Semantic information retrieval for geoscience resources : results and analysis of an online questionnaire of current web search experiences

An online questionnaire “Semantic web searches for geoscience resources” was completed by 35 staff of British Geological Survey (BGS) between 28th July 2015 and 28th August 2015. The questionnaire was designed to better understand current web search habits, preferences, and the reception of semantic search features in order to inform PhD research into the use of domain ontologies for semantic information retrieval. The key findings were that relevance ranking is important in focussed searches that seeks the answer to a specific question, because 50% of people only look at the first 10 results. Relevance ranking is important but not so critical for broad reaching literature and data gathering searches because 88% of respondents would typically assess more than 10 results in this case. A large majority of respondents usually or sometimes had to perform multiple searches or construct advanced searches in order to include all relevant variations in terminology, and an optional feature in the search engine that expanded the search terms for them would be beneficial and desirable. All respondents reported that their search results were at some point, dominated by irrelevant result entries. Asked how a feature that disambiguates terms in search queries should be implemented, 81% will like to be able to specify intended context/meaning of search terms but only when such terms are ambiguous. The conclusion was that the collected responses, though a small sample, indicated vast support for the implementation of semantic search features to add narrower or equivalent terms to original search intent and to specify the context/meaning of ambiguous search terms, but the respondents preferred to be in control of whether or not those features were implemented on each search

The Impact of Imbalanced training Data on Local matching learning of ontologie

International audienceMatching learning corresponds to the combination of ontology matching and machine learning techniques. This strategy has gained increasing attention in recent years. However, state-of-the-art approaches implementing matching learning strategies are not well-tailored to deal with imbalanced training sets. In this paper, we address the problem of the imbalanced training sets and their impacts on the performance of the matching learning in the context of aligning biomedical ontologies. Our approach is applied to local matching learning, which is a technique used to divide a large ontology matching task into a set of distinct local sub-matching tasks. A local matching task is based on a local classifier built using its balanced local training set. Thus, local classifiers discover the alignment of the local sub-matching tasks. To validate our approach, we propose an experimental study to analyze the impact of applying conventional resampling techniques on the quality of the local matching learning