MKEM: a multi-level knowledge emergence model for mining undiscovered public knowledge

Background: Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. Methods: We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for ...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Ali Zeeshan Ijaz, Min Song, Doheon Lee
• Keywords: CIKM 2009, Database, Implicit Relationships, Knowledge Emergence Model, Undiscovered Public Knowledge