## Toward a New Generation of Semantic Web Applications > [!Cite]- > Aquin, M. d’, E. Motta, M. Sabou, et al. “Toward a New Generation of Semantic Web Applications.” _IEEE Intelligent Systems_ 23, no. 3 (2008): 20–28. [https://doi.org/10.1109/MIS.2008.54](https://doi.org/10.1109/MIS.2008.54). > > [link](http://ieeexplore.ieee.org/document/4525139/) [online](http://zotero.org/users/17587716/items/QFVUJZTQ) [local](zotero://select/library/items/QFVUJZTQ) [pdf](file://C:\Users\erikt\Zotero\storage\XYK4MMLQ\d'Aquin%20et%20al.%20-%202008%20-%20Toward%20a%20New%20Generation%20of%20Semantic%20Web%20Applications.pdf) ## Notes %% begin notes %% %% end notes %% %% begin annotations %% ### Imported: 2025-10-13 6:43 am The Semantic Web’s embryonic nature is reflected in its existing applications. Most of these applications tend to produce and consume their own data, much like traditional knowledge-based applications, rather than actually exploiting the Semantic Web as a large-scale information source. Although corporate Semantic Webs often provide perfectly adequate solutions to a company’s needs, they actually fall short of fully exploiting the Semantic Web’s exciting potential as a largescale source of background knowledge. we began an ambitious research program two years ago dubbed “Next-Generation Semantic Web Applications.” Our project’s objective was to experiment with a new class of applications that would go beyond classic corporate Semantic Webs and intelligently exploit the Semantic Web as a large-scale, heterogeneous semantic resource. The fundamental problem of understanding intelligence is not the identification of a few powerful techniques, but rather the question of how to represent large amounts of knowledge in a fashion that permits their effective use.2 A few years later, Brian Smith precisely formulated this knowledge-based paradigm when he defined the knowledge-representation hypothesis: Any mechanically embodied intelligent process will be comprised of structural ingredients that we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and independent of such external semantic attribution, play a formal but causal and essential role in engendering the behaviour that manifests that knowledge.3 Hence, the essential element of AI’s knowledge-based paradigm is this causal relationship between a system’s explicit knowledge representation and its (intelligent) behavior. Unfortunately, the paradigm has a key problem in its so-called knowledge acquisition bottleneck.4 This KA bottleneck concerns the difficulty of acquiring, representing, and maintaining an intelligent system’s knowledge base. That is, as we move from classic KBSs to Semantic Web applications, intelligence becomes a side effect of scale, rather than of sophisticated logical reasoning. The most influential example is probably Swoogle (http:// swoogle.umbc.edu), a search engine that crawls and indexes online Semantic Web documents. Swoogle claims to adopt a Web view on the Semantic Web, and, indeed, most of its techniques are inspired by traditional Web search engines. Relying on such well-studied techniques offers a range of advantages, but it also has a major limitation: by largely ignoring the semantic particularities of the indexed data, Swoogle falls short of offering the functionalities required from a truly Semantic Web gateway. They provide only weak access to semantic information, because they don’t consider the accessed document’s semantic content. They typically pay limited attention to semantic relations between ontologies. Swoogle, for example, considers only those relations that are explicitly stated (such as import). This is a serious limitation; as semantic resources, ontologies can be compared and related to each other through semantic relations (they might, for example, be versions of each other, mutually incompatible, and so on). This is particularly important for semantic applications that must exploit several, interrelated ontologies. In looking at results from existing Semantic Web search engines, it appears that they don’t consider even the simplest (syntactic) notion of duplication (or copy), because the same documents often appear, at different ranks, several times in the results. Motivated by the needs of next-generation applications, we developed the Watson Semantic Web gateway (http://watson.kmi. open.ac.uk). Watson offers a single access point to online semantic information and provides efficient services to support application developers in exploiting this voluminous distributed and heterogeneous data. Although superficially similar to existing Semantic Web search engines, Watson overcomes their limitations by providing support for finding, selecting, exploiting, and combining online semantic resources. %% end annotations %% %% Import Date: 2025-10-13T06:43:26.514-06:00 %%