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Auflistung nach Autor:in "Sturm, Christoph"

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  • Konferenzbeitrag
    SportsTables: A new Corpus for Semantic Type Detection
    (BTW 2023, 2023) Langenecker, Sven; Sturm, Christoph; Schalles, Christian; Binnig, Carsten
    Table corpora such as VizNet or TURL which contain annotated semantic types per column are important to build machine learning models for the task of automatic semantic type detection. However, there is a huge discrepancy between corpora that are used for training and testing since real-world data lakes contain a huge fraction of numerical data which are not present in existing corpora. Hence, in this paper, we introduce a new corpus that contains a much higher proportion of numerical columns than existing corpora. To reflect the distribution in real-world data lakes, our corpus SportsTables has on average approx. 86% numerical columns, posing new challenges to existing semantic type detection models which have mainly targeted non-numerical columns so far. To demonstrate this effect, we show the results of a first study using a state-of-the-art approach for semantic type detection on our new corpus and demonstrate significant performance differences in predicting semantic types for textual and numerical data.
  • Textdokument
    Towards Learned Metadata Extraction for Data Lakes
    (BTW 2021, 2021) Langenecker, Sven; Sturm, Christoph; Schalles, Christian; Binnig, Carsten
    An important task for enabling the efficient exploration of available data in a data lake is to annotate semantic type information to the available data sources. In order to reduce the manual overhead of annotation, learned approaches for automatic metadata extraction on structured data sources have been proposed recently. While initial results of these learned approaches seem promising, it is still not clear how well these approaches can generalize to new unseen data in real-world data lakes. In this paper, we aim to tackle this question and as a first contribution show the result of a study when applying Sato -a recent approach based on deep learning -to a real-world data set. In our study we show that Sato is only able to extract semantic data types for about 10% of the columns of the real-world data set. These results show the general limitation of deep learning approaches which often provide near-perfect performance on available training and testing data but fail in real settings since training data and real data often strongly vary. Hence, as a second contribution we propose a new direction of using weak supervision and present results of an initial prototype we built to generate labeled training data with low manual efforts to improve the performance of learned semantic type extraction approaches on new unseen data sets.
  • Konferenzbeitrag
    Unified querying of ontology languages with the SIRUP ontology query API
    (Datenbanksysteme in Business, Technologie und Web, 11. Fachtagung des GIFachbereichs “Datenbanken und Informationssysteme” (DBIS), 2005) Ziegler, Patrick; Sturm, Christoph; Dittrich, Klaus R.
    Ontology languages to represent ontologies exist in large numbers, and users who want to access or reuse ontologies can often be confronted with a language they do not know. Therefore, due to their great number, ontology languages are nowadays themselves a source of heterogeneity. In this paper, we present the SIRUP Ontology Query API (SOQA) that was developed for the SIRUP approach to semantic data integration [ZD04b]. SOQA is an ontology languageand platform-independent API for query access to ontological metadata and data that can be represented in a variety of ontology languages. Based on SOQA, we provide SOQA-QL, an SQL-like query language that supports declarative queries against ontological metadata and data, and the SOQA Browser, a tool to graphically inspect all ontology information that can be accessed through SOQA.