This topic will be discussed in the Webinar on 5 March 2009. Actually, there are two well known technical issues when reasoning with ontologies that contain hundreds of thousands of classes/subclasses and where change happens frequently.


The first problem, materializing type information, takes far too much time. In some triple stores, materialization takes almost as long as loading the data. Once an ontology changes, the entire materialization process has to start over.

The second problem, optimizing a SPARQL engine for a reasoning triple store, is more challenging than just using SPARQL as a retrieval language. In a non-reasoning SPARQL engine, optimizing is relatively straightforward, applying the right hash and sort joins once given the statistics of the database when it reorders appropriately. However, when SPARQL is used on top of a reasoner, suddenly additional considerations are required. In practice, you only know the statistics of each clause after you have done the reasoning.

This Webinar will discuss a new solution that mitigates or nearly solves both problems. We will discuss some indexing techniques that do not require materialization and we will cover how an ordinary backtracking technique can be very fast with the right reordering.

Register for this webinar at:

Oracle 10g Release 2 / Oracle 11g introduces the industry’s first open, scalable, secure platform to store RDF and OWL data. Based on a graph data model, RDF triples are persisted, indexed and queried, similar to other object-relational data types.


Oracle Jena adaptor software implements the well-known Jena Graph and Model APIs. (Jena is an Open Source framework developed by Hewlett-Packard and is available under a BSD-style license; see for details.) It extends the capabilities of Oracle semantic data management (Oracle 10gR2 RDF and Oracle 11gR1 RDF/OWL) with a set of easy-to-use Java APIs. Enhancements have been done to the server side to accommodate those APIs.


Application developers can now use the power of the Oracle 11g Database to design and develop a wide range of semantic-enhanced business applications. Application areas include:

  • Life Sciences: Biological pathway analysis, discovery and enhanced search.
  • Defence & Intelligence: Data and content integration, reasoning and inference.
  • Enterprise Application Integration: Data and systems integration, semantic enterprise integration and semantic web services.
  • CRM/ERP: Supply chain integration, sourcing optimization and customer service automation.


Oracle Semantic Technologies Software Documentation

[1] Oracle Semantic Technologies Technical Presentation (PDF)

[2] Oracle RDF Overview

[3] Oracle Database 11gInference Best Practices with RDFS/OWL

[4] Semantic data management on Windows XP and configure semantic web technology support in Oracle 11g.

Since the inception of the Semantic Web, the development of languages for modelling ontologies has been seen as a key task. The initial proposals focused on RDF and RDF Schema; however, these languages were soon found to be too limited in expressive power.


OWL Web Ontology Language became a W3C recommendation in February 2004. OWL is actually a family of three language variants (often called species) of increasing expressive power: OWL Lite, OWL DL, and OWL Full.


The standardization of OWL has sparked the development and/or adaption of a number of reasoners, including FacT++, Pellet, RACER, and HermiT, and ontology editors, including Protégé and Swoop.


Practical experience with OWL 1 has shown that OWL 1 DL, the most expressive but still decidable language of the OWL 1 family, lacks several constructs that are often necessary for modelling complex domains


Why OWL 2?

Although, or even perhaps because, OWL 1 has been successful, certain problems have been identified in its design. None of these problems are severe, but, taken together; they indicate a need for a revision of OWL 1.


One of the important limitations of OWL 1 is the lack of a suitable set of built-in datatypes; because OWL 1 relies on XML Schema (xsd) for the list of built-in datatypes. OWL 2 is a new version of OWL which considerably improves the datatypes. Apart from addressing acute problems with expressivity, a goal in the development of OWL 2 was to provide a robust platform for future development.


OWL 2 extends the W3C OWL Web Ontology Language with a small but useful set of features that have been requested by users, for which effective reasoning algorithms are now available, and those OWL tool developers are willing to support. The new features include extra syntactic sugar, additional property and qualified cardinality constructors, extended datatypes support, simple meta-modelling, and extended annotations.


Considerable progress has been achieved in the development of tool support for OWL 2. The new syntax is currently supported by the new version of the OWL API. The widely used Protégé system has recently been extended with support for the additional constructs provided by OWL 2. The commercial tool TopBraid composer also currently supports OWL 2. Support for OWL 2 has also been included into the FaCT++ and the Pellet systems.


Reference: OWL2: The Next Step for OWLand “OWL 2.0: W3C Working Draft 02 December 2008” .

2008 is just about to end, but looking back clearly reveals: it was an exciting year for the Semantic Web. Remember Yahoo’s BOSS strategy and Search Monkey initiave, Microsoft’s struggle over semantic search, the release of Calais, Cuil, Zemanta or Twine, the silent decline of Web 2.0 and the sudden appearance of a “Gimme-A-Break” Web 3.0 …

And what is to expect for 2009? Media industries and advertisers will jump on the Semantic Web, we will see a lot more of open data bubbles on the web, and smartly designed applications will bring “semantics to the home”.

The Semantic Web Challenge offers participants the chance to show the best of the Semantic Web. The Challenge thus serves several purposes:

  • Helps us illustrate to society what the Semantic Web can provide
  • Gives researchers an opportunity to showcase their work and compare it to others
  • Stimulates current research to a higher final goal by showing the state-of-the-art every year


What is the goal?

