Summary of the paper
| Title | The Weltmodell: A Data-Driven Commonsense Knowledge Base |
|---|---|
| Authors | Alan Akbik and Thilo Michael |
| Abstract | We present the Weltmodell, a commonsense knowledge base that was automatically generated from aggregated dependency parse fragments gathered from over 3.5 million English language books. We leverage the magnitude and diversity of this dataset to arrive at close to ten million distinct N-ary commonsense facts using techniques from open-domain Information Extraction (IE). Furthermore, we compute a range of measures of association and distributional similarity on this data. We present the results of our efforts using a browsable web demonstrator and publicly release all generated data for use and discussion by the research community. In this paper, we give an overview of our knowledge acquisition method and representation model, and present our web demonstrator. |
| Topics | Knowledge Discovery/Representation, Information Extraction, Information Retrieval |
Full paper  |
The Weltmodell: A Data-Driven Commonsense Knowledge Base |
| Bibtex | @InProceedings{AKBIK14.409, author = {Alan Akbik and Thilo Michael}, title = {The Weltmodell: A Data-Driven Commonsense Knowledge Base}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |