Table of Contents

• List of Publications by Functional Area and Contributing Team
  • Causal Knowledge Extraction and Assembly
    • Florida Institute for Human and Machine Cognition (IHMC)
    • Raytheon BBN
    • University of Arizona
    • Multi-Institute Collaborations
  • Qualitative Analysis
    • University of Pittsburgh
  • Quantitative Analysis
    • Columbia, CSIRO, and PIK
    • Kimetrica
    • Stanford/Atlas AI
    • University of California, Santa Barbara (UCSB)
    • University of Southern California (USC) Information Sciences Institute (ISI)

List of Publications by Functional Area and Contributing Team

This page contains the full list of publications authored by teams contributing to the World Modelers program.

Causal Knowledge Extraction and Assembly

Florida Institute for Human and Machine Cognition (IHMC)

• Submitted paper on the Collaborative Dialogue Agent to a special issue on Conversational AI for AI Magazine

Raytheon BBN

• Rapid Customization for Event Extraction. DOI: https://doi.org/10.18653/v1/P19-3006
• [Submitted] White paper on learning ontologies and constructing an event timeline/database
• A Case Study on Learning a Unified Encoder of Relations. DOI: https://doi.org/10.18653/v1/W18-6126
• “Event Detection with Minimal Supervision”, submitted to NAACL 2019: [link forthcoming]
• The following two papers were accepted by the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019).
  • “Machine Reading for Interventions.” Bonan Min, Yee Seng Chan, Haoling Qiu and Joshua Fasching. Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature. EMNLP-IJCNLP 2019.
  • We demonstrated that aggregated event counts (extracted from US-based streaming news articles) correlated well with socio-economic indicators such as unemployment rate, Economic Policy Uncertainty, and VIX. Bonan Min and Xiaoxi Zhao. Measure Country-Level Socio-Economic Indicators with Streaming News: An Empirical Study. EMNLP-IJCNLP 2019.
• We submitted a paper summarizing our TDP work to ACL-2020. Can BERT improve temporal dependency parsing? Hayley Ross, Jonathon Cai, and Bonan Min. Submitted to ACL-2020. [link forthcoming]
• Publication on how information extraction has changed over the past 25 years, connecting to evaluations organized by the US Government. “Twenty-five years of information extraction.” Ralph Grishman. In Natural Language Engineering (2019), 25, pp. 677-692. DOI: https://doi.org/10.1017/S1351324919000512
• Master’s thesis titled “On zero-shot transfer learning for event extraction”, which shows that the use of paradigmatic event role names and event type names have significant impact on the success of the zero-shot approach. https://cs.nyu.edu/media/publications/haroon_shaheer.pdf
• We submitted a paper summarizing or sub-event relation extraction work to ACL-2020. “Weakly Supervised Subevent Knowledge Acquisition.” Wenlin Yao, Zeyu Dai, Maitreyi Ramaswamy, Ruihong Huang and Bonan Min. Submitted to ACL 2020. DOI: https://doi.org/10.18653/v1/2020.emnlp-main.430
• We submitted the few-shot event extraction work to LREC-2020: “Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach.” Bonan Min, Yee Seng Chan, and Lingjun Zhao. Submitted to LREC-2020
• Our work on few-shot event extraction was selected for publication at LREC-2020: “ Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach.” Bonan Min, Yee Seng Chan, and Lingjun Zhao. Submitted to LREC-2020

University of Arizona

• Having Your Cake and Eating it Too: Training Neural Retrieval for Language Inference without Losing Lexical Match. Yadav, V.; Bethard, S.; and Surdeanu, M. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1625–1628, 2020.
• Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering. Yadav, V.; Bethard, S.; and Surdeanu, M. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4514-4525, Online, July 2020.
• Exploring Interpretability in Event Extraction: Multitask Learning of a Neural Event Classifier and an Explanation Decoder. Tang, Z.; Hahn-Powell, G.; and Surdeanu, M. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, Seattle, United States, July 2020.
• Data and Model Distillation as a Solution for Domain-transferable Fact Verification.Mithun, M.; Suntwal, S.; and Surdeanu, M. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021.
• Interpretability Rules: Jointly Bootstrapping a Neural Relation Extractor with an Explanation Decoder. Tang, Z.; and Surdeanu, M. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: TrustNLP Workshop, 2021.
• If You Want to Go Far Go Together: Unsupervised Joint Candidate Evidence Retrieval for Multi-hop Question Answering. Yadav, V.; Bethard, S.; and Surdeanu, M. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4571–4581, Online, June 2021. Association for Computational Linguistics

