Does Term Expansion Matter for the Retrieval of Biodiversity Data?

ABSTRACT While term expansion techniques are well investigated for many domains, semantic enrichment of keyword queries for the retrieval of scientific datasets is still paid little attention to. In particular, a systematic analysis of which kind of semantically related concepts lead to the most relevant results is missing. Based on query expansion techniques, we semantically enriched search queries provided by biodiversity researchers to answer specific research questions. We applied them to a system indexing over 92,856 biological metadata files harvested from GFBio -the German Federation for Biological Data. We compared the outcome with the original keyword-based query. The result reveals that enriched keywords deliver a larger number of relevant datasets and that datasets retrieved based on keywords and their synonyms were judged more relevant. Query expansion with other related concepts returned a mixed picture

Evaluating semantic search tools using the SEALS platform

In common with many state of the art semantic technologies, there is a lack of comprehensive, established evaluation mechanisms for semantic search tools. In this paper, we describe a new evaluation and benchmarking approach for semantic search tools using the infrastructure under development within the SEALS initiative. To our knowledge, it is the first effort to present a comprehensive evaluation methodology for semantic search tools. The paper describes the evaluation methodology including our two-phase approach in which tools are evaluated both in a fully automated fashion as well as within a user-based study. We also present and discuss preliminary results from the first SEALS evaluation campaign together with a discussion of some of the key findings

PowerAqua: Open Question Answering on the Semantic Web

With the rapid growth of semantic information in the Web, the processes of searching and querying these very large amounts of heterogeneous content have become increasingly challenging. This research tackles the problem of supporting users in querying and exploring information across multiple and heterogeneous Semantic Web (SW) sources. A review of literature on ontology-based Question Answering reveals the limitations of existing technology. Our approach is based on providing a natural language Question Answering interface for the SW, PowerAqua. The realization of PowerAqua represents a considerable advance with respect to other systems, which restrict their scope to an ontology-specific or homogeneous fraction of the publicly available SW content. To our knowledge, PowerAqua is the only system that is able to take advantage of the semantic data available on the Web to interpret and answer user queries posed in natural language. In particular, PowerAqua is uniquely able to answer queries by combining and aggregating information, which can be distributed across heterogeneous semantic resources. Here, we provide a complete overview of our work on PowerAqua, including: the research challenges it addresses; its architecture; the techniques we have realised to map queries to semantic data, to integrate partial answers drawn from different semantic resources and to rank alternative answers; and the evaluation studies we have performed, to assess the performance of PowerAqua. We believe our experiences can be extrapolated to a variety of end-user applications that wish to open up to large scale and heterogeneous structured datasets, to be able to exploit effectively what possibly is the greatest wealth of data in the history of Artificial Intelligence