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Found 4 papers, 1 papers with code
AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model
In this paper we present AMALGAM, a matching approach to fairify tabular data with the use of a knowledge graph.
Evaluation Dataset and Methodology for Extracting Application-Specific Taxonomies from the Wikipedia Knowledge Graph
In this work, we propose an iterative methodology to extract an application-specific gold standard dataset from a knowledge graph and an evaluation framework to comparatively assess the quality of noisy automatically extracted taxonomies.
IAM at CLEF eHealth 2018: Concept Annotation and Coding in French Death Certificates
In this paper, we describe the approach and results for our participation in the task 1 (multilingual information extraction) of the CLEF eHealth 2018 challenge.
Large scale biomedical texts classification: a kNN and an ESA-based approaches
Furthermore, we investigate if the results of this method could be useful as a complementary feature of our kNN-based approach. ResultsExperimental evaluations performed on large standard annotated datasets, provided by the BioASQ organizers, show that the kNN-based method with the Random Forest learning algorithm achieves good performances compared with the current state-of-the-art methods, reaching a competitive f-measure of 0. 55% while the ESA-based approach surprisingly yielded reserved results. ConclusionsWe have proposed simple classification methods suitable to annotate textual documents using only partial information.
