Aboutness Overview
Aboutness is what the things in OpenAlex are about. Most works are “about” something, and that aboutness aggregates up to characterize authors, institutions, sources, and other entities. OpenAlex offers several distinct aboutness signals, each usable as a filter; which one fits depends on your question.
Two properties help you choose. Granularity (the number of groups) sets how fine-grained an analysis can be. Familiarity is how recognizable the scheme is to others — how easily you can compare or share results.
| Signal | # groups | Familiarity | Fit to custom areas |
|---|---|---|---|
| SDGs | 17 | High | Low |
| Domains | 4 | High | Low |
| Fields | 26 | High | Low |
| Subfields | 252 | High | Medium |
| Topics | 4,516 | Low | Medium-high |
| Keywords | ~65,000 | Medium | High |
| Concepts (deprecated) | ~65,000 | High | Variable |
| Text search | ∞ | Low | High |
A rough guide: the topics hierarchy (domains → fields → subfields → topics) is the supported general-purpose system — pick the level whose granularity matches your question. Keywords fit narrower, more specific slices. SDGs map research onto the UN Sustainable Development Goals and little else. Concepts are deprecated — kept for continuity with Microsoft Academic Graph, no longer maintained; see Concepts. Text search fits custom areas no scheme covers, at the cost of comparability.
Aboutness for your own text
For the topics hierarchy and keywords, you can supply your own custom text — the title and abstract of an unpublished article, say, or a grant proposal — and get back SDGs, domains, fields, subfields, topics, and keywords in exactly the form OpenAlex assigns them to works. See the text aboutness endpoint.