eduKateSG · Why Science?
A smart literature map still needs identifiers, query context and careful reading
Use the Semantic Scholar Academic Graph through paper IDs, authors, references, citations, fields, pagination, recommendations and open-data releases while preserving evidence limits.
Scientific search is no longer a list of titles alone. Modern tools connect papers with authors, venues, references, citations, topics and related work. Semantic Scholar, developed by Ai2, provides a search service and programmatic Academic Graph API for exploring those connections.
The official API distinguishes a Semantic Scholar paperId from a numerical corpusId and also accepts several external identifier forms for paper retrieval. Clients choose response fields explicitly; when fields are omitted, a compact response can contain only the paper identifier and title.
Did you know? Citation endpoints can expose contexts, intent labels and an influential-citation signal where available. These features can help prioritise reading, but an algorithmic label does not replace the citing sentence, surrounding argument or full papers.
This guide treats Semantic Scholar as an intelligent discovery layer. Search, citation graphs and recommendations can reveal promising sources. Every result still needs identity checks, full-text reading, publication-status review and domain judgment.
Chapter index
Chapters 1–9: paper and author identity, fields and links
- 1. The paperId is the primary graph key
- 2. External IDs support retrieval
- 3. Titles do not establish identity alone
- 4. Paper fields are selected
- 5. Author records require disambiguation
- 6. Paper–author links show association
- 7. References point backward
- 8. Citations point forward
- 9. Citation context aids interpretation
Chapters 10–17: citations, paging, recommendations and verification
- 10. Influential is an algorithmic signal
- 11. Search filters define the universe
- 12. Sorting can change during paging
- 13. Offsets and limits are part of retrieval
- 14. Batch requests reduce repeated calls
- 15. Recommendations begin with examples
- 16. Datasets freeze monthly releases
- 17. Literature-graph skills support careers
A scholarly search system connects papers, authors, citations and venues through structured records. Semantic Scholar assigns a paperId used by its site and API, while corpusId is another identifier used in datasets. These are distinct from DOI and other registry IDs. This turns algorithm-assisted discovery into an auditable research route.
Try this
Evidence question: Which identifier scheme does this value belong to? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Store paperId, corpusId and DOI in separate columns. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Keep the identifier, retrieval date and source page in the note so the route can be repeated.
Evidence checkpoint
Record the evidence object for the paperid is the primary graph key, its identifier or version context, and one limit that must remain visible.
Requested API fields determine how much of a paper or author object is returned. The API can retrieve papers using supported identifiers such as DOI and other scholarly database IDs. Exact prefixes prevent ambiguous interpretation. It also keeps search rank and citation counts separate from scientific validity.
Try this
Evidence question: Which external ID found this paper? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Repeat the lookup with the canonical prefixed form. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. If a field is missing, write “not shown” and follow the official documentation rather than inventing a value.
Evidence checkpoint
Record the evidence object for external ids support retrieval, its identifier or version context, and one limit that must remain visible.
Citation context and recommendation signals can guide reading without deciding which claim is true. Similar or changed titles can refer to different versions or outputs. Identifiers, authors, venue and year provide additional evidence. Students can explore related work while preserving the query and evidence boundary.
Try this
Evidence question: Do the title and identifiers resolve to one intended work? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Compare the API record with the publisher landing page. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Before comparing two records, check that their object types, methods, versions and units really align.
Evidence checkpoint
Record the evidence object for titles do not establish identity alone, its identifier or version context, and one limit that must remain visible.
Persistent external identifiers help reconcile a paper across multiple literature services. API clients specify fields such as year, authors, abstract, citations or references. A default response may intentionally omit most attributes. A careful workflow treats recommendations as candidates and verifies the underlying papers.
Try this
Evidence question: Was the field absent or simply not requested? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Save the fields parameter beside the response. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Finish by naming one supported statement and one stronger statement that still needs another evidence layer.
Evidence checkpoint
Record the evidence object for paper fields are selected, its identifier or version context, and one limit that must remain visible.
CHAPTER 5 OF 17 · Read authors and citations
5. Author records require disambiguation
A scholarly search system connects papers, authors, citations and venues through structured records. Author entities have identifiers and paper lists, but name matching can be imperfect. Affiliations and external profiles help distinguish namesakes. This turns algorithm-assisted discovery into an auditable research route.
Try this
Evidence question: Which works and identifiers support this author identity? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Check a sample against ORCID or institutional evidence. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Keep the identifier, retrieval date and source page in the note so the route can be repeated.
Evidence checkpoint
Record the evidence object for author records require disambiguation, its identifier or version context, and one limit that must remain visible.
Requested API fields determine how much of a paper or author object is returned. An author list connects people to a paper. It does not by itself describe each person’s conceptual, experimental or writing contribution. It also keeps search rank and citation counts separate from scientific validity.
Try this
Evidence question: What evidence supports contribution roles? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Use the paper’s contribution statement when available. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. If a field is missing, write “not shown” and follow the official documentation rather than inventing a value.
Evidence checkpoint
Record the evidence object for paper–author links show association, its identifier or version context, and one limit that must remain visible.
Citation context and recommendation signals can guide reading without deciding which claim is true. A paper’s references endpoint lists works appearing in its bibliography under the graph’s matching. Missing matches can make the returned list incomplete. Students can explore related work while preserving the query and evidence boundary.
