Machine Learning
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Efficient Weight Learning for Markov Logic Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
DBpedia - A crystallization point for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Open information extraction using Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning 5000 relational extractors
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Scalable knowledge harvesting with high precision and high recall
Proceedings of the fourth ACM international conference on Web search and data mining
Inductive learning of disjointness axioms
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part II
PARIS: probabilistic alignment of relations, instances, and schema
Proceedings of the VLDB Endowment
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A machine learning approach for instance matching based on similarity metrics
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Computing incoherence explanations for learned ontologies
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
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Over the recent past, information extraction (IE) systems such as Nell and ReVerb have attained much success in creating large knowledge resources with minimal supervision. But, these resources in general, lack schema information and contain facts with high degree of ambiguity which are often difficult to interpret. Whereas, Wikipedia-based IE projects like DBpedia and Yago are structured, have disambiguated facts with unique identifiers and maintain a well-defined schema. In this work, we propose a probabilistic method to integrate these two types of IE projects where the structured knowledge bases benefit from the wide coverage of the semi-supervised IE projects and the latter benefits from the schema information of the former.