (a huge amount of RDF data!)
  • …

Data may be extracted (a.k.a. “scraped”)

• Different tools, services, etc, come around every day:
  • get RDF data associated with images, for example:
    • service to get RDF from flickr images (see example)
    • service to get RDF from XMP (see example)
  • XSLT scripts to retrieve microformat data from XHTML files
  • scripts to convert spreadsheets to RDF
  • etc
• Most of these tools are still individual “hacks”, but show a general tendency
• W3C’s new GRDDL technology is a formal way of doing this for XML/XHTML

Linking to SQL

• A huge amount of data in Relational Databases
• Although tools exist, it is not feasible to convert that data into RDF
• Instead: SQL ⇋ RDF “bridges” are being developed:
  • a query to RDF data is transformed into SQL on-the-fly
  • the modalities are governed by small, local ontologies or rules
• An active area of development, on the radar screen of W3C!
• There are a number of projects “harvesting” and linking data to RDF (e.g., “Linking Open Data on the Semantic Web” community project)

SPARQL as a unifying point?

Missing features, functionalities…

• Everybody has a favorite item, ie, the list tends to infinite…
• W3C is a standardization body, and has to look at where a consensus can be found

Rules

• OWL-DL and OWL-Lite are based on Description Logic; there are things that DL cannot express
  • a well known examples is Horn rules:
    • (P₁ ∧ P₂ ∧ …) → C
  • there are a number of attempts to combine these: RuleML, SWRL, cwm, …
• There is also an increasing number of rule-based system that want to interchange rules
  • a new type of data (potentially) on the Web to be interchanged…

Rules (cont)

• Some typical use cases
  • Negotiate eBusiness contracts across platforms: supply vendor-neutral representation of your business rules so that others may find you
  • Describe privacy requirements and policies, and let clients “merge” those (e.g., when paying with a credit card)
  • Medical decision support, combining rules on diagnoses, drug prescription conditions, etc,
  • Extend RDFS (or OWL) with rule-based statements (e.g., the uncle example)
• The “Rule Interchange Format” Working Group is working on this problem as we speak…

“Light” ontologies

• For a number of applications RDFS is not enough, but even OWL Lite is too much
• There may be a need for a “light” version of OWL, just a few extra possibilities v.a.v. RDFS
• There are a number of proposals, papers, prototypes around: EL++, RDFS++, OWL Feather, pD*, DL Lite,…
• This might consolidate in the coming years

New versions of RDF and OWL?

• Such specifications have their own life
• Missing features come up, errors show up
• There may be a next version at some point
  • but: it is always a difficult decision; introducing a new version creates uncertainty in the developers’ community 

Other items…

• Revision of the RDF model (eg, no restriction on predicates and literals)
• Revision of OWL (you may have heard of OWL1.1…)
• Fuzzy logic
  • look at alternatives of Description Logic based on fuzzy logic
  • alternatively, extend RDF(S) with fuzzy notions
• Probabilistic statements
• Security, trust, provenance
  • combining cryptographic techniques with the RDF model, sign a portion of the graph, etc
• Ontology merging, alignment, term equivalences, versioning, development, …
• etc

A major problem: messaging

• Some of the messaging on Semantic Web has gone terribly wrong . See these statements:
  • “the Semantic Web is a reincarnation of Artificial Intelligence on the Web”
  • “it relies on giant, centrally controlled ontologies for "meaning" (as opposed to a democratic, bottom–up control of terms)”
  • “one has to add metadata to all Web pages, convert all relational databases, and XML data to use the Semantic Web”
  • “it is just an ugly application of XML”
  • “one has to learn formal logic, knowledge representation techniques, description logic, etc, to use it”
  • “it is, essentially, an academic project, of no interest for industry”
  • …
• Some simple messages should come to the fore!

RDF ≠ RDF/XML!

• RDF is a model, and RDF/XML is only one possible serialization thereof
  • lots of people prefer, for example, Turtle
  • a good percentage of the tools have Turtle parsers, too!
• The model is, after all, simple: interchange format for Web resources. That is it !

RDF is not that complex…

• Of course, the formal semantics of RDF is complex
• But the average user should not care, it is all “under the hood”
  • how many users of SQL have ever read its formal semantics?
  • it is not much simpler than RDF…
• People should “think” in terms of graphs, the rest is syntactic sugar!