Multi-Institute Collaborations

• (Submitted) [Two Six Technologies, University of Arizona, Harvard Medical School, Raytheon BBN, Qntfy] Taxonomy Builder: a data-driven and user-centric tool for streamlining taxonomy construction
• [University of Arizona, Harvard Medical School, University of Florida, Uncharted] MOIRE: MOdeling and Inference from REading, Submitted modeling paper to Modeling the World’s Systems Conference 2019
• [University of Arizona, Harvard Medical School, University of Florida, Uncharted] Submitted system demo papers describing Eidos and Delphi to NAACL-HLT 2019 and Modeling the World’s Systems Conference
• [University of Arizona, Harvard Medical School] Eidos, INDRA, & Delphi: From free text to executable causal models. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Qualitative Analysis

University of Pittsburgh

• Sayed, K., Telmer, C. A., Butchy, A. A., & Miskov-Zivanov, N. (2017, September). Recipes for translating big data machine reading to executable cellular signaling models. In International Workshop on Machine Learning, Optimization, and Big Data (pp. 1-15). Springer, Cham.
• Sayed, K., Kuo, Y. H., Kulkarni, A., & Miskov-Zivanov, N. (2017, December). DiSH simulator: Capturing dynamics of cellular signaling with heterogeneous knowledge. In 2017 Winter Simulation Conference (WSC) (pp. 896-907). IEEE. DOI: http://doi.org/10.1109/WSC.2017.8247841.
• Zhou, G., Liang, K. W., & Miskov-Zivanov, N. (2018, August). Sensitivity Analysis of Discrete Models and Application in Biological Networks. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 605-606).
• (Lecture) N. Miskov-Zivanov, “Dynamic System Explanation, DySE, a framework that evolves to reason about complex systems,” Artificial Intelligence for Data Discovery and Reuse (AIDR) 2019.
• (Poster) K. Sayed, “Stochastic Simulations of Hybrid Models with DiSH,” Modeling the World’s Systems (MWS) 2019.
• (Poster) Y. Ahmed, “Automated assembly of models: Applications in biology and world systems,” Modeling the World’s Systems (MWS) 2019.
• Telmer, C. A., Sayed, K., Butchy, A., Bocan, K. N., Holtzapple, E., Hansen, C. E., … & Miskov-Zivanov, N. (2019, May). Dynamic system explanation: DySE, a framework that evolves to reason about complex systems-lessons learned. In Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse (pp. 1-10).
• Zhou, G., Liang, K. W., & Miskov-Zivanov, N. (2019). Intervention Pathway Discovery via Context-Dependent Dynamic Sensitivity Analysis. arXiv preprint https://doi.org/10.48550/arXiv.1902.03216.
• Andjelkovic, S., & Miskov-Zivanov, N. (2021, December). DiSH-trend: Intervention Modeling Simulator That Accounts for Trend Influences. In Winter Simulation Conference (WSC). IEEE. DOI: https://doi.org/10.1109/WSC52266.2021.9715401

Uncharted Software

• F. Husain, P. Proulx, M. -W. Chang, R. Romero-Gómez and H. Vasquez, “A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling,” 2021 IEEE Visualization Conference (VIS), 2021, pp. 121-125, doi: 10.1109/VIS49827.2021.9623318.

