Combine or connect: Practical experiences querying library linked data

Abstract

Linked Data provides a conceptual foundation for creating unified views across Digital Libraries, but implementation challenges must be overcome to realize the vision of computationally assisted cross‐corpus research. We report practical experiences comparing two alternative workset building approaches across combined datasets: the HathiTrust Digital Library and the Early English Books Online Text Creation Partnership. In one experiment we combine both datasets within one triplestore using a single ontology and apply consolidated querying; in the other we build two distributed triplestores, each dataset conforming to its own ontology, and connected through federated querying. Each solution presents tradeoffs in complexity, system efficiency and responsiveness, and in the workload of configuring new methods providing access to Digital Libraries. We demonstrate that choosing a consolidated or federated approach fundamentally alters the dataset configuration process for cross‐corpora workset building, so should be considered early in deployment specification and design. As both approaches provide equivalent functionality to the end‐user, the practice and experience documented here inform design and development of distributed Linked Data Digital Libraries offering combined collection querying.

Linked data for libraries: Creating a global knowledge space, a systematic literature review

“…Weigl et al presented their practical experience on two data sets using as a base the HathiTrust DigitalLibrary and the Early English Books Online Text CreationPartnership. Combining the data sets with one triplestore or with two triplestores together with ontologies, they documented the functionality [161]. Finally, Di Iorio and Schaerf [162] managed to analyse a digital library system so as to record how an information system manages data sources considering linked data.…”

Evaluating BERT-based scientific relation classifiers for scholarly knowledge graph construction on digital library collections

“…According to [4,5], current keyword-arXiv:2305.02291v1 [cs.DL] 3 May 2023 based methods for indexing scholarly articles cause related documents to be scattered and emphasize concentration on selected keywords rather than all aspects of knowledge in an article. Given these limitations, in order to optimize scholarly knowledge organization and representation in DL, some initiatives [23,52] advocate for building an interlinked and semantically rich knowledge graph structure using a combination of human curation and machine learning techniques.…”