Combining Semantic Web Search with the Power of Inductive Reasoning

With the introduction of the Semantic Web as a future substitute of the Web, the key task for the Web, namely, Web search, is evolving towards some novel form of Semantic Web search. A very promising recent approach to Semantic Web search is based on combining standard Web pages and search queries with ontological background knowledge, and using standard Web search engines as the main inference motor of Semantic Web search. In this paper, we continue this line of research. First, we further explore the completeness of the above Semantic web search. Second, and as the central contribution of this paper, we propose to further enhance this approach by the use of inductive reasoning techniques. This increases the robustness of Semantic Web search, as it adds the important ability to handle inconsistencies, noise, and incompleteness, which are all very likely to occur in distributed and heterogeneous environments such as the Web. In particular, inductive reasoning allows to infer (from training individuals) new knowledge, which is not logically deducible. We also report on a prototype implementation of the new approach based on inductive reasoning and its experimental evaluations.