Assessing National Development Plans for Alignment With Sustainable Development Goals via Semantic Search

Authors

  • Jonathan Galsurkar IBM T.J. Watson Research Center
  • Moninder Singh IBM T.J. Watson Research Center
  • Lingfei Wu IBM T.J. Watson Research Center
  • Aditya Vempaty IBM T.J. Watson Research Center
  • Mikhail Sushkov IBM Watson
  • Devika Iyer United Nations Development Programme
  • Serge Kapto United Nations Development Programme
  • Kush Varshney IBM T.J. Watson Research Center

DOI:

https://doi.org/10.1609/aaai.v32i1.11424

Keywords:

Social Good, United Nations, Semantic Search, Sustainable Development Goals, Word embeddings, word2vec, tf-idf

Abstract

The United Nations Development Programme (UNDP) helps countries implement the United Nations (UN) Sustainable Development Goals (SDGs), an agenda for tackling major societal issues such as poverty, hunger, and environmental degradation by the year 2030. A key service provided by UNDP to countries that seek it is a review of national development plans and sector strategies by policy experts to assess alignment of national targets with one or more of the 169 targets of the 17 SDGs. Known as the Rapid Integrated Assessment (RIA), this process involves manual review of hundreds, if not thousands, of pages of documents and takes weeks to complete. In this work, we develop a natural language processing-based methodology to accelerate the workflow of policy experts. Specifically we use paragraph embedding techniques to find paragraphs in the documents that match the semantic concepts of each of the SDG targets. One novel technical contribution of our work is in our use of historical RIAs from other countries as a form of neighborhood-based supervision for matches in the country under study. We have successfully piloted the algorithm to perform the RIA for Papua New Guinea’s national plan, with the UNDP estimating it will help reduce their completion time from an estimated 3-4 weeks to 3 days.

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Published

2018-04-27

How to Cite

Galsurkar, J., Singh, M., Wu, L., Vempaty, A., Sushkov, M., Iyer, D., Kapto, S., & Varshney, K. (2018). Assessing National Development Plans for Alignment With Sustainable Development Goals via Semantic Search. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11424