Is is something to do with meaning? Reference? Formal Semantics?
To a computer, then, the web is a flat, boring world devoid of meaning...This is a pity, as in fact documents on the web describe real objects and imaginary concepts, and give particular relationships between them...Adding semantics to the web involves two things: allowing documents which have information in machine-readable forms, and allowing links to be created with relationship values.TimBL, WWW1994
The Semantic Web uses URIs not just to refer to web-pages, but to things.
For the Semantic Web to work, there must be agreement from decentralized agents on what "things" a URI refers to.
Everything Must Have a URI?: The Eiffel Tower versus a web-page about the Eiffel Tower: Isn't there something fundamentally different? And what URI do you use for the Eiffel Tower qua Eiffel Tower?
The URI of the web-page is clearly http://www.tour-eiffel. fr/.
Thesis
How can in an agent determine what does a URI refer to or mean? This is trivial for the hypertext web, as it simply can be given by whatever web-page is accessed by the URI. But on the Semantic Web, URIs are used to refer!We're going to have use an interdisciplinary approach from two rarely connected fields: philosophy and the World Wide Web.
Information: whatever in common between two things, realized by a message.
Encoding: a set of precise regularities that can be realized by a message. (English syntax, volatages)
Content: whatever is held in common between the source and the receiver as a result of the conveyance of a particular information-bearing message. (Semantics of a sentence, pictures)
The priority of meaning over reference
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.
Sense = Content of Information (the result of an interpretation of an encoding).
Representations are just information with distal content.
By virtue of some distant causal connection, some representation does work as a "stand-in" for a distal referent in order to accomplish some purpose.
Resource: Any thing capable of having identity. A resource is typically not a particular encoding of the information but the sense that can be given by many encodings (media types).
Web Representation: The encoding of the content given by a resource given in response to a request to a URI.
Subject and object node using same URIs
The Semantic Web’s real selling point is URI-based data integration.Subject/Property/Object of RDF are just like semantic networks, but with URIs
Infamous argument between Pat Hayes and Tim Berners-Lee.
Social Meaning and RDF: "the meaning of an RDF document includes the social meaning, the formal meaning, and the social meaning of the formal entailments" so that "when an RDF graph is asserted in the Web, its publisher is saying something about their view of the world" and "such an assertion should be understood to carry the same social import and responsibilities as an assertion in any other format" RDF Concepts and Abstract Syntax DraftBerners-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"
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 sense We are also assuming the real world (as opposed to any logical idealization of it) is divided neatly into individuals.Russell 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 extentional satisfication, 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.
From what does one build the model of a RDF graph? All the triples that use a URI on the Web? Just the triples at hand? Triples accessible via the URI?
In this reading, URIs are no different than any other symbol, and access via a URI is orthogonal to its referential use. Therefore, the Semantic Web is not interestingly different from any other KR language!A name can be transmitted through time via historical and causal chains, with a name being given its original referent through a process Kripke calls baptism.
Putnam's doctrine of natural kinds being assigned names by scientists is an expansion of the causal theory of reference to deal with more than proper names.
A URI is given its meaning by a baptism by its owner, such as registering a domain name.
Only the intended referent of the name matters and is primary over any accessible Web representations, and each URI should be given to an individual referent.The application of Web architecture and the 303 decision to the Semantic Web is the second-generation Web of linked data.
Claims billions of RDF triples!Question:Are there too many or no URIs of interest?
Answer: : Sample the Semantic Web using a query log, prune any query with less than 10 repetitions.
Use brute-force simple rules and gazetteers (U.S. Census for names, Alexandra project for places, Wordnet and hyponym with hypernyms) to discover named entities with low recall and high precision.
Data Set: Microsoft Live Search Query Log for 1 month from 2007
There are too many URIs for the same thing...
An average of 1,339 URIs (S.D. 8,000) returned per query. That's a lot, but most may obviously be URIs that just mention the term...
Entity Queries: alpha = 2.31, with long tail behavior starting around a frequency of 17 and a Kolmogorov-Smirnov D-statistic of .0241, indicating a significant good fit.
Concept Queries: The alpha= of the queries for concept queries was calculated to be 2.12, with long tail behavior starting around a frequency of 36 with a Kolmogorov-Smirnov D-statistic of .017.
Question: Is the amount of Linked Data returned correlated with the popularity of the query?
No: Spearman's rank correlation statistic was the insignificant .0077 ($p > .05), while for concept queries, the correlation was the still insignificant at .0125 ($p > .05$)
Number | Percent | Language Construct |
73,451 | 30.31% | rdfs:Class |
47,044 | 19.30% | rdfs:comment |
44,113 | 18.10% | rdfs:subClassOf |
8,630 | 3.54% | owl:Ontology |
7,256 | 2.97% | rdfs:label |
6,618 | 2.14% | rdf:Subject |
5,107 | 2.09% | owl:ObjectProperty |
3,642 | 1.49% | rdfs:subPropertyOf |
1,157 | 0.47% | owl:sameAs |
535 | 0.29% | rdfs:range |
One of the most popular OWL constructs is indeed the controversial owl:sameAs term used to declare equivalence.
One critique holds that it is declaring equivalence between things that are not equivalent, so equivalence is being used too much.Only 0.47% of overall Semantic Web modelling term usage, it is far from insignificant, with 1,157 occurrences.
Given the amount of Semantic Web URIs returned by the queries, it appears that the manual discovery and publication of co-referential URIs using owl:sameAs falls far behind the actual growth of Linked Data.Surprise! Likely owl:sameAs is not being used enough.
The amount of co-ordination needed does not need full agreement or lack of ambiguity, but only the minimal agreement needed to get the task at hand completed.
All words in a language (even knowledge representation languages) are given meaning by their use with other words (language-game), and these parasite off the meaning of natural language terms.
Sense can be reconstructed to be construed in terms of the socially-grounded objective use that is necessary in order to grasp the use of a name across a language.
The sense (and secondarily, possibly reference can only be detected, by the use of the language in an embodied form of life, an agent accomplishing real-world tasks.
Answer: Use a form-of-life average Web users are familiar with, such as a search paradigm.
Since knowledge representation languages and hypertext web-pages use the same sense, they share a representational nexusWe can then use relevance feedback from the hypertext web-pages to improve the results of the Semantic Web search.
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.
In their instructions, relevance was defined can be determined by whether or not accurate information about the information need is expressed by the result. This excludes both link farms, non-standard redirects, and legitimate hubs.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. Fleiss'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 Relevance Judgements:
Results: | Hypertext | Semantic Web | Resolved: | 197 (98%) | 132 (66%) | Unresolved: | 3 (2%) | 68 (34%) | Top Relevant: | 121 (61%) | 76 (58%) |
Top Non-Relevant: | 76 (39%) | 56 (42%) |
Results of Querying the Hypertext Web
Results of Querying the Semantic Web
Average Precision Scores for Vector-space Model Parameters: Relevance Feedback From Hypertext to Semantic Web
Next result was BM25 with the slight performance-enhancing modifications used in the InQuery system and comparison function used with standard Rocchio relevance feedback with slight modifications as used by Okapi and window size = 100 was best.So, it is unwise to normalise the models, as that will almost certainly dampen the effect of valuable features like crucial keywords.
The reason BM25-based vector models in particular perform so well is that they are able to effectively keep track of both term frequency and inverse term frequency accurately.BM25 provides a slight amount of rather unprincipled non-linearity in the importance of the various variables, effectively keep track of both term frequency and inverse term frequency accurately while massively lowering the power of another document length.
Average Precision Scores for Language Model Parameters: Relevance Feedback From Hypertext to Semantic Web
The best relevant models sampled over top 10,000 words with a cross entropy smoothing factor set to .5. Relevance models over all concatenated relevant documents beats relevance models with documents sampled individually and then combined, as well as all vector-space models.Results: | Feedback | FALCON-S |
Top Relevant: | 118 (89%) | 76 (58%) |
Non-Relevant Top: | 14 (11%) | 56 (42%) |
Non-Relevant Top Entity: | 9 (64%) | 23 (41%) |
Non-Relevant Concept: | 5 (36%) | 33 (59%) |
A respectable 19% in average precision over the engine FALCON-S, intuitively makes the system's ability to place a relevant URI in the top rank acceptable for most users.
The distribution of `natural' language terms extracted from RDF terms, while often irregular, will either be repeated very heavily or fall into the sparse long tail, which can then be dealt with by relevance models.
The hypertext search engine is being `seeded' with a high-quality accurate description of the information need expressed by the query to be used for query expansion.Please see paper for actual equations describing all the IR frameworks we used, i.e. relevance models, BM25, Ponte's method, local content analysis, and more.
The Web is an autonomous subject-matter from artificial intelligence, one that is built on Licklider and Engelbart's vision of humans and machines "collective intelligence."
Not only natural languages but knowledge representation languages can have issues of sense and reference.
The primary building block of the Web is the URI, which identifies a resource with a distinct sense that may have multiple encodings.
The real substantive claim of the Semantic Web is that the use of URIs for non-Web accessible entities and concepts will let knowledge representation globalize
Yet there is no working theory of reference for the Semantic Web, thus URIs are not re-used and shared globally
There are currently too many URIs for a single entity or queries on the Semantic Web
A neo-Wittgensteian theory of reference would ground meaning, sense, and reference in actual forms of life.
The primary form of life on the Web is keyword-based searching of natural language hypertext web-pages, so the Semantic Web should build its theory of meaning on that.
There are too many URIs returned for a single sense as given by a set of keywords
We can use relevance feedback from hypertext web-pages to select the "best" URI from the Semantic Web.
Is this a unified picture?
Thesis Result: Finding and giving meaning to URIs on the Semantic Web can be built out of the semantics implicitly given by natural language the searching behavior of ordinary users - a public language position inspired by Wittgenstein.
Future work includes:/p>
Any questions?