@inproceedings{WiegandRothKlakow2019, author = {Michael Wiegand and Benjamin Roth and Dietrich Klakow}, title = {Data-driven Knowledge Extraction for the Food Domain}, series = {Proceedings of the 11th Conference on Natural Language Processing (KONVENS 2012). Empirical Methods in Natural Language Processing, September 19-21, 2012, Vienna, Austria}, editor = {Jeremy Jancsary}, publisher = {{\"O}sterreichische Gesellschaft f{\"u}r Artificial Intelligence}, address = {Wien}, isbn = {3-85027-005-X}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84529}, pages = {21 -- 29}, year = {2019}, abstract = {In this paper, we examine methods to automatically extract domain-specific knowledge from the food domain from unlabeled natural language text. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. We also examine a combination of extraction methods and also consider relationships between different relation types. The extraction methods are applied both on a domain-specific corpus and the domain-independent factual knowledge base Wikipedia. Moreover, we examine an open-domain lexical ontology for suitability.}, language = {en} }