Large-scale, parallel automatic patent annotation
Proceedings of the 1st ACM workshop on Patent information retrieval
Semantically Conceptualizing and Annotating Tables
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
Fuzzy Annotation of Web Data Tables Driven by a Domain Ontology
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
XLWrap --- Querying and Integrating Arbitrary Spreadsheets with SPARQL
ISWC '09 Proceedings of the 8th International Semantic Web Conference
An ontological and terminological resource for n-ary relation annotation in web data tables
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
Addressing the long tail in empirical research data management
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Entity discovery and annotation in tables
Proceedings of the 16th International Conference on Extending Database Technology
Scalable column concept determination for web tables using large knowledge bases
Proceedings of the VLDB Endowment
Ontology of units of measure and related concepts
Semantic Web - Linked Data for science and education
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Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format difficult. For example, the table header cell "f (Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.