Two papers accepted at German Conference on Artificial Intelligence

Two papers - "A Robust Number Parser based on Conditional Random Fields" by Heiko Paulheim and "One Knowledge Graph to Rule them All?" by Daniel Ringler and Heiko Paulheim - have been accepted at the 40th German Conference on Artificial Intelligence (KI 2017).

A Robust Number Parser based on Conditional Random Fields

Abstract: When processing information from unstructured sources, numbers  have  to  be  parsed  in  many  cases  to  do  useful  reasoning  on  that information. However, since numbers can be expressed in different ways, a robust number parser that can cope with number representations in different shapes is required in those cases. In this paper, we show how to train such a parser based on Conditional Random Fields. As training data, we use pairs of Wikipedia infobox entries and numbers from public knowledge graphs. We show that it is possible to parse numbers at an accuracy of more than 90%.

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One Knowledge Graph to Rule them All? Analyzing the Di fferences between DBpedia, YAGO, Wikidata & co.

Abstract: Public Knowledge Graphs (KGs) on the Web are considered a valuable asset for developing intelligent applications. They contain general knowledge which can be used, e.g., for improving data analytics tools, text processing pipelines, or recommender systems. While the large players, e.g., DBpedia, YAGO, or Wikidata, are often considered similar in nature and coverage, there are, in fact, quite a few di fferences. In this paper, we quantify those di fferences, and identify the overlapping and the complementary parts of public KGs. From those considerations, we can conclude that the KGs are hardly interchangeable, and that each of them has its strenghts and weaknesses when it comes to applications in di fferent domains.

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