Predictive Article Recommendation Using Natural Language Processing and Machine Learning to Support Evidence Updates in Domain-specific Knowledge Graphs

Objectives: Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs.

Methods: New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inclusion in the knowledge graph; discrepancies are resolved by consensus.

Results: Study classifiers achieved F-scores from 0.88 to 0.94, and similarity thresholds for each study type were determined by experimentation. Our approach reduces human literature review load by 99%, and over the past 12 months, 41% of recommendations were accepted to update the knowledge graph.

Conclusion: Integrated search and recommendation exploiting current evidence in a knowledge graph is useful for reducing human cognition load.