Abstract:
Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search, recommendation or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus or in the navigation and thus do not directly model human intuition.
In this talk, we show the utilisation of metric learning to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. Our approach is based on knowledge that emergent as semantic information in Social Media systems and is embedded in the user's content or its navigational traces. We argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost as a first step to improve the extraction of the hidden semantic. We present results on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Second, human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We will show that our method can significantly improve semantic relatedness measures by learning from the additional explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for researchers and practitioners of Semantic Web and show the power of Machine Learning methods for extracting semantics.
Biodata:
Andreas Hotho is a professor at the University of Würzburg and the head of the DMIR group. In this context, he is directing the BibSonomy project spanning the L3S Research Center located in Hanover, the KDE group of the University of Kassel and the DMIR group. Prior, he was a senior researcher at the University of Kassel. He started his research at the AIFB Institute at the University of Karlsruhe where he was working on text mining, ontology learning and semantic web related topics. Currently, he is working in the area of data science, data mining, semantic web mining and social media analysis.
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