
Verb/predicate/Property using same URIs
Is is something to do with "meaning" or "roles given by natural language processing" or...
Semantic Web = structured data with a simple formal semantics as a Web standard.
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


Subject and object node using same URIs
The semantic Web’s real selling point is URI-based data integration.Users need to re-use URIs for vocabularies and entities in order to get the full benefit of the Semantic Web.
Semantic Web-enabled ontology mapping and entity integration does not seem to work quite well at the moment in open domains...)Why not use Web search?: But if a user searches for a concept via keywords, you get too many URIs and its very user-hostile to inspect the RDF (or lack thereof) at these URIs to ascertain their meaning.
How can we get semantic search results to be better?Hypothesis
How can we actually make a virtuous cycle between hypertext search and semantic search in order to improve results of structured data without asking user to do anything different than they currently do.Question:Is there too many or no URIs for things?
Answer: : Sample the Semantic Web using a query log, prune any query with less than 10 repetitions.
Data Set: Microsoft Live Search Query Logs
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.
There are too many URIs for the same thing probably...
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...
Top 10 Domain Names: Entities
Top 10 Domain Names: Concepts
No-one naturally will rank and look at raw RDF except Semantic Web developers: ordinary users will not use Tabulator or other linked data browsers.
The amount of co-ordination needed does not need full agreement, but only the minimal agreement needed to get the task at hand completed.
How can we boot-straps URIs for things and re-use these URIs in a way that people will actually use?
Answer: Use a paradigm average Web users are familiar with, such as a search paradigm and use their click-throughs as relevance feedback to re-rank URIs.
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. 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 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.
Text automatically extracted from hypertext documents is `messy,' being of low quality and bursty, with highly varying document lengths.
The amount of triples in Semantic Web documents follow a power-law, so there are wildly varying document lengths of both the relevance model and the document models.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.
| 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.
Results: Finding the best URIs on the Semantic Web can be built out of the social semantics implicitly given by the searching behavior of ordinary users.
My implementation that ranks all Semantic Web search engines is here.
We need to deploy methodology from IR, NLP, machine-learning, databases, and actual human evaluation in order to make the Semantic Web actually work.
We should probably start going "back to basics" with RDF, emphasizing URI re-use, high quality associated descriptions, links, and simple vocabularies and entities humans want to use rather than high-level ontologies, inference, and agents.