KAAPA: Knowledge Aware Answers from PDF Analysis

Fauceglia, N.; Canim, M.; Gliozzo, A.; Liang, J. J.; Wang, N. X. R.; Burdick, D.; Mihindukulasooriya, N.; Castelli, V.; Feigenblat, G.; Konopnicki, D.; Katsis, Y.; Florian, R.; Li, Y. Y.; Roukos, S.; Sil, A.; Assoc Advancement Artificial, Intelligence

Fauceglia, N.; Canim, M.; Gliozzo, A.; Liang, J. J.; Wang, N. X. R.; Burdick, D.; Mihindukulasooriya, N.; Castelli, V.; Feigenblat, G.; Konopnicki, D.; Katsis, Y.; Florian, R.; Li, Y. Y.; Roukos, S.; Sil, A.; Assoc Advancement Artificial, Intelligence.

35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence ; 35:16029-16031, 2021.

Article in English | Web of Science | ID: covidwho-1436748

ABSTRACT

ABSTRACT

We present KAAPA (Knowledge Aware Answers from PDF Analysis), an integrated solution for machine reading comprehension over both text and tables extracted from PDFs. KAAPA enables interactive question refinement using facets generated from an automatically induced Knowledge Graph. In addition, it provides a concise summary of the supporting evidence for the provided answers by aggregating information across multiple sources. KAAPA can be applied consistently to any collection of documents in English with zero domain adaptation effort. We showcase the use of KAAPA for QA on scientific literature using the COVID-19 Open Research Dataset.

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Collection: Databases of international organizations Database: Web of Science Language: English Journal: 33rd Conference on Innovative Applications of Artificial Intelligence Year: 2021 Document Type: Article

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