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Structured data artifacts such as tables are widely used in scientific literature to organize and concisely communicate important statistical information. Discovering relevant information in these tables remains a significant challenge owing to their structural heterogeneity, dense and often implicit semantics, and diffuse context. This paper describes how we leverage semantic technologies to enable technical experts to search and explore tabular data embedded within scientific documents. We present a system for the on-demand construction of knowledge graphs representing scientific tables (drawn from online scholarly articles hosted by PubMed Central) and for synthesizing tabular responses to semantic search requests against such graphs. We discuss key differentiators in our overall approach, including a two-stage semantic table interpretation that relies on an extensive structural and syntactic characterization of scientific tables and a prototype knowledge discovery engine that uses automatically inferred semantics of scientific tables to serve search requests by potentially fusing information from multiple tables on the fly. We evaluate our system on a real-world dataset of approximately 120,000 tables extracted from over 62,000 COVID-19-related scientific articles. 

Synthesizing information from collections of tables embedded within scientific and technical documents is increasingly critical to emerging knowledge-driven applications. Given their structural heterogeneity, highly domain-specific content, and diffuse context, inferring a precise semantic understanding of such tables is traditionally better accomplished through linking tabular content to concepts and entities in reference knowledge graphs. However, existing tabular data discovery systems are not designed to adequately exploit these explicit, human-interpretable semantic linkages. Moreover, given the prevalence of misinformation, the level of confidence in the reliability of tabular information has become an important, often overlooked, factor in the discovery over open datasets. We describe a preliminary implementation of a discovery engine that enables table-based semantic search and retrieval of tabular information from a linked knowledge graph of scientific tables. We discuss the viability of semantics-guided tabular data analysis operations, including on-the-fly table generation under reliability constraints, within discovery scenarios motivated by intelligence production from documents.