Quantitative Analysis

Columbia, CSIRO, and PIK

• Kakinuma, K., M.J. Puma, Y. Hirabayashi, M. Tanoue, E.A. Baptista, and S. Kanae, 2020: Flood-induced population displacements in the world. Environ. Res. Lett., 15, no. 12, 124029, doi: https://doi.org/10.1088/1748-9326/abc586.
• Falkendal, T., C. Otto, J. Schewe, J. Jägermeyr, M. Konar, M. Kummu, B. Watkins, and M.J. Puma, 2021: Grain export restrictions during COVID-19 risk food insecurity in many low- and middle-income countries. Nat. Food, 2, no. 1, 11-14, doi: https://doi.org/10.1038/s43016-020-00211-7.
• Schon, J., K. Mezuman, A. Heslin, R.D. Field, and M.J. Puma, 2021: How fire patterns reveal uneven stabilization at the end of conflict: Examining Syria’s unusual fire year in 2019. Environ. Res. Lett., 16, no. 4, 044046, doi: https://doi.org/10.1088/1748-9326/abe327.
• Lehikoinen, E., P. Kinnunen, J. Piipponen, A. Heslin, M.J. Puma, and M. Kummu, 2021: Importance of trade dependencies for agricultural inputs: A case study of Finland. Environ. Res. Commun., 3, no. 6, 061003, doi: https://doi.org/10.1088/2515-7620/ac02d0.
• In preparation: Puma MJ, Chon S, Wada Y, Cook BI, Nordbotten JM, Falkendal, T, Otto C. A Richter scale reveals the magnitude of global food disruptions. (In preparation for Environ. Res. Lett.)
• In preparation: Heino M, Kinnunen P, Anderson W, Ray D, Puma MJ, Varis O, Kummu M. Increased probability of hot and dry weather extremes during the growing season threaten crop yields. (In preparation)
• In preparation: Mezuman K, Puma MJ, Suleimenova D, and Groen D. Impact of food insecurity on refugee movement in an agent-based model. (in preparation)
• In preparation: Mezuman K, Puma MJ, Concha Larrauri P, and Lall U. Applying Bayesian inference to model refugee movement. (In preparation.)
• In preparation: Karakoc DB, Konar M, Varshney L, Puma MJ. Resilience and controllability of the US food systems. (In preparation)
• In preparation: Schauberger B, Ostberg S: Global crop yield forecasting on high resolution several weeks ahead of harvest. (In preparation)

Kimetrica

• CHIRTS – We are close to re-submitting a paper on CHIRTS-daily to Scientific Data. We have revised CHIRTS-daily to use a different satellite input (ERA5 instead of MERRA2), have increased the accuracy of the previous product (based on validation from station data and external products). All of these results will be summarized in the paper. A new Usage Section focuses on a sample application in Ethiopia.

Stanford/Atlas AI

• Eoin McGuirk and Marshall Burke (2017) The Economic Origins of Conflict in Africa. http://web.stanford.edu/~mburke/papers/McGuirkBurke2017_NBER.pdf

University of California, Santa Barbara (UCSB)

• Paper submitted to NIPS
• Paper presented at COMPASS conference
• Davenport, F., L. Harrison, S. Shukla, G. Husak, C. Funk, and A. McNally. What is the Best Model Specification and Earth Observation Product for Predicting Regional Grain Yields in Food Insecure Countries? (In Review)
• WRSI paper complete, being submitted now to either Science Advances or ERL
• Submitted paper on predicting crop yield
• Davenport, F. M., Harrison, L., Shukla, S., Husak, G., Funk, C., & McNally, A. (2019). “Using out-of-sample yield forecast experiments to evaluate which earth observation products best indicate end of season maize yields.” Environmental Research Letters, 14(12), 124095. doi: https://doi.org/10.1088/1748-9326/ab5ccd
• Paper submitted to J. of Climate -Funk et al. (2019) The 1983-2016 Climate Hazards Infrared plus station high-resolution Tmax climate data record, In Review
• Shukla, S., Husak, G., Turner, W., Davenport, F., Funk, C., Harrison, L., & Krell, N. (2021). “A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa.” PLoS ONE, 16(1), e0242883. doi: https://doi.org/10.1371/journal.pone.0242883
• Paper accepted in PNAS: Global Urban Population Exposure to Extreme Heat, -Tuholske et al. (forthcoming)
• Davenport, F. M., Shukla, S., Turner, W., Funk, C., Krell, N., Harrison, L., Husak, G., Lee, D., & Peterson, S. (2021). Sending out an S.O.S: using start of rainy season indicators for market price forecasting to support famine early warning. Environmental Research Letters.
• Submitted paper to Scientific Data: “CHIRPS-compatible NCEP GEFS precipitation forecasts for advancing early warning capabilities” by Laura Harrison, Martin Landsfeld, Gregory Husak, Frank Davenport, Shrad Shukla, Will Turner, Pete Peterson, Chris Funk

University of Southern California (USC) Information Sciences Institute (ISI)

• “Artificial Intelligence for Water Resources Management: Towards Efficient Integration”, Invited talk by Yolanda Gil at the Annual Meeting of the American Association for the Advancement of Science (AAAS), special session on “Finding Water Management Solutions With Artificial Intelligence”, February 17, 2018.
• “MINT: Model Integration Through Knowledge-Powered Data and Process Composition,” Yolanda Gil, Kelly Cobourn, Ewa Deelman, Chris Duffy, Rafael Ferreira da Silva, Armen Kemanian, Craig Knoblock, Vipin Kumar, Scott Peckham, Lucas Carvalho, Yao-Yi Chiang, Daniel Garijo, Deborah Khider, Ankush Khandelwal, Minh Pahm, Jay Pujara, Varun Ratnakar, Maria Stoica, Binh Vu. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
• “A Semantic Model Catalog to Support Comparison and Reuse,” Daniel Garijo, Deborah Khider, Yolanda Gil, Lucas Carvalho, Bakinam Essawy, Suzanne Pierce, Daniel Hardesty Lewis, Varun Ratnakar, Scott Peckham, Chris Duffy, Jonathan Goodall. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
• “Towards Model Integration via Abductive Workflow Composition and Multi-Method Scalable Model Execution,” Rafael Ferreira da Silva, Daniel Garijo, Scott Peckham, Yolanda Gil, Ewa Deelman, Varun Ratnakar. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
• “Principle-based, Semi-automatic Ontology Generation to Support Cross-Domain Interoperability of Data Sets and Models,” Scott D. Peckham and Maria Stoica. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
• “Quo Vadis: Reflecting on the Past, Looking into the Future,” Yolanda Gil, invited keynote at the First US Semantics Symposium, March 1-2, 2018, http://us2ts.org/posts/program/.
• “OntoSoft: A Semantic Model Registry to Support Model Comparison and Reuse,” Yolanda Gil, invited presentation at the ESIP Science Software Webinar Series, March 21, 2018. http://wiki.esipfed.org/index.php/2017-2018_Speaker_Series
• Papers presented at the 9th International Congress on Environmental Modelling and Software:
  • “MINT: Model Integration Through Knowledge-Powered Data and Process Composition,” Yolanda Gil, Kelly Cobourn, Ewa Deelman, Chris Duffy, Rafael Ferreira da Silva, Armen Kemanian, Craig Knoblock, Vipin Kumar, Scott Peckham, Lucas Carvalho, Yao-Yi Chiang, Daniel Garijo, Deborah Khider, Ankush Khandelwal, Minh Pahm, Jay Pujara, Varun Ratnakar, Maria Stoica, Binh Vu. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
  • “A Semantic Model Catalog to Support Comparison and Reuse,” Daniel Garijo, Deborah Khider, Yolanda Gil, Lucas Carvalho, Bakinam Essawy, Suzanne Pierce, Daniel Hardesty Lewis, Varun Ratnakar, Scott Peckham, Chris Duffy, Jonathan Goodall. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
  • “Towards Model Integration via Abductive Workflow Composition and Multi-Method Scalable Model Execution,” Rafael Ferreira da Silva, Daniel Garijo, Scott Peckham, Yolanda Gil, Ewa Deelman, Varun Ratnakar. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
  • “Principle-based, Semi-automatic Ontology Generation to Support Cross-Domain Interoperability of Data Sets and Models,” Scott D. Peckham and Maria Stoica. Submitted to the 9th International Congress on Environmental Modelling and Software, 2018. https://tinyurl.com/y7xluudu
• Gil invited to speak at DARPA’s D60 event in the panel on “Accelerating Science“, presentation titled “Artificial Intelligence for Rigorous Science and Interdisciplinary Frontiers” includes slide on World Modelers work
• Invited talk at DARPA’s D60 event by Yolanda Gil in the panel on “Accelerating Science“ on September 6, presentation titled “Artificial Intelligence for Rigorous Science and Interdisciplinary Frontiers” included slide on World Modelers work
• “An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models.” Daniel Garijo, Deborah Khider, Varun Ratnakar, Yolanda Gil, Ewa Deelman, Rafael Ferreira da Silva, Craig Knoblock, Yao-Yi Chiang, Minh Pham, Jay Pujara, Binh Vu, Dan Feldman, Rajiv Mayani, Kelly Cobourn, Chris Duffy, Armen Kemanian, Lele Shu, Vipin Kumar, Ankush Khandelwal, Kshitij Tayal, Scott Peckham, Maria Stoica, Anna Dabrowski, Daniel Hardesty-Lewis, Suzanne Pierce. Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion. pp 111-112, Los Angeles, CA, January 2019. Available from http://doi.org/10.1145/3308557.3308711.
• “The Scientific Variables Ontology: A Blueprint for Custom Manual and Automated Creation and Alignment of Machine-Interpretable Qualitative and Quantitative Variable Concepts.” Maria Stoica, Scott Peckham. Paper submitted to: Modeling the World’s Systems Conference, 2019.
• “Parsing, Representing and Transforming Units of Measure.” Basel Shbita, Arunkumar Rajendran, Jay Pujara, and Craig A. Knoblock. Modeling the World’s Systems 2019.
• “OKG-Soft: An Open Knowledge Graph for Describing, Composing and Reusing Software.” Daniel Garijo, Maximiliano Osorio, Deborah Khider, Varun Ratnakar and Yolanda Gil. Submitted to the 15th IEEE International Conference on eScience. San Diego, CA, September 24-27, 2019. [Under review]
• “FAIR Computational Workflows.” Carole Goble, Sarah Cohen-Boulakia,Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, and Daniel Schober. Accepted to the Data Intelligence journal special issue on FAIR best practices, 2019. Available from https://zenodo.org/record/3268653.
• Goble, C.; Cohen-Boulakia, S.; Soiland-Reyes, S.; Garijo, D.; Gil, Y.; Crusoe, M. R.; Peters, K.; and Schober, D, FAIR Computational Workflows, in Data Intelligence, 2(1): 108-121. 2020.
• Garijo, D. and Osorio M., OBA: An Ontology-Based Framework for Creating REST APIs for Knowledge Graphs, in Proceedings of the 2020 International Semantic Web Conference.
• Gil,Y.; Garijo, D.; Khider, D.; Knoblock, C.A.; Ratnakar, V.; Osorio, M.; Vargas, H.; Pham,M.; Pujara, J.; Shbita, B. Vu, B; Chiang, Y.; Feldman, D.; Lin, Y.; Song, H.; Kumar, V.; Khandelwal, A.; Steinbach, M.; Tayal, K.; Xu, S.; Pierce, S.A.; Pearson, L.; Hardesty-Lewis, D.; Deelman, E.; Ferreira da Silva, R.; Mayani, R.; Kemanian, A.R.; Shi, Y.; Leonard, L.; Peckham, S.; Stoica, M.; Cobourn, K.; Zhang, Z.; Duffy, C.; and Shu, L. Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making , ACM Transactions on Interactive Intelligent Systems (TiiS), 11(2). 2021.
• Willard, J.; Jia, X.; Xu, S.; Steinbach, M.; and Kumar, V., Integrating physics-based modeling with machine learning: A survey, in Preprint.
• Vu, B.; Knoblock, C.A.; Szekely, P.; Pham, M.; Pujara, J., A Graph-based Approach for Inferring Semantic Descriptions of Wikipedia Tables, in Proceedings of the International Semantic Web Conference, 2021.
• Pham, M.; Knoblock, C.A.; Chen, M.; Vu, B.; Pujara, J., SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables, in Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021.