Research Area: Machine Learning
Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach or an application is much more complicated. Many publications leverage multiple modalities to convey their findings and spread their ideas - they include pseudocode, tables, images and diagrams in addition to text, and often make publicly accessible their implementations. It is important to be able to represent and query them as well. We utilize RDF Knowledge graphs (KGs) to represent multimodal information and enable expressive querying over modalities. In our demo we present an approach for extracting KGs from different modalities, namely text, architecture images and source code. We show how graph queries can be used to get insights into different facets (modalities) of a paper, and its associated code implementation. Our innovation lies in the multimodal nature of the KG we create. While our work is of direct interest to DL researchers and practitioners, our approaches can also be leveraged in other scientific domains.
Keywords:
Multimodal Knowledge Graph
Deep Learning Papers
Machine Learning
Author(s) Name: Amar Viswanathan Kannan , Dmitriy Fradkin , Ioannis Akrotirianakis , Tugba Kulahcioglu , Arquimedes Canedo , Aditi Roy , Shih-Yuan Yu , Malawade Arnav , Mohammad Abdullah Al Faruque
Journal name:
Conferrence name: CIKM -20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Publisher name: ACM
DOI: 10.1145/3340531.3417439
Volume Information:
