Exploring the use of Graph and Machine / Deep Learning technologies with the NASA ADS content
2020-11-09, 11:45–12:00, Times in UTC

The NASA Astrophysics Data System (ADS) manages more than 14 million scientific abstracts with more than 5 million full text, more than 128 million citations and thousands of other relationships (e.g., articles’ keywords and data sources). NASA ADS users regularly explore the data using our website and API, which already relies on modern search technology such as Apache Solr. One of the next steps is to provide an even better service by, for instance, automatically enriching our dataset (e.g., article clustering/classification) or improving the search results (e.g., PageRank computation). To accomplish these goals, we are exploring state-of-the-art Graph and Machine/Deep Learning technologies such as Neo4J (Graph Database), BERT (Google’s Language Model for Natural Language Processing tasks) and Graph Neural Networks. We present our preliminary findings to shed some light on the challenges and opportunities that these technologies can offer.

Theme – Machine Learning, Statistics, and Algorithms