Topics
A topic is a fine-grained research area — “Artificial Intelligence in Healthcare and Education,” “Geological and Geochemical Analysis,” and about 4,500 more. Topics are the bottom, most granular level of OpenAlex’s classification hierarchy: 4 domains → 26 fields → 252 subfields → 4,516 topics. Every work with enough metadata is assigned up to three topics automatically, and those assignments aggregate up to characterize authors, institutions, and sources. A topic’s OpenAlex ID looks like T11636; fetch one at api.openalex.org/topics/T11636. Topics are one of several aboutness signals — see that page to choose the right level of granularity for your question.
How it’s made
Topics are inferred, not looked up: a machine-learning model reads each work’s text and predicts what it’s about. That’s why topics (and the whole hierarchy above them) live under Aboutness rather than Vocabulary — the labels are standardized, but the assignment is a prediction. The classification system was developed with CWTS at Leiden University, extending their open approach to classifying research publications.
Building the topic list
The set of topics itself was built from the citation network. OpenAlex started with works that have incoming and outgoing citations and clustered them by citation relationships: works that cite each other frequently land in the same cluster, and those clusters correspond to real research communities. A large language model then generated a human-readable name and description for each cluster. Finally, each topic was mapped to a subfield, field, and domain using Scopus’s ASJC categories, which is how every topic gets a place in the four-level hierarchy.
Assigning topics to works
A deep-learning classifier assigns topics to any work from its title, abstract, citations, and journal name. The model handles missing data gracefully — it can classify a brand-new work that has no incoming citations yet from just its title, abstract, and source. It scores every candidate topic; the highest-scoring one becomes the work’s primary_topic, and the top few (up to three) appear in the work’s topics array, each with a score. Some works can’t be classified at all — no title, no abstract, no citations means the model has nothing to work with. About 12% of works have no topic for this reason (39.6M of 322M as of mid-2026); count them with filter=primary_topic.id:null.
One primary subfield per work
Because a work’s topics roll up the hierarchy, every work also gets a single primary subfield, field, and domain — the ones its primary_topic maps to. This single-primary choice is deliberate: it lets OpenAlex normalize citation impact (FWCI) against works in the same subfield, and it means a work is classified from its own text, not from the catch-all subject of the journal it happened to appear in. The trade-off is precision over recall: a work about the statistics of cancer trials gets one primary subfield, even though it touches several.
Subfields vs. concepts
Before topics, OpenAlex classified works with concepts — a Wikipedia-derived vocabulary inherited from the Microsoft Academic Graph. Concepts are deprecated: no longer maintained, and superseded by topics. The two work very differently. Concepts matched work metadata to Wikipedia concepts, accepting every match above a relevancy score and firing parent concepts whenever a child matched — high recall, low precision, so concept queries surface many works that aren’t really on point. Topics (and their subfields) come from the primary-topic pipeline above, with each topic mapping to a single subfield — much higher precision, at the cost of missing works whose primary topic lands elsewhere. To recover some recall, filter on the full topics array (topics.subfield.id:...) instead of primary_topic alone, which matches any of a work’s assigned topics.
You can also run the classifier on your own text — a draft abstract or a grant proposal — and get back the same topics, subfields, keywords, and SDGs OpenAlex would assign. See the text aboutness endpoint.
Attributes
This is the canonical dictionary of every attribute on a topic object. Attributes shared with other entities (id, ids, display_name, works_count, cited_by_count, created_date, updated_date) are documented once on Common attributes; topic-specific notes are below.
id
String. The OpenAlex ID for this topic, e.g. https://openalex.org/T11636. Topics use the T#### scheme (unlike domains, fields, and subfields, which use bare numeric IDs). See Common attributes.
ids
Object. External identifiers for this topic, as URIs. Topic-specific keys: openalex and (when a matching article exists) wikipedia.
display_name
String. The topic’s name, e.g. “Artificial Intelligence in Healthcare and Education.” Generated by an LLM from the topic’s citation cluster. See Common attributes.
description
String. A paragraph describing what the topic’s cluster of papers is about, also LLM-generated.
keywords
List. A handful of short phrases summarizing the topic (e.g. “Machine Learning,” “Medical Imaging”). These are descriptive labels on the topic itself, distinct from work-level keywords, which are scored per work.
subfield
Object. The subfield this topic belongs to (id, display_name) — the level directly above it in the hierarchy.
field
Object. The field this topic rolls up into (id, display_name).
domain
Object. The domain this topic rolls up into (id, display_name) — the top of the hierarchy.
siblings
List. The other topics that share this topic’s subfield (id, display_name), useful for navigating laterally within a subfield. Empty for a topic that is the only one in its subfield.
works_count
Integer. How many works have this topic assigned (as their primary or a secondary topic). See Common attributes.
cited_by_count
Integer. Total citations across all works assigned this topic. See Common attributes.
works_api_url
String. A ready-made Works API URL for every work tagged with this topic, e.g. https://api.openalex.org/works?filter=topics.id:T11636. A convenience link; OpenAlex doesn’t store work IDs on the topic object.
created_date
String. The date this topic was added to OpenAlex (YYYY-MM-DD). See Common attributes.
updated_date
String. The ISO 8601 UTC timestamp of the last change to the topic object. See Common attributes.
In the API
The Topics endpoint is at api.openalex.org/topics. Fetch a single topic by ID — /topics/T11636 — or a list, and filter, search, sort, group, and page over it.
You can filter and group topics by their place in the hierarchy — domain.id, field.id, subfield.id — and by works_count, cited_by_count, id, and display_name, all of which also sort. Full-text matching uses the search parameter (the older .search filters like display_name.search and keywords.search are deprecated). Most topic use, though, is on the Works endpoint: filter=primary_topic.id:T11636 (works whose primary topic is this one), filter=topics.id:T11636 (works with this topic anywhere in their top three), or the hierarchy roll-ups topics.subfield.id, topics.field.id, and topics.domain.id. See the Topics API reference and the endpoints index.