The Web is the largest database on the Earth, and Google has the largest index of this database. Two researchers at University of Amsterdam proposed a new system that uses Google search to learn and distinguish the meanings of words.

Their work is based on the theory that the meaning of a word can usually be gleaned from the words used around it. Take the word “rider”. Its meaning can be deduced from the fact that it is often found close to words like “horse” and “saddle”.

Instead relying on a common sense knowledge base such as Cyc, the reseachers use Google search to measure how closely two words relate to each other.

To do this, it needs to build a word tree - a database of how words relate to each other. It might start off with any two words to see how they relate to each other. For example, if it googles “hat” and “head” together it gets nearly 9 million hits, compared to, say, fewer than half a million hits for “hat” and “banana”. Clearly “hat” and “head” are more closely related than “hat” and “banana”.

To gauge just how closely, Vitanyi and Cilibrasi have developed a statistical indicator based on these hit counts that gives a measure of a logical distance separating a pair of words. They call this the normalised Google distance, or NGD. The lower the NGD, the more closely the words are related.

See also: “Google’s search for meaning“, New Scientist.

Developers Guide to Semantic Web Toolkits for different Programming Languages

We are collecting links to Semantic Web toolkits for different programming languages and evaluate for each toolkit:

• which features are offered (APIs, query languages, storage, reasoning support)
• the strength of the development effort (number of developers involved, latest release)
• the activity level of the toolkit’s user community (number of downloads, active mailing list)

As expected Java seems to be the preferred language, Pyhton and Perl are also catching up. JENA 2.1 has had 24600 downloads and KAON 1.2.7 14200.

Jorge Luis Borges described an interesting ontology for animals attributed to the Chinese encyclopaedia Celestial Empire of Benevolent Knowledge. This appeared in the essay ‘The Analytical Language of John Wilkins’ in Other Inquisitions, 1937-1952, (1964).

The OWL Web Ontology Language for Services (OWL-S) has been officially submitted to and and accepted by the W3C as a member submission. The submission is described as

This submission contains a proposal for a Web Services description language, the Web Ontology Language for Services (OWL-S), which builds on Semantic Web technology developed at W3C. OWL-S is an OWL-based Web service ontology, which supplies a core set of markup language constructs for describing the properties and capabilities of Web services in unambiguous, computer-interpretable form. OWL-S markup of Web services will facilitate fuller automation of Web service tasks, such as Web service discovery, execution, composition and interoperation.

The real content of the submission is included in two key documents:

• OWL-S: Semantic Markup for Web Services
• OWL-S’ Relationship to Selected Other Technologies

and in a set of eight ontologies encoded in OWL: Service, Profile, Process, Grounding, Logical Expression Constructs, List Constructs, Profile Additional Parameters, and Actor.

Swoogle is experimenting with a new interface that searches for semantic web documents using terms whose local names contain certain substrings. This seems to provide a good way to find documents about a given topic. For example, to find ontologies about time, you might search for documents using terms matching before after time instant. For efficiency reasons, we are not matching on a substring, but rather decompose the local name into one or more lexemes. For example, the local name BeliefConnective is decomposed into {belief connective}. You can, of course, still search using substrings, but queries contaning such constraints can be expensive.