PubMed · 13143563
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A SAUVAGEOT. [Concerning semantic changes].. https://pubmed.ncbi.nlm.nih.gov/13143563/
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MOTIVATION: Hypothesis generation is a fundamental problem in biomedical text mining that aims to generate ideas that are new, interesting, and plausible by discovering unexplored links between biomedical concepts. Despite significant advances made by existing approaches, they do not fully leverage the evolutionary properties of biomedical concepts. This is limiting because scientific knowledge continually evolves over time, with new facts being added and old ones becoming obsolete. Thus, it is crucial to capture the evolutionary properties of biomedical concepts from multiple perspectives (e.g. spatial, temporal, and semantic) to generate hypotheses that reflect the up-to-date information landscape of the biomedical domain. RESULTS: We introduce a novel framework, ConceptDrift, that models the hypothesis generation task as a sequence of temporal graphlets and simultaneously encodes spatial, temporal, and semantic change. Unlike existing approaches that treat these dimensions independently, ConceptDrift is the first to provide a holistic understanding of concept evolution by integrating them into a unified framework. Grounded in the theories of the Distributional Hypothesis and Conceptual Change, our method adapts these principles to the unique challenges of large-scale biomedical literature. We conduct extensive experiments across multiple datasets and demonstrate that ConceptDrift consistently outperforms state-of-the-art baselines in generating accurate and meaningful hypotheses. Our framework shows immediate practical benefits for web-based literature mining tools in life sciences and biomedicine, offering more robust and predictive feature representations. AVAILABILITY AND IMPLEMENTATION: https://github.com/amir-hassan25/ConceptDrift (DOI: 10.6084/m9.figshare.29975476).
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The Unified Medical Language System integrates about 800,000 concepts from 99 biomedical terminologies. Each concept is assigned to at least one semantic type of the Semantic Network. During the integration, it is unavoidable that some classification errors and inconsistencies will be introduced. In this paper, we present an auditing technique to find such errors and inconsistencies. Our technique is based on an expert reviewing the pure intersections of meta-semantic types of the metaschema, a compact abstract view of the Semantic Network. Results regarding the pure intersections are reported. The analysis results for pure intersections with 1 to 6 concepts are presented. Various kinds of errors are identified.