With 13,000,000 volumes comprising 4.5 billion pages of text, it is currently very difficult for scholars to locate relevant sets of documents that are useful in their research from the HathiTrust Digital Libary (HTDL) using traditional lexically-based retrieval techniques. Existing document search tools and document clustering approaches use purely lexical analysis, which cannot address the inherent ambiguity of natural language. A semantic search approach offers the potential to overcome the shortcoming of lexical search, but-even if an appropriate network of ontologies could be decided upon-it would require a full semantic markup of each document. In this paper, we present a conceptual design and report on the initial implementation of a new framework that affords the benefits of semantic search while minimizing the problems associated with applying existing semantic analysis at scale. Our approach avoids the need for complete semantic document markup using pre-existing ontologies by developing an automatically generated Concept-in-Context (CiC) network seeded by a priori analysis of Wikipedia texts and identification of semantic metadata. Our Capisco system analyzes documents by the semantics and context of their content. The disambiguation of search queries is done interactively, to fully utilize the domain knowledge of the scholar. Our method achieves a form of semantic-enhanced search that simultaneously exploits the proven scale benefits provided by lexical indexing.

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To realize the great potential value of large‐scale digital libraries, we need a fuller understanding of the range of ways in which scholarly communities conduct research, or want to conduct research within them. Scholars build collections in the course of their work. How can we anticipate and support various kinds of collection‐building and ‐use, in order to support the diversity of researchers who work in libraries of digital books? This paper reports selected results of a study of how potential user groups of the HathiTrust Digital Library create and use collections in their research. This study aims to contribute to our broader understanding of scholarly practice, particularly of humanities scholars’ collecting activities. The results of the study inform ongoing work to develop a workset‐creation tool for the HathiTrust Research Center.

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

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