The overall objective of the challenge is to apply Semantic Web techniques in building online end-user applications that integrate, combine and deduce information needed to assist users in performing tasks.

The Semantic Web Challenge Advisory board also defines an additional goal every year based on the development of the Challenge. Find out more about the upcoming Challenge.


Semantic Web Challenge 2008

Here is the list of accepted submissions for “Semantic Web Challenge 2008” which will be presented at ISWC 2008. You will find more information about these projects at

We see the Semantic Web as an enabler of the Relationship Web. What metadata, annotation, and labeling are to the Semantic Web, relationships of all forms (implicit, explicit, and formal) are to the Relationship Web.  The primary goal of the Semantic Web has been described (by Tim Berners-Lee and many others) as integration of data or labeling of Web resources for more precise exploitation by both machines and humans.   At the next level, the Relationship Web organizes Web resources for analysis that goes beyond integration to trailblazing, leading to deeper insights and better decision making.


Relationship Web takes you away from “which document” could have information I need, to “what’s in the resources” that gives me the insight and knowledge I need for decision making.


For more information, See the article: “Relationship Web Blazing Semantic Trails between Web Resources“.

The Semantic Deep Web integrates Semantic Web components with the employment of ontology-aware browsers to squeeze information out of the Deep Web, which is nonindexable, invisible, and concealed online content that is only accessible via Web services or Web-form interfaces, write New Jersey Institute of Technology professor James Geller and colleagues. “The primary goals of the Semantic Deep Web are to access Deep Web data through various Web technologies and to realize the Semantic Web’s vision by enriching ontologies using this data,” the authors note. To access the Deep Web with Semantic Web technologies, the Semantic Deep Web utilizes ontology plug-in search, a method for enriching a domain ontology with Deep Web data semantics so that it can be used to refine user search queries processed by a conventional search. Another key Semantic Deep Web process is Deep Web service annotation, in which Deep Web services are annotated with Deep Web data semantics so that they can be searched by a Semantic Web search engine. It is simpler from a semantic perspective to obtain ontologies from Deep Web data sources, especially well-structured relational back-end databases, than from unstructured natural-language text documents. Activities Geller lists as necessary for fusing Semantic Web and Deep Web technologies together include the development of ontology-aware, high-quality Web search engines; construction of large ontologies from Deep Web sites, beginning with all e-commerce subdomains; achieving acceptance of an “open source attitude” in the e-commerce space to simplify the building of Deep Web ontologies by accessing securely locked data sources; creation of libraries of semantic crawlers designed to extract back-end database information; and assembly of comprehensive index structures for Deep Web sites.

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Conventional information retrieval techniques are document-centric search paradigm. Users are required to investigate an document or lists of documents in order to obtain an answer.
Semantic serach is an object, entity or knowledge centric approach based on the semantic web technologies.

It aims to find precise and abundant information of the objects under consideration and their related objects. Semantic search technology enables accurate retrieval of information via concept/meaning match. It is very effective, and perhaps the only method, in application to credible and dynamic content. Because most of the credible and dynamic content are statistically flat (infertile) for popularity algorithms to work effectively beyond common queries.

hakia (  is a general purpose “semantic” search engine, dedicated to quality search experience.

For more information, you may refer to An Overview of Semantic Search Engines.

The Resource Description Framework (RDF) is a W3C Recommendation for the formulation of metadata on the World Wide Web. RDF Schema (RDFS) extends this standard with the means to specify domain vocabulary and object structures. These techniques will enable the enrichment of the Web with machine-processable semantics, thus giving rise to what has been dubbed the Semantic Web.


The basic building block in RDF is an subject-predicate-object triple, commonly written as P(S;O). That is, a subject S has an predicate (or property) P with value O. Another way to think of this relationship is as a labeled edge between two nodes: [S] –P–>[O].


The RDF data model provides no mechanisms for declaring vocabulary that is to be used. RDF Schema is a mechanism that lets developers define a particular vocabulary for RDF data (such as the predicate hasWritten) and specify the kinds of objects to which predicates can be applied (such as the class Writer). RDFS does this by pre-specifying some terminology, such as Class, subClassOf and Property, which can then be used in application-specific schemata.


RDFa was first proposed by Mark Birbeck in the form of a W3C note entitled XHTML and RDF, which was then presented to the Semantic Web Interest Group at the W3C’s 2004 Technical Plenary. RDF/A is a set of attributes used to embed RDF in XHTML. 


For more information, watch this video :

Moreover, have a look to the  presentation : RDFa – Bridging the Web of Documents and the Web of Data” that will be presented in ISWC2008.

“The Semantic Web is an extension of the current web in which information is given a well-defined meaning, better enabling computers and people to work in cooperation” [Berners-Lee Definition]. Today’s Web enriched by a formal semantics in form of ontologies that captures the meaning of pages and links in a machine-understandable form. The main idea of the Semantic Web is to enrich the current Web by machine-processable information in order to allow for semantic-based tools supporting the human user. The architecture of semantic web offers the opportunity to develop techniques for determining importance and relevance of Web contents.

Semantic Web Technology is a weblog that try to disseminate approaches, research challenge and new events in the semantic web.

Beside this, we would like to introduce SemWebTec discussion yahoo-group ( and invite everyone to join to this group. The objective of this scientific discussion group is to prepare a Q/A media for better understanding and improve our knowledge.