Armadillo: Integrating Knowledge for the Semantic Web

Chapman, Mr. Sam and Norton, Mr. Barry and Ciravegna, Prof. Fabio (2005) Armadillo: Integrating Knowledge for the Semantic Web. Machine Learning for the Semantic Web Dagstuhl Seminar 05071, Dagstuhl, DE.

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The Semantic Web, SW, needs semantically-based structured content to both enable better document retrieval and empower semantically-aware agents. One prerequisite for the SW is the widespread adop- tion of such structured knowledge, so without a uni- versal acceptance other automated methods need to be employed to generate structured content from the existing unstructured web. Most of the current tech- nologies available for creating structured content is based on static human centred annotation, very of- ten completely manual, of documents. Manual annotation is time-consuming and can in- troduce noise, being incom- plete or incorrect, hence decreasing the quality of the information. For these reasons, we believe that the SW needs automatic methods for annotating con- tent. Automatic annotation services such as Sem- Tag and Armadillo intend to solve this problem by automatically providing SW content. In this paper we describe the Armadillo approach to automatic annotation and detail the methods em- ployed internally for integrating and ensuring consis- tency of elicited knowledge. Armadillo is a tool for extracting and integrating information from large repositories (e.g. the Web) developed at Sheffield. The methodology employed for validating and inte- grating the information is a series of weak eviden- tial similarity tests, implemented through a library of String Metrics. The paper focuses on presenting the Armadillo tool and details a use case, relating the methodologies used.

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