Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Learning to extract relations for protein annotation
Bioinformatics
Information extraction challenges in managing unstructured data
ACM SIGMOD Record
SOFIE: a self-organizing framework for information extraction
Proceedings of the 18th international conference on 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
From information to knowledge: harvesting entities and relationships from web sources
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Find your advisor: robust knowledge gathering from the web
Procceedings of the 13th International Workshop on the Web and Databases
Discovering drug–drug interactions
Bioinformatics
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This paper introduces DIDO, a system providing convenient access to knowledge about factors involved in human diseases, automatically extracted from textual Web sources. The knowledge base is bootstrapped by integrating entities from hand-crafted sources like MeSH and OMIM. As these are short on relationships between dierent types of biomedical entities, DIDO employs flexible and robust pattern learning and constraint-based reasoning methods to automatically extract new relational facts from textual sources. These facts can then be iteratively added to the knowledge base. The result is a semantic graph of typed entities and relations between diseases, their symptoms, and their factors, with emphasis on environmental factors but covering also molecular determinants. We demonstrate the value of DIDO for knowledge discovery about causal factors and properties of complex diseases, including factor-disease chains.