Guided curation of semistructured data in collaboratively-built knowledge bases

• Avoid proliferation of structures in the knowledge base.
• Semantic refinement by resolving homonyms and avoiding synonyms.
• Exploit user’s extensive and valuable knowledge.
• Increase the quantity of information contained in the knowledge base.
• Increase the quality of information in the knowledge base.

The collaborative curation of semistructured knowledge has become a popular paradigm on the web and also within enterprises. In such knowledge bases a common structure of the stored information is crucial for providing efficient and precise search facilities. However, the task of refining, extending and homogenizing knowledge and its structure is very complex. In this article we present two paradigms for the simplification of this task by providing guidance mechanisms to the user. Both paradigms aim at combining the power of automated extraction algorithms with the semantic awareness of human users to accomplish this refinement task.