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Even though the Linking Open Data cloud is constantly growing, there is a serious lack of published data sets related to the domain of academic mathematics. At the same time, since most scholarly publications in mathematics are well-structured and conventional, it's promising to get their helpful detailed representation. The paper describes an approach to extracting and analyzing the structure of mathematical papers. We present the Mocassin ontology that is used by analysis algorithms and candoi:10.1145/1988688.1988713 dblp:conf/wims/SolovyevZ11 fatcat:d6lbshnslncpvi5j4pfnaj37iq