We present our work on developing a software platform for mining mathematical scholarly papers to obtain a Linked Data representation. Currently, the Linking Open Data (LOD) cloud lacks up-to-date and detailed information on professional level mathematics. To our mind, the main reason for that is the absence of appropriate tools that could analyze the underlying semantics in mathematical papers and effectively build their consolidated representation. We have developed a holistic approach to analysis of mathematical documents, including ontology based extraction, conversion of the article body as well as its metadata into RDF, integration with some existing LOD data sets, and semantic search. We argue that the platform may be helpful for enriching user experience on modern online scientific collections.
Abstract. Google search, having become familiar by now, has a significant disadvantage -low precision. The terms occurring in the texts are an important source of additional information available for mathematical collections. The use of terms can help the precision of search improvement, since search queries are composed of terms. The article presents the approach to implementation of this idea. We have developed a prototype of the search system with application of mathematical terms. The results of its testing are delivered in a collection of articles taken from mathematical journals.
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