The Semantic Web and Policy Workshop will be held at the 4th International Semantic Web Conference on 7 November 2005 in Galway, Ireland. The workshop is focused on two research areas:

• policy-based frameworks for the semantic web for security, privacy, trust, information filtering, accountability, etc.
• applying semantic web technologies in policy frameworks for application domains such as grid computing, networking, storage systems, pervasive computing and specifying agent communities norms.

In addition to presentations of nine submitted papers, Ora Lassila will give an invited talk on “Applying Semantic Web in Mobile and Ubiquitous Computing: Will Policy-Awareness Help?” and a panel of policy researchers will initiate a discussion of “The 2005 Web Policy Zeitgeist”. The proceedings is available and participants can register at the online.

Brooks, T.A. (2004). “The Nature of Meaning in the Age of Google”, Information Research, 9(3) has an interesting take on things.

“The characteristic tension of the culture of lay indexing is between genuine information and spam. Google’s success requires maintaining the secrecy of its parsing algorithm despite the efforts of Web authors to gain advantage over the Googlebot. Legacy methods of asserting meaning such as the META keywords tag and Dublin Core are inappropriate in the lawless meaning space of the open Web. A writing guide is urged as a necessary aid for Web authors who must balance enhancing expression versus the use of technologies that limit the aggregation of their work.”

Was it ever so? In the world of an earlier generation, of every earlier generation, was there also this tension between those with information to promote and the mediators, publishers and gatekeepers? Does it matter that the mediator is an automaton, as foreshadowed in Metropolis?

Altova, the maker of popular XML editor XMLSpy, annouced the release of Altova SemanticWorks.

Altova SemanticWorksâ„¢ 2006 is the ground-breaking visual RDF/OWL editor from the creators of XMLSpy. Visually design Semantic Web instance documents, vocabularies, and ontologies then output them in either RDF/XML or N-triples formats. SemanticWorksâ„¢ 2006 makes the job easy with tabs for instances, properties, classes, etc., context-sensitive entry helpers, and automatic format checking. It is the sensible way to put the Semantic Web to work for you.

This is a good sign for the Semantic Web research and development community. It’s a sign that semantics is getting commericial attention. I remember seeing a similar pattern back in the old days when XML was a new term that not everyone knows. Altova released their XMLSpy in a time when many people are skeptical about the use of XML. Could this mean that one or two years from now, RDF & OWL will be the key languages for building smart applications? I surely hope so.

David Wood announced in his blog that US Defense contractor Northrop Grumman has purchased the IP of of Tucana Technologies, which includes the Tucana Knowledge Server (TKS). Wood was the CTO and co-founder of Tucana, which had ceased operations in the last week of December 2004. He reports that Northrup will continue to develop TKS and also to support Kowari, the open sourced, scalable RDF database. This is great news and potenmtially significant to the continued evolution of semantic web technology.

Emergent Semantics is a new Yahoo group that describes itself as follows:

“This group is dedicated to the discussion of the emergent semantic web – a mechanism through which meaning arises out of interactions between software agents, humans and metadata. The so-called “folksonomies” Flickr, Del.icio.us are examples of such emergent phenomena. The idea is to extend this to the entire web and allow media to interact in ways currently not possible. For example, we can envisage ways in which IR algorithms can generate relations and allow for serendipitous interactions of data.”

The W3C’s Semantic Web Best Practices and Deployment Working Group aims to “provide hands-on support for developers of Semantic Web applications.” Their approach is to develop and publish notes explaining good ways to tackle common KR problems in RDF and OWL. For example, given RDF’s underlying binary relations, what are good ways to encode the n-ary relationships needed by many domains? If you are building ontologies or just trying to understand how RDF and OWL should be used, you need to take a look at these.

The working group has published three new working drafts:

• SKOS Core Guide. “SKOS Core provides a model for expressing the basic structure and content of concept schemes (thesauri, classification schemes, subject heading lists, taxonomies, terminologies, glossaries and other types of controlled vocabulary).”
• SKOS Core Vocabulary Specification. This document gives a reference-style overview of the SKOS Core Vocabulary as it stands at the time of publication. It also describes the policies for ownership, naming, persistence and change by which the SKOS Core Vocabulary is managed.
• QuickGuide to Publishing a Thesaurus on the Semantic Web. “This document describes in brief how to express the content and structure of a thesaurus, and metadata about a thesaurus, in RDF.”

