REVIEW
Accurate Portraits of Scientific Resources and Knowledge Service Components
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Accurate Portraits of Scientific Resources and Knowledge Service Components
read the original abstract
With the advent of the cloud computing era, the cost of creating, capturing, and managing information has gradually decreased. The amount of data on the Internet is showing explosive growth, and more scientific and technological resources are being uploaded to the network. Different from news and social media data, scientific and technological resources are mainly composed of academic-style resources or entities, such as papers, patents, authors, and research institutions. There is a rich relationship network between these resources, from which a large amount of cutting-edge scientific and technological information can be mined. Existing scientific resource management and classification standards are difficult to completely cover all entities and associations, and they cannot accurately extract the important information contained in scientific and technological resources. Therefore, how to construct a complete and accurate representation of scientific and technological resources from structured and unstructured reports and texts, and how to tap the potential value of scientific and technological resources, are urgent problems. A feasible solution is to construct accurate portraits of scientific and technological resources by combining knowledge graph technology, text representation learning, entity extraction, and knowledge service components.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.