Scientific Discovery as Link Prediction in Influence and Citation Graphs

Transcription

1 Scientific Discovery as Link Prediction in Influence and Citation Graphs Fan Luo, Marco Valenzuela-Escárcega, Gus Hahn-Powell, Mihai Surdeanu Text Graphs Workshop, June 6,

2 Background 2

3 Psychology Biology Economy Environment Source: Nelson, et al. Effects of poverty on interacting biological systems underlying child development. The Lancet Child & Adolescent Health 3

4 Publications indexed by PubMed each year since

5 If humans cannot keep up, machines must help! 5

6 Previous Work: Influence Search We implemented a machine reading system focused on influence statements in children s health literature 1. Large-scale automated reading with Reach discovers new cancer driving mechanisms. 2. Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph. 6

7 Influence Search 7

8 Use Case DE - MODIFIED 1K OUTPUT economic growth Campylobacter poverty Diarrhea food insecurity MGNREGR nutrition (including all positive food supply changes) supplementary food acute malnutrition malnutrition childhood/infant mal. under-nutrition inadequate nutrition PEM economic decisions in Bas-Congo EBF HIV health practitioner childhood obesity Infection Disease (incl ci) excess high protein intake rural setting informed parent obesity later obesity overweight obesity IMR Death EBF birth ARI asphyxia Adult obesity weight gain in infancy Breastfeeding Stunting cheap processed food nutrient enriched diet rapid urbanisation HFD CVD pneumonia NCD Nutrition education low maternal cd4 cell count government subsidy socio-economic changes parent caregiver poor maternal heath/death Inadequate infant care Immune system abnormality poor nutrition Improved water access mms provision enforcement of state regulations early childhood obesity prevention Constructed in 2 days (human + machine); Normally, it takes 1 month (human alone). Bill & Melinda Gates Foundation 6 Model courtesy of: Lyn Powell, HBGDki-qPM team 8

9 Motivation 9

10 Past vs. Future This system can only search past, published facts No information about what comes next in science 10

11 Definition White spaces in science + Topics that are insufficiently studied, but + May lead to important scientific discoveries 11

12 Our Contributions 1. White space discovery = link prediction over the influence graph Predict whether an influence link will be added to the graph dietary fish oil reduces blood reduces viscosity promotes Reynaud s disease Binary classification task: positive, if the influence relation will be added to the influence graph in the future; negative, otherwise Swanson, D.R. Undiscovered public knowledge. The Library Quarterly, 56 (2),

13 Our Contributions 1. White space discovery = link prediction over the influence graph Predict whether an influence link will be added to the graph dietary fish oil reduces Reynaud s disease Binary classification task: positive, if the influence relation will be added to the influence graph in the future; negative, otherwise Swanson, D.R. Undiscovered public knowledge. The Library Quarterly, 56 (2),

14 Our Contributions 2. Features from multiple graphs! Influence graph (to understand influence connectivity) Citation graph (to understand community overlap) 14

15 Dataset 15

16 Complication: No "Back to the Future" 16

17 Dataset blood viscosity? >= t fish oil Raynaud s disease Constructed through backtesting 17

18 Dataset t <= r3.year <= present (Positive) r3 not exist until present (Negative) 18

19 Note: Transitivity Generally Not True! Rainfall Hurricane Crop yield Missing information impacts non-linear models! 19

20 Dataset t =

21 Features Extracted from two graphs Influence graph (influence relations between concepts) 1,564,748 distinct nodes Connected by 2,395,944 influence relations Citation graph (citations between papers) 119K papers 5,523,759 citation links 21

22 Feature groups Feature Group Intuition From Connectivity features Communitybased features Information retrieval features The more connected concepts are, the easier is to discover a relation between them The larger the intersection of communities containing the two influence statements, the easier it is to make the connection The more distinct a concept or an influence statement is, the harder it is to make a discovery around it influence graph citation graph papers containing influence statements 22

23 Connectivity Features Out degree Connecting paths A B C In degree 24

24 Community-based Features A B cite cite cite cite The communities were detected using the Coda algorithm (Yang et al., 2014) 25

25 Community-based Features A B cite cite cite cite cite cite cite B C Bridging inter-disciplinary papers 26

26 Information Retrieval Features Inverse document frequency (IDF) score of lemmas in concept A IDF score of lemmas in concept B IDF score of lemmas in concept C Number of papers that mention A Number of papers that mention B B C 27

27 Evaluation 28

28 Evaluation Metrics Unranked 1 Ranked

29 Results 30

30 All Feature Groups Help F1 scores for feature ablation

31 What DoestheSystem Predict? 32

32 Conclusions Novel strategy for the identification of white spaces in scientific knowledge Operates over real-world graphs of influence relations and citations F1 score of 27 points, and a mean average precision of 68% Important to Researchers: What should I research next? Program officers: What should I fund next? 33

33 Thank you! Resource Available: Data and code Influence searchengine Fan Luo, Marco Valenzuela-Escárcega, Gus Hahn-Powell, Mihai Surdeanu {fanluo, marcov, hahnpowell, 34

34 Acknowledgements Marco Valenzuela-Escarcega, Gus Hahn-Powell, and Mihai Surdeanu declare a financial interest in lum.ai. This interest has been properly disclosed to the University of Arizona Institutional Review Committee and is managed in accordance with its conflict of interest policies. This work was funded by the Bill and Melinda Gates Foundation HBGDki Initiative. 35

35 Appendix 36

36 37

37 Connectivity Features Community based Features Information retrieval Features 38