The SPARQL Query Language for RDF is back to the status of Candidate Recommendation. Some of you may know it already had this status on April 6, 2006. It was returned to being a Working Draft on October 4, 2006.

Swoogle - a search engine for semantic web documents (a.k.a. ontologies) - is now available in an improved 2007 version:

The biggest change is that Swoogle?s IR index is now updated incrementally, as new or modified Semantic Web documents are processed. When Swoogle processes an RDF document, it analyzes it to extract metadata, and then adds or updates the metadata in Swoogle?s database as well as (re-) indexes information about the document in Swoogle?s IR engine. Previously, these information in the database was updated as documents were found but the IR index was regenerated periodically in an off line batch process. Consequently, the two were not completely synchronized. They are now, at least on a daily basis.

Recently I discovered a nice survey on semantic web search engines. If you are interested in this topic (as I am) you should not miss it:

A Categorization Scheme for Semantic Web Search Engines (Esmaili & Abolhassani, 2006)

In addition check at the references on the Search on the Semantic Web page (of course there is some overlap - search on the semantic web is a young discipline ).

In my research on search in the Semantic Web, (semantic) similarity plays a crucial role. Today I discovered a great resource for semantic similarity: (Semantic) Similarity-Blog by Krzysztof Janowicz.

After a long series of beta releases Protégé 3.2 (final) was released on November 11!
Also a pre-release of Protégé 4.0 is available together with some screenshots of the new Protégé. Besides the good news of an all new version of Protégé, there are other great news: While we are excited about the proposed enhancements for Protégé 4, we will continue to support the Protégé 3.x series indefinitely as we have important clients and applications that depend on the 3.x implementation.

James Hendler explicitly and Tim Berners-Lee implicitly inform us about the Web Science Research Initiative (WSRI) formed by MIT and University of Southampton. The initiative aims at exploring web-related research issues. For a motivation on WSRI the interested reader is referred to Creating a Science of the Web (Berners-Lee et al. 2006) available on the WSRI site. Also very interesting: The page of the Web Science Workshop with pictures of some of the web science celebrities.

As Danny Ayers notes Chris Bizer and Richard Cyganiak yesterday announced a RDF version of DBLP, the popular computer science publication database. More info can be found on their D2R Server DBLP page.

Danny Ayers posted the link to The ultimate mashup — Web services and the semantic Web, Part 1: Use and combine Web services over at IBM’s developerWorks. I am allready curious about the other 5 parts of this tutorial series: In Part 6, the fun increases. At this point, you have a working application and the framework in place so that the system can use semantic reasoning to understand the services at its disposal. In this part, you give the user control, enabling him or her to map new services into the ontology and to pick and choose the data that is used for a custom mashup.

Recently I read about Semantic Radar over at Frédérick Giasson’s weblog. Semantic Radar is a plug-in for Firefox, notifying the user if it finds SIOC, FOAF or DOAP data referenced by the page he or she is currently viewing. When such data is detected a little icon will appear on the bottom right side of you browser, which lets you access the embedded data easily. I really like this little helper!

I previously blogged about a very comprehensive survey-paper on semantic annotation. Besides the survey the paper also lists seven requirements for semantic annotation systems in the context of a document centred knowledge management approach and reviews them in the light of the annotation tools surveyed. Here are the seven requirements from Uren et al. (2006):

• Requirement 1—standard formats: … Using standard formats is preferred, wherever possible, because the investment in marking up resources is considerable and standardization builds in future proofing because new tools, services, etc., which were not envisaged when the original semantic annotation was performed may be developed. Compliance with standards also frees companies from the constraints of proprietary formats when choosing knowledge management software. It is the activity of the W3C in developing and promoting international standards for the SemanticWeb that has convinced us that this route is worth following in knowledge management. Two types of standard are required, standards for describing ontologies such as the Web Ontology Language OWL and standards for annotations such as the W3C’s RDF annotation schema.
  Many of the reviewed tools already use W3C standards.
• Requirement 2—user centered/collaborative design: … Annotation can potentially become a bottleneck if it is done by knowledge workers with many demands on their time. Since few organizations have the capacity to employ professional annotators, it is crucial to provide knowledge workers with easy to use interfaces that simplify the annotation process and place it in the context of their everyday work. A good approach would be a single point of entry interface, so that the environment in which users annotate documents is integrated with the one in which they create, read, share and edit them. System design also needs to facilitate collaboration between users, which is a key facet of knowledge work with experts from different fields contributing to and reusing intelligent documents. …
  … More attention needs to be paid to build in or plug-in semantic annotation facilities in commonly used packages to encourage knowledge workers to view annotation as part of the authoring process not as an afterthought, and also to supporting annotation in collaborative environments, …
• Requirement 3—ontology support (multiple ontologies and evolution): … annotation tools need to be able to support multiple ontologies. For example, in a medical context, there may be one ontology for general metadata about a patient and other technical ontologies that deal with diagnosis and treatment. … In addition, systems will have to cope with changes made to ontologies over time, such as incorporating new classes or modifying existing ones. In this case, the problem is ensuring consistency between ontologies and annotations with respect to ontology changes. …
  … Ontology maintenance, which directly affects the maintenance of annotations, is poorly supported, or not supported at all, by the current generation of tools. This perhaps reflects the intended user groups; with the assumption being that knowledge workers will use existing ontologies rather than editing or creating them. … A genuinely integrated semantic annotation environment should give the user automatic support for ontology maintenance, for example, using text mining methods to suggest new classes as they emerge in documents and spotting inconsistencies between new and existing annotations. …
• Requirement 4—support of heterogeneous document formats: … Documents will be in many different formats including word processor files, spreadsheets, graphics files and complex mixtures of different formats. This presents a technical challenge rather than a research challenge, but dealing with multiple document formats is a prerequisite for integrating annotation into existing work practices.
• Requirement 5—document evolution (document and annotation consistency): Ontologies change sometimes but some documents change many times. … What should happen to the annotations on a document when it is revised, poses both technical and application specific questions. … Annotation environments need to help knowledge workers maintain appropriate annotations as documents change.
  The survey did not discover any concerted work on these lines.
• Requirement 6—annotation storage options: The Semantic Web model assumes that annotations will be stored separately from the original document, whereas the “word processor” model assumes that comments are stored as an integral part of the document, which can be viewed or not as the reader prefers. …
  … However, separate storage of annotations has advantages for KM. … It also makes it easy to produce different views of a document for users with different roles in an organization or different access rights, thus facilitating knowledge sharing and collaboration. We therefore argue that separate storage is the better model, even when extra overheads are required to maintain links between a document and its annotations.
• Requirement 7—automation: Another aspect of easing the knowledge acquisition bottleneck is the provision of facilities for automatic mark-up of document collections to facilitate the economical annotation of large document collections. To achieve this, the integration of knowledge extraction technologies into the annotation environment is vital. …
  Language technologies present usability challenges when deployed for knowledgeworkers since most are research tools or designed for use by specialists. … In addition to the usability challenges there are also research challenges, among which we have highlighted the extraction of relations as important for semantic annotation.