Semantic search promises to produce precise answers to user queries by taking advantage of the availability of explicit semantics of information in the context of the semantic web. Existing tools have been primarily designed to enhance the performance of traditional search technologies but with little support for naive users, i.e., ordinary end users who are not necessarily familiar with domain specific semantic data, ontologies, or SQL-like query languages. This paper presents SemSearch, a search engine, which pays special attention to this issue by hiding the complexity of semantic search from end users and making it easy to use and effective. In contrast with existing semantic-based keyword search engines which typically compromise their capability of handling complex user queries in order to overcome the problem of knowledge overhead, SemSearch not only overcomes the problem of knowledge overhead but also supports complex queries. Further, SemSearch provides comprehensive means to produce precise answers that on the one hand satisfy user queries and on the other hand are self-explanatory and understandable by end users. A prototype of the search engine has been implemented and applied in the semantic web portal of our lab. An initial evaluation shows promising results.

The goal of semantic search is to improve on traditional search methods by exploiting the semantic metadata. In this paper, we argue that supporting iterative and exploratory search modes is important to the usability of all search systems. We also identify the types of semantic queries the users need to make, the issues concerning the search environment and the problems that are intrinsic to semantic search in particular. We then review the four modes of user interaction in existing semantic search systems, namely keyword-based, form-based, view-based and natural language-based systems. Future development should focus on multimodal search systems, which exploit the advantages of more than one mode of interaction, and on developing the search systems that can search heterogeneous semantic metadata on the open semantic Web.

Because poor quality semantic metadata can destroy the effectiveness of semantic web technology by hampering applications from producing accurate results, it is important to have frameworks that support their evaluation. However, there is no such framework developed to date. In this context, we proposed i) an evaluation reference model, SemRef, which sketches some fundamental principles for evaluating semantic metadata, and ii) an evaluation framework, SemEval, which provides a set of instruments to support the detection of quality problems and the collection of quality metrics for these problems. A preliminary case study of SemEval shows encouraging results.

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Abstract. Because metadata that underlies semantic web applications is gathered from distributed and heterogeneous data sources, it is important to ensure its quality (i.e., reduce duplicates, spelling errors, ambiguities). However, current infrastructures that acquire and integrate semantic data have only marginally addressed the issue of metadata quality. In this paper we present our metadata acquisition infrastructure, ASDI, which pays special attention to ensuring that high quality metadata is derived. Central to the architecture of ASDI is a verification engine that relies on several semantic web tools to check the quality of the derived data. We tested our prototype in the context of building a semantic web portal for our lab, KMi. An experimental evaluation comparing the automatically extracted data against manual annotations indicates that the verification engine enhances the quality of the extracted semantic metadata.