Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
One-class svms for document classification
The Journal of Machine Learning Research
Text classification from positive and unlabeled documents
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Towards the self-annotating web
Proceedings of the 13th international conference on World Wide Web
Weakly-supervised relation classification for information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Proceedings of the 15th international conference on World Wide Web
Neural Computation
ACM SIGIR Forum
Combining linguistic and statistical analysis to extract relations from web documents
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Relation extraction using label propagation based semi-supervised learning
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
RelExt: a tool for relation extraction from text in ontology extension
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Automatic extraction of hierarchical relations from text
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Extracting instances of relations from web documents using redundancy
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Characterizing the semantic web on the web
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Extracting relations in social networks from the web using similarity between collective contexts
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
A framework for schema-driven relationship discovery from unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Enhancing relation extraction by eliciting selectional constraint features from wikipedia
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Catriple: Extracting Triples from Wikipedia Categories
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
SOFIE: a self-organizing framework for information extraction
Proceedings of the 18th international conference on World wide web
International Journal of Human-Computer Studies
Extracting Enterprise Vocabularies Using Linked Open Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Disambiguating identity web references using Web 2.0 data and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Learning 5000 relational extractors
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Large scale relation detection
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Information extraction from Wikipedia using pattern learning
Acta Cybernetica
Building ontological models from Arabic Wikipedia: a proposed hybrid approach
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Introduction to linked data and its lifecycle on the web
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
SCMS: semantifying content management systems
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Open language learning for information extraction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Crosslingual distant supervision for extracting relations of different complexity
Proceedings of the 21st ACM international conference on Information and knowledge management
Towards an enhanced and adaptable ontology by distilling and assembling online encyclopedias
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Introduction to linked data and its lifecycle on the web
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
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Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly out-performs the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains.