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Eng."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>State-of-the-art approaches in the field of neural embedding models (NEMs) enable progress in the automatic extraction and prediction of semantic relations between important entities like active substances, diseases, and genes. In particular, the <jats:italic>prediction property<\/jats:italic> is making them valuable for important research-related tasks such as hypothesis generation and drug repositioning. A core challenge in the biomedical domain is to have <jats:italic>interpretable<\/jats:italic> semantics from NEMs that can distinguish, for instance, between the following two situations: (a) drug x <jats:italic>induces<\/jats:italic> disease y and (b) drug x <jats:italic>treats<\/jats:italic> disease y. However, NEMs alone cannot distinguish between associations such as treats or induces. Is it possible to develop a model to learn a <jats:italic>latent representation<\/jats:italic> from the NEMs capable of such disambiguation? To what extent do we need domain knowledge to succeed in the task? In this paper, we answer both questions and show that our proposed approach not only succeeds in the <jats:italic>disambiguation<\/jats:italic> task but also advances current growing research efforts to find real predictions using a sophisticated retrospective analysis.\nFurthermore, we investigate which type of associations is generally better contextualized and therefore probably has a stronger influence in our disambiguation task. In this context, we present an approach to extract an interpretable <jats:italic>latent semantic subspace<\/jats:italic> from the original embedding space in which <jats:italic>therapeutic<\/jats:italic> drug\u2013disease associations are more likely\n.<\/jats:p>","DOI":"10.1007\/s41019-020-00140-2","type":"journal-article","created":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T10:02:55Z","timestamp":1598263375000},"page":"333-345","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Exploiting Latent Semantic Subspaces to Derive Associations for Specific Pharmaceutical Semantics"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8733-2037","authenticated-orcid":false,"given":"Janus","family":"Wawrzinek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Mar\u00eda Gonz\u00e1lez","family":"Pinto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Wiehr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wolf-Tilo","family":"Balke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,24]]},"reference":[{"issue":"3","key":"140_CR1","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1007\/s11192-010-0202-z","volume":"84","author":"PO Larsen","year":"2010","unstructured":"Larsen PO, Von Ins M (2010) The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. 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