Declarative Data Cleaning: Language, Model, and Algorithms
Proceedings of the 27th International Conference on Very Large Data Bases
Distant supervision for relation extraction without labeled data
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Relational duality: unsupervised extraction of semantic relations between entities on the web
Proceedings of the 19th international conference on World wide web
Open information extraction using Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
YAGO2: exploring and querying world knowledge in time, space, context, and many languages
Proceedings of the 20th international conference companion on World wide web
An analysis of open information extraction based on semantic role labeling
Proceedings of the sixth international conference on Knowledge capture
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
ClausIE: clause-based open information extraction
Proceedings of the 22nd international conference on World Wide Web
INDREX: in-database distributional relation extraction
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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Current techniques for Open Information Extraction (OIE) focus on the extraction of binary facts and suffer significant quality loss for the task of extracting higher order N-ary facts. This quality loss may not only affect the correctness, but also the completeness of an extracted fact. We present KrakeN, an OIE system specifically designed to capture N-ary facts, as well as the results of an experimental study on extracting facts from Web text in which we examine the issue of fact completeness. Our preliminary experiments indicate that KrakeN is a high precision OIE approach that captures more facts per sentence at greater completeness than existing OIE approaches, but is vulnerable to noisy and ungrammatical text.