Prof. Dr. Harald Sack
- Information Service Engineering Lecture for SoSe 2020 on Youtube
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Harald Sack is Vice President Information Service Engineering at FIZ Karlsruhe – Leibniz Institute for Information Infrastructure and Professor for Information Service Engineering at Karlsruhe Institute of Technology (KIT), Institute of Applied Informatics and Formal Description Methods (AIFB) with the research group “Information Service Engineering”.. After graduating in computer science at the University of the Federal Forces Munich Campus in 1990, he worked as systems/network engineer and project manager in the signal intelligence corps of the German federal forces from 1990–1997. In 1997 he became an associated member of the graduate program ‘mathematical optimization’ at the University of Trier, where he obtained a PhD in Computer Science in 2002. From 2002–2009 worked as PostDoc at the Friedrich-Schiller-University in Jena. From 2009 - 2016 he worked as Senior Researcher and head of the research group 'semantic technologies’ at the Hasso Plattner-Institute for IT-Systems Engineering (HPI) at the University of Potsdam.
He is charter member and general secretary of the 2008 founded German IPv6 Council. Harald Sack has published more than 130 papers in international journals and conferences including three standard textbooks on networking technologies. He is co-founder of yovisto GmbH (www.yovisto.com).
In order to develop and provide new information services, the research department Information Service Engineering investigates models and methods for efficient semantic indexing, aggregation, linking, and retrieval of comprehensive heterogeneous and distributed data sources. To this end, both statistic and linguistic analysis methods (Natural Language Processing) as well as machine learning in combination with symbolic logic and interference mechanisms are applied.
The activities focus on:
- Automatic analyses (text and multi-media) with special emphasis on high efficiency and result quality. Besides fundamental new approaches, a combination of already existing methods (e.g., linguistic methods) are persued for text mining as well as for machine learning based classification of images and videos. Semantic analyses based on the results of raw data analysis in combination with existing metadata in order to sustainably index and automatically re-use document content, with a focus on accuracy and fault tolerance. This encompasses the following relevant sub-disciplines: Integration of heterogeneous meta data and content indexing via Named Entity Recognition, Named Entity Linking and Common Entity Linking
- Efficient semantic annotation of multi-media documents dependent on the document modality with automated user assistance (semi-automated annotation) as well as context-sensitive visualization and re-use of user generated annotations.
- Synchronization and integration of distributed heterogeneous knowledge bases with respect to provenance, reliability, diversity, multilingualism, and topicality (real-time).
- Semantic search, i.e., based on the results achieved via semantic analysis or after semantic annotation, the obtained semantic information is used to improve the retrieval process with respect to the completeness and accuracy of the search results.
- Explorative search and intelligent recommendation systems. There is a smooth transition from semantic search to explorative search and recommendation systems. While with semantic search results are generated based on semantic similarity, explorative search considers further relationships among documents and their meaningful content elements.
- Semantic Technologies, Semantic Search, Ontology Engineering, Natural Language Processing, Knowledge Discovery, Data Mining, Artificial Intelligence, Semantic Web