Semantic Web ≠ Ontologies on the Web!

• Formal ontologies (like OWL) are important, but use them only when necessary
  • you can be a perfectly decent citizen of the Semantic Web if you do not use Ontologies, not even RDFS…
  • remember the “light ontologies” issue?

SW Ontologies ≠ some central, big ontology!

• The “ethos” of the Semantic Web is on sharing, ie, sharing ontologies (small or large)
• A huge, central ontology would be unmanageable
• OWL includes statements for versioning, for equivalence and disjointness of terms
  • a revision of those may be necessary, but the goal is clear
• The practice:
  • SW applications using ontologies always mix large number of ontologies and vocabularies (FOAF, DC, and others)
  • the real advantage comes from this mix: that is also how new relationships may be discovered

Semantic Web ≠ an academic research only!

• SW has indeed a strong foundation in research results
• But remember:
  • (1) the Web was born at CERN…
  • (2) …was first picked up by high energy physicists…
  • (3) …then by academia at large…
  • (4) …then by small businesses and start-ups…
  • (5) “big business” came only later!
• network effect kicked in early…
• Semantic Web is now at #4, and moving to #5!

Some Semantic Web deployment communities

• The technology is picked up by specialized communities
  • just like the high energy physics community did for the original Web…
• Some examples: digital libraries, defence, eGovernment, energy sector, financial services, health care, life sciences…
• Health care and life science sector is now very active
  • also at W3C, in the form of an Interest Group

The “corporate” landscape is moving

• Major companies offer (or will offer) Semantic Web tools or systems using Semantic Web: Adobe, Oracle, IBM, HP, Software AG, webMethods, Northrop Gruman, Altova,…
• Some of the names of active participants in W3C SW related groups: ILOG, HP, Agfa, SRI International, Fair Isaac Corp., Oracle, Boeing, IBM, Chevron, Siemens, Nokia, Merck, Pfizer, AstraZeneca, Sun,…
• “Corporate Semantic Web” listed as major technology by Gartner in 2006

Data integration

• Data integration comes to the fore as one of the SW Application areas
• Very important for large application areas (life sciences, energy sector, eGovernment, financial institutions), as well as everyday applications (eg, reconciliation of calendar data)
• Life sciences example:
  • data in different labs…
  • data aimed at scientists, managers, clinical trial participants…
  • large scale public ontologies (genes, proteins, antibodies, …)
  • different formats (databases, spreadsheets, XML data, XHTML pages)
  • etc

General approach

1. Map the various data onto RDF
   • assign URI-s to your data
   • “mapping” may mean on-the-fly SPARQL to SQL conversion, “scraping”, etc
2. Merge the resulting RDF graphs (with a possible help of ontologies, rules, etc, to combine the terms)
3. Start making queries on the whole!

Remember the role of SPARQL?

A number of projects in the area

• Pfizer, NASA, Eli Lilly, MITRE Corp., Elsevier, EU Projects like Sculpteur and Artiste, UN FAO’s MeteoBroker, DartGrid, …
• Developments are under way at various places in the area

Example: ontology controlled annotation

Example: find the right experts at NASA

Expertise locator for nearly 20,000 NASA civil servants using RDF integration techniques over 6 or 7 geographically distributed databases, data sources, and web services…

(Courtesy of Clark & Parsia, LLC)

Portals

• Vodafone's Live Mobile Portal
  • search application (e.g. ringtone, game) using RDF
    • page views per download decreased 50%
    • ringtone up 20% in 2 months
• Other portal examples: Sun’s White Paper Collections and System Handbook collections; Nokia’s S60 support portal; Harper’s Online magazine linking items via an internal ontology; Oracle’s virtual press room; Opera’s community site, Yahoo! Food, FAO's Food, Nutrition and Agriculture Journal portal,…

Other Application Areas Come to the Fore

• Knowledge management
• Business intelligence
• Linking virtual communities
• Management of multimedia data (e.g., video and image depositories)
• Content adaptation and labeling (e.g., for mobile usage)
• etc

Thank you for your attention!

These slides are publicly available on:

http://www.w3.org/2007/Talks/0424-Stavanger-IH/

in XHTML and PDF formats; the XHTML version has active links that you can follow