The Evolving Web • The (possibly informal) semantics of 'inform' is embedded in a procedure by a human.

• The system places a call to the procedure when it encounters 'inform'.

• The 'meaning' of 'inform' is what happens when this procedure is called.

• Machine processible semantics? -perhaps.

Machine Processible Semantics

• Learning the meaning of a term from a formal declarative specification of the semantics…

• General case: no assumptions, nothing shared • all symbols might as well be in 'Greek' script • no knowledge of language syntax, or semantics • Cryptography, impossible to automate • So, we have to cheat…

• We must make some assumptions… Assumptions: language

• Shared language syntax and semantics,

• e.g. KIF, RDF(S), DAML+OIL

• But: may have incompatible assumptions in conceptualization.

• Time point, vs. time interval • Agent can never incorporate meaning of new term in its axioms.

More Assumptions: compatibility

• Logical compatibility as well as language.

• But: Different people build different ontologies for the same domain.

• Two terms, same meaning, or vica versa; • Same concept modeled at different level of detail; • Different language primitives used for same concept; -e.g. red an attribute, or RedThings a class.

• Computationally intractable to determine if two terms actually mean the same thing. • 'mammal' defined in shared animal ontology in OIL.

• Machine can learn something about meaning.

• I.e. there are now more things that it cannot be.

• Still plenty of scope for ambiguity; • Definition of mammal in OIL can never be complete.

• Can do some inference • e.g. for search application looking for content about mammals.

When is Semantic Web Needed?

• Good Question! Where are the use cases?

• No case made for search, at least not for humans. Google works brilliantly!

• Build it and they will come! Or will they?

Law of the Semantic Web?

The more agreement there is, the less it is necessary to have "machine sensible semantics".

• E.g. <h2> in HTML specification;

• No need to do inference;

• Just embed the semantics in the browsers.

•Locating Resources • free text & keyword search semantic search • Web Users • primarily humans both humans and machines • Web Tasks & Services • a place to find things a place to do things Semantics is the Core Requirement • web content with no semantics with semantics Agents and the Semantic Web • Semantic Web: killer 'app' for agents? • Agents need to communicate and understand meaning. • Advertise and require capabilities • Locate meaningful information resources on web & combine them in meaningful ways to perform tasks • How to interpret communication acts? • But what do we mean by the Semantic Web? TBL's Vision • Extension of current web; • Layered, extendible, composable; • Meta-data, Ontologies, KBs, Agents, WWKB • Inference, proofs, queries • 'Semantics' -in machine processible form. What do we mean by 'Semantics'? • Semantics of What? • language?, term?, expression? • communication protocol? • domain ontology & markup! • Plicity: Are the semantics implicit or explicit? • Formality: How are semantics expressed? • Semantics Processing: Who are they for? • human only -fully manual • human and computer -partially automated • computer only -fully automated -<delivery-date> … </delivery-date> • Used by screen-scrapers, wrappers • Rife with ambiguity. • Informal: only humans can use (until NLP solved) • Text specification document for HTML e.g. <h2> • UML semantics document • Java language definition, for compiler writers • Still ambiguous Examples • 'Formal Comments' • Semantics of FIPA ACL 'inform' in modal logic • Formal definitions in any requirements spec (e.g. Z) • Many axioms in Ontolingua ontologies • Much less ambiguous • Still error-prone, human in the loop. • Automated • RDF(S), DAML+OIL term definitions e.g. mammal, date • How does the machine process the semantics? Machine Processible Semantics • How can an agent learn the meaning of a term? • Procedural Semantics • How does an agent system know what to do when it sees the term 'inform'

• I.e. have same set of models More Assumptions: sharing • Term explicitly mapped to a shared concept • Encounter new term, leprechaun, a subclass of mammal.

•Analogy: So what if my toaster can talk to my washing machine! • What would they say? • Does this improve my life?

Acknowledgements

Material from this lecture was drawn from many fruitful discussions with:

Processing Semantics

• Relies on a formal semantics of OIL to infer semantics of terms and expressions in OIL.

• OIL semantics is for humans

• it helps build inference engines;

• not machine processible.

• Humans may still embed some meaning in code

• May be dangerous to do so -or -• May be necessary to do so…

Summary:

Where IS the semantics?

• Often just in the human.

• Informally in specification documents.

• Embedded in implemented code.

• Formal Comments to help humans understand and/or write code.

• Formally encoded for machine processing • In the representation language specification Summary: Characterizing the Semantic Web

• Purpose, Benefits, Mechanisms of semantics • What has the semantics?

• Language? Terms? Communication protocols?

• Representing and Processing semantics

• Implicit or Explicit?

• Formal or Informal?

• For human or for computer?

• Agreement and Sharing of semantics

• Does agreement reduce need for explicit semantics?