These join some very useful previous working group notes and working drafts, including the following:

• Representing Classes As Property Values on the Semantic Web. “This document addresses the issue of using classes as property values in OWL and RDF Schema. It is often convenient to put a class (e.g., Animal) as a property value (e.g., topic or book subject) when building an ontology. While OWL Full and RDF Schema do not put any restriction on using classes as property values, in OWL DL and OWL Lite most properties cannot have classes as their values. We illustrate the direct approach for representing classes as property values in OWL-Full and RDF Schema. We present various alternative mechanisms for representing the required information in OWL DL and OWL Lite.”
• Defining N-ary Relations on the Semantic Web: Use With Individuals. ” In Semantic Web languages, such as RDF and OWL, a property is a binary relation; that is, it links two individuals or an individual and a value. How do we represent relations among more than two individuals? How do we represent properties of a relation, such as our certainty about it, severity or strength of a relation, relevance of a relation, and so on? The document presents ontology patterns for representing n-ary relations and discusses what users must consider when choosing these patterns.”
• Representing Specified Values in OWL: “value partitions” and “value sets”. “Modelling various descriptive “features” (also known variously as “qualities”, “attributes” or “modifiers”) is a frequent requirement when creating ontologies. For example: “size” may describe persons or other physical objects and be constrained to take the values “small”, “medium” or “large”; rank may describe military officers and restricted to a specific list of values depending on the military organisation. In OWL such descriptive features are modelled as properties whose range specifies the constraints on the values that the property can take on. This document describes two methods to represent such features and their specified values: 1) as partitions of classes; and 2) as enumerations of individuals.”

The standard view of the semantic web assumes that we will not have a single consensus ontology for a given domain, but many, each with its own base of users and applications. Thus it’s essential that we have good techniques and tools to translate information expressed in one collection of ontologies into another. One of the issues that we have not yet faced head on is that most of these mappings will probably be approximations. Here’s a good overview of the Bayesian approach to OWL ontology mapping being developed by Yun Peng and his students.

A Bayesian Methodology towards Automatic Ontology Mapping, Zhongli Ding, Yun Peng, Rong Pan, and Yang Yu, AAAI Workshop on Contexts and Ontologies, July 09, 2005.

This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling uncertainty in semantic web. The proposed method includes four components: 1) learning probabilities (priors about concepts, conditionals between subconcepts and superconcepts, and raw semantic similarities between concepts in two different ontologies) using Naive Bayes text classification technique, by explicitly associating a concept with a group of sample documents retrieved and selected automatically from World Wide Web (WWW); 2) representing in OWL the learned probability information concerning the entities and relations in given ontologies; 3) using the BayesOWL framework to automatically translate given ontologies into the Bayesian network (BN) structures and to construct the conditional probability tables (CPTs) of a BN from those learned priors or conditionals, with reasoning services within a single ontology supported by Bayesian inference; and 4) taking a set of learned initial raw similarities as input and finding new mappings between concepts from two different ontologies as an application of our for-malized BN mapping theory that is based on evidential reasoning across two BNs.

Some SPIRE folks and I have talked about using folksonomies to do for data sharing what they´ve done for picture and bookmark sharing. In particular, one thing that makes tagging sights like flickr and del.icio.us so powerful is that every user is free to add tags to already tagged resources. In other words, the metadata attached to a resource can evolve as the community gains new understandings of what the resource relates to. We´ve been talking for a while (mentioned it in at least one proposal) about developing a system that allows scientists to tag data according to what they find it useful for, and that attaches those tags to the data. But, in a sense, the Technorati-blogging infrastructure already provides such a system.

For example, I blogged a link to an NBII dataset, tagged it both “foodweb” and “habitat”, and now it shows up at technorati.com/tag/foodweb and technorati.com/tag/habitat. If anybody used this data, they could tag it according to what they use it for. For example, the habitat data could be tagged “colorado river toad”. All the tags of a dataset can be found by following the linkbacks. (flickr provides a more straightforward way of viewing all the tags associated with a picture.)

More generally … People have been contrasting folksonomies with the semantic web, as if they were in opposition to each other. In many cases, however, folksonomies can easily slip into the semantic web framework, playing the role currently played by taxonomies. any thoughts on this?

The Foundation for Intelligent Physical Agents (FIPA) was established almost ten years ago to develop software standards for heterogeneous and interacting agents and agent-based systems. It successfully developed and published a very good set of standards for agent communication and agent infrastructure. The last several years has seen a gradual decline in paying members, fewer technical people able to devote time and a general loss of momentum.

This fall the FIPA board decided to close down FIPA as a Swiss-based organization and find a sponsor to help maintain and develop the FIPA standards. The membership voted to follow this course and discussions were held with a number of candidate organizations. The IEEE Computer Society invited FIPA to become part of its family of standards committees and working groups. In March, the FIPA membership voted to join as the “FIPA Standards Committee”. This committee will be a self-organizing body with its own policies and procedures, dues structures, and bank account within the IEEE. The IEEE Computer Society will provide the umbrella organization, website maintenance, voting support, and all the other benefits that a large standards organization provides.

FIPA was an exciting experiment and perhaps a bit ahead of its time. I think that joining IEEE is a good decision and am optimistic that this will provide a new home for FIPA’s ideas and standards to evolve as new technologies appear and mature. The agents vision is still the right one, IMHO, and FIPA’s good work will be needed soon.

We will demonstrate Swoogle and Swangling at the 2005 Semantic Web for National Security (SWANS) conference. The concepts and features to be demostrated are all in the Swoogle tour. You can also see the Swoogle poster and the swangling poster that we will use.

Via SearchViews.
Vimeo has a similar interface to Flickr and works on collaborative content management.
Its currently in Beta and does not support account signups. But it does look cool.

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.