1. Where are the Semantics in the Semantic Web? Michael Ushold The Boeing Company

2. Evolution of the Web Locating Resources: evolving from keyword search to semantic search Users: evolving from human only to human and machines Services: evolving from a place to find things to a place to do things. Semantics: evolving from little or no explicit semantics to rich semantic infrastructure

3. Semantic Web Vision No widespread agreement on exactly what the semantic web is Clear emphasis on “Machine usable Web content” Has “more meaning” Requires machines to: “know” how to recognize content “know” what to do when they encounter it Machine access to the “semantics” of Web content is at the heart of confusion about the Semantic Web.

4. Semantics: A Many-Splendored Thing “Semantics” = “meaning” Kinds of semantics Real-world: mapping of model into the real world for human interpretation Agent communication language performatives: e.g. request or inform Axiomatic: set of descriptions expressed in a logic language Model-theoretic: describes conditions objects must satisfy to be assigned meaning Intended vs. actual meaning: we usually intend to describe one model but actually describe several Things that have semantics Terms referring to real-world objects (e.g. semantic markup) Terms in agent communication languages (e.g. inform) Languages for representing these terms (e.g. OWL)

5. Semantic Continuum “hardwired” reduce hardwiring and begin to infer something

6. Machine Processible Semantics Possible for agents to automatically infer something How – when never before encountered? Extremely difficult for humans, never mind machines Have to make assumptions Language heterogeniety: a single language already known to the agent Incompatible conceptualizations: (e.g. time intervals vs. time points) must be compatible Term heterogeneity: impossible to guess intended meaning; must correspond to a publicly declared concept (i.e. must be a concept in a shared ontology)

7. Formal Semantics for Machine Processing Semantics of the ontology “hardwired” – but the agent can infer automatically.

8. Side Comments Ushold’s comments “… remains an unproven conjecture that such approaches will enhance search capabilities, or have significant impact … on the Web.” “… insufficient business drivers to motivate venture capitalists to heavily invest in Semantic Web companies.” Other comments “A computer doesn’t truly ‘understand’ anything, but computers can manipulate terms in ways that are useful and meaningful to the human user” [Berners- Lee] “Key Point: the manipulation only has to be good enough. And that’s our challenge and our opportunity!”

9. Requirements for Machine Usable Content The machine needs to know what to do with the content it encounters. Since humans write programs, humans must know what to do. So, humans must know the meaning of the expected content. Then, humans can “hardwire” the agents Or, humans can “hardwire” the semantic specifications used by agents and can even share these specifications publicly.

10. “Hardwiring” Automatically determining meaning of Web content likely impossible Thus, humans will always be “hardwiring” semantics into Web applications Question: What is “hardwired” and what is not? “Hardwire” term understanding into every agent? “Hardwire” the semantics using representation languages Only one language? Only one conceptualization? (or do we need mappings back and forth?) Shared, publicly declared ontologies? (or private?)

11. “Agreeing” The more agreement the better (?) Emerging standards (OWL) Without agreement effort required to make sure the concepts are right Guesswork may undermine the reliability of applications

12. “Sharing” Assumed we will share Standards emerging in a variety of sectors Dublin Core for elements like title, subject, date, … NewsML and PRISM for news and magazine publishing

13. Web shopping agents work because … Humans know how to proceed: Everything hardwired Although no agreement, strong overlap in underlying concepts Semantics not specified, but generally understood Although no public standards, the general understanding makes them unnecessary The requirements are met: Humans know the meaning Humans know what to do with the content Humans program the machine to know what to do

14. Side Questions What’s “hardwired” and what’s not for our information extraction ontologies? How do we manage “agreement”? Do we (could we) “share”?

15. Vision of the Semantic Web Do Web shopping agents satisfy the vision? Probably not “Degenerate” case – perhaps Genuine examples – are there any? How to move toward the vision: Move along the semantic continuum to more clearly specified (formal) semantics Note: there’s nothing inherently good about being further along the continuum “What is good is what works.” Reduce the amount of hardwiring Change which parts are hardwired Increase the amount of inference Increase the amount of public standards and agreements: “The more agreement there is, the less it is necessary to have machine processable semantics.” Develop technologies for semantic mapping and translation

16. So, Where are the Semantics in the Semantic Web? Often just in the human In informal specification documents Hardwired in implemented code In formal specifications to help humans understand and/or write code Formally encoded for machine processing In axiomatic and model-theoretic semantics of representation languages