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Links Joining Across Different Kinds of Information using URIs. URIs not just to refer to web-pages, but to things.
There must be agreement from decentralized agents on what a URI means or denotes - i.e. its semantics.
Berners-Lee said "a single meaning is given to each URI" which is summarized by the slogan that a URI "identifies one thing."
Hayes retorted that "I'm not saying that the `unique identification' condition is an unattainable ideal: I'm saying that it doesn't make sense, that it isn't true, and that it could not possibly be true. I'm saying that it is crazy" since it goes against the "basic results in 20th century linguistic semantics"Berners-Lee responded that "`we are not experimental philosophers, we are philosophical engineers" such that "we are not analysing a world, we are building it"
Example:Hesperus has a sense ("the morning star") different from that of Phosphorus ("the evening star"), yet both have the same referent, the planet Venus.
Patches of sense-data known through direct acquaintance allow one to ground the atoms of logical statements or create descriptions that can form the basis for names.
The priority of reference over senseRussell deals with referents in the past, or imaginary referents by having a name be a short-cut to a set of logical expressions.
Names are shorthand for logical expressions - so the name "Eiffel Tower" includes a host of facts about it such as "in Paris" and "completed in 1889", as well as an existential quantification!Tarski removed the quaint Russellian sense-data epistemology, grounding out in just a mathematical model defined by extensional satisfaction, composition, and axioms.
This formal semantics became the foundation of the Semantic Web via the RDF Formal Semantics specified by Pat Hayes.
Ambiguity is built in: The web of logical statements is the bearer of meaning, and whatever satisfies the model could be a referent.
Before one reasons using logic, one must share a form-of-life so that one can agree on the assumptions.
This means that two entities must not only share the set of possible referents, but the Fregean "sense" of each referent. How can we formalize the notion of sense?
Answer: Use the computational traces of users in their everyday Web activity, with the end-goal of building formal theories of reference upon the "pretty-good-enough" statistical approximations of sense.We hypothesise this philosophical notion of sense can be approximated by empirical statistics of natural language terms (language models) generated by the ordinary activity of information gathering- on the Web, such as tagging and searching.
A social semantics of sense can only be approximated via tagging if users agree on a macro-scale when tagging a resource.
Question:How can agents in a large decentralized agreement reach agreement on the sense of resources? Is it possible and how close is agreement?
Tagging: Description being natural language terms added after a resource is discovered.
Plotting the infamous power-law for a singe resource, averaged over 500 "popular" resources from del.icio.us (Halpin, Robu, and Shepard, 2007, World Wide Web Conference ).Distributions are rank-ordered frequencies of tags.
To determine if agreement is possible, we need to have some measure of agreement.
Kullback-Leibler Divergence: Information-Theoretic Measure to determine differences between two distributions.

Collection i distributions over i time-steps (months in del.icio.us)
Choose as P the i-1 distribution the distribution, and Q as i distribution. When KL approximates zero, distribution has stopped changing. Tagging distributions stabilize, typically within a few weeks.
Is it Just Reinforcement?: Possibly tagging is a flawed example of decentralized agreement on information agreement, due to the presence of tag reinforcement mechanisms.
There are two main models of tagging:
Cattuto et. al: Their model uses a simple Simon-Yuill process, with a probability conditioned two parameters, either you either choose choose a new word from random or you re-inforce an existing word.
Dellshaft and Staab: Their model uses a multi-parameterized process, with a probability conditioned by the exponent of the power-law, either you either choose choose a new word from "topic distribution" or you re-inforce an existing word.
Both models say reinforcement is the answer...
Experiment (Halpin and Bollen, Web Intelligence 2009): 200 participants. Half get to see reinforced tags (feedback condition), and half do not get to see reinforced tags (no feedback condition). Over 11 URIs from easy-to-understand subject matter, chosen at random URIs given by del.icio.us from popular "lifestyle" tags.
Feedback
No Feedback
The "no feedback" condition is actually a power-law - and feedback simply shortens long tail.
People in a decentralized environment, even without feedback, will in general describe resources in the same manner using natural language terms, at least as regarding popular categories and general subject matters they are equally familiar with.
This is likely to be true because tags are grounded in the natural language, and as we use the same background knowledge (form-of-life) to describe web-sites.
Further experiments are done being done with the same experimental set-up looking at specialist knowledge in tagging.
However, tagging is just one kind of generation of terms. Search engines mediate access to a far greater number of resources than collaborative tagging systems.
Consider tagging to just be post-hoc search, with a much more sparse number of terms spread over a wider group of resources.So each ontological term (URI on the Semantic Web) should have a "sense" of natural language terms. This sense could then be used as a query expansion or term expansion technique to bootstrap tasks like information retrieval and ontology mapping respectively.
Can we also look at what ontologies are associated with what keywords using Semantic Web search engines like Sindice?Can we use hypertext search terms and even decomposed hypertext documents as sources of sense? Can we have use ontologies themselves as sources of keywords?
To Test: See whether treating ontologies as a sense helps normal hypertext search, and see whether or not senses deduced from hypertext search can help the "semantic" search for ontologies?Halpin and Lavrenko, forthcoming, Journal of Web Semantics
Have a human judge actually figure out what web-pages are relevant, and then use those to feed back and expand the query, in order to re-rank the results.
Experiment: Had 200 queries from previous work, retrieved top 10 hypertext Web (Yahoo!) results and top 10 (FALCON-S) results each judged by 3 judges for relevancy. Fliess's Kappa=0.5724$ (p < .05, 95% Confidence interval [0.5678,0.5771]), indicating the rejection of the null hypothesis and moderate agreement.

Results of Querying the Hypertext Web

Results of Querying the Semantic Web
Summary of Best MAP Scores: Relevance Feedback From Hypertext to Semantic Web
MAP for Language Model Parameters: Relevance Feedback From Hypertext to Semantic Web
Summary of Best MAP Scores: Relevance Feedback From Semantic Web to Hypertext
Is this a unified picture - a computational and approximate notion of sense?
Thesis: Finding and giving meaning to ontological terms (URIs) on the Semantic Web can be built out of the social semantics implicitly given by the searching and tagging behavior of ordinary users. With Henry Thompson, currently expanding this work to test the reliability of links in "Linked Data" and TREC-style evaluations of Semantic Web search engines.
However, the major drawback of this work has been an inability to correlate tags, users, search terms, and Semantic Web ontologies (as well as group membership and social networks of these users) on a large-scale. So off to Yahoo! Research for a few months...
One question would be how to boot-strap even more structured data?
While users produce these senses for free while searching, this knowledge is trapped and can only be reliably tracked by major search engines.Laying the foundations for wide-scale capture and sharing of social semantics by ordinary users in browsers, and hopefully building a statistical and social infrastructure for the Semantic Web.