Try this
Evidence question: Which bibliography entries were not resolved? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Compare an endpoint sample with the published reference list. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Before comparing two records, check that their object types, methods, versions and units really align.
Evidence checkpoint
Record the evidence object for references point backward, its identifier or version context, and one limit that must remain visible.
Persistent external identifiers help reconcile a paper across multiple literature services. The citations endpoint returns papers that cite the selected work. Coverage and updates affect totals and retrieved edges. A careful workflow treats recommendations as candidates and verifies the underlying papers.
Try this
Evidence question: Which graph release or retrieval date produced the list? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: State the service and date beside every citation count. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Finish by naming one supported statement and one stronger statement that still needs another evidence layer.
Evidence checkpoint
Record the evidence object for citations point forward, its identifier or version context, and one limit that must remain visible.
A scholarly search system connects papers, authors, citations and venues through structured records. Context snippets show where a work was cited, and intent fields may classify the role of a citation. Snippets can omit surrounding qualifications. This turns algorithm-assisted discovery into an auditable research route.
Try this
Evidence question: Does the full paragraph support the label? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Open the citing paper before paraphrasing its use. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Keep the identifier, retrieval date and source page in the note so the route can be repeated.
Evidence checkpoint
Record the evidence object for citation context aids interpretation, its identifier or version context, and one limit that must remain visible.
Requested API fields determine how much of a paper or author object is returned. The isInfluential field is a modelled indicator intended to distinguish some citations by estimated influence. It is not an editorial judgment or truth score. It also keeps search rank and citation counts separate from scientific validity.
Try this
Evidence question: What claim would overreach this signal? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Use it to prioritise reading, not to rank validity. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. If a field is missing, write “not shown” and follow the official documentation rather than inventing a value.
Evidence checkpoint
Record the evidence object for influential is an algorithmic signal, its identifier or version context, and one limit that must remain visible.
Citation context and recommendation signals can guide reading without deciding which claim is true. Bulk paper search supports query text and filters such as date, publication type, open-access PDF and citation thresholds. Every filter excludes otherwise relevant records. Students can explore related work while preserving the query and evidence boundary.
Try this
Evidence question: Which constraint most changes the question? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Save an unfiltered and filtered request for comparison. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Before comparing two records, check that their object types, methods, versions and units really align.
Evidence checkpoint
Record the evidence object for search filters define the universe, its identifier or version context, and one limit that must remain visible.
Persistent external identifiers help reconcile a paper across multiple literature services. API documentation notes that data changes during paging can affect result order for some sorts, while paperId ordering helps stability. A careful workflow treats recommendations as candidates and verifies the underlying papers.
Try this
Evidence question: Could updates create duplicates or gaps? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Deduplicate by paperId and record the sort. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Finish by naming one supported statement and one stronger statement that still needs another evidence layer.
Evidence checkpoint
Record the evidence object for sorting can change during paging, its identifier or version context, and one limit that must remain visible.
CHAPTER 13 OF 17 · Evaluate recommendations
13. Offsets and limits are part of retrieval
A scholarly search system connects papers, authors, citations and venues through structured records. Citation, reference and author-paper endpoints return result windows. A first page is not a complete corpus. This turns algorithm-assisted discovery into an auditable research route.
Try this
Evidence question: Which offset and limit were collected? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Log every window and the next marker. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Keep the identifier, retrieval date and source page in the note so the route can be repeated.
Evidence checkpoint
Record the evidence object for offsets and limits are part of retrieval, its identifier or version context, and one limit that must remain visible.
Requested API fields determine how much of a paper or author object is returned. Batch endpoints retrieve selected fields for several paper identifiers. Efficient requests still need size limits and error handling. It also keeps search rank and citation counts separate from scientific validity.
Try this
Evidence question: Were failed or missing IDs recorded? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Keep input order and per-ID status in the receipt. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. If a field is missing, write “not shown” and follow the official documentation rather than inventing a value.
Evidence checkpoint
Record the evidence object for batch requests reduce repeated calls, its identifier or version context, and one limit that must remain visible.
Citation context and recommendation signals can guide reading without deciding which claim is true. The Recommendations API accepts positive and, for one operation, negative example papers and returns candidate papers. Results depend on those seeds and requested limit. Students can explore related work while preserving the query and evidence boundary.
Try this
Evidence question: Which seed paper drove this recommendation? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Record positive and negative IDs with the output. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Before comparing two records, check that their object types, methods, versions and units really align.
Evidence checkpoint
Record the evidence object for recommendations begin with examples, its identifier or version context, and one limit that must remain visible.
Persistent external identifiers help reconcile a paper across multiple literature services. Semantic Scholar offers downloadable Academic Graph datasets updated on a release schedule. A dataset result may differ from the live API after later changes. A careful workflow treats recommendations as candidates and verifies the underlying papers.
Try this
Evidence question: Which release ID and component were used? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Save the release manifest and licence context. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Finish by naming one supported statement and one stronger statement that still needs another evidence layer.
Evidence checkpoint
Record the evidence object for datasets freeze monthly releases, its identifier or version context, and one limit that must remain visible.
CHAPTER 17 OF 17 · Evaluate recommendations
17. Literature-graph skills support careers
A scholarly search system connects papers, authors, citations and venues through structured records. Researchers, librarians, evidence reviewers, machine-learning engineers and science communicators use paper graphs. Their responsibility is to keep algorithms and evidence roles distinct. This turns algorithm-assisted discovery into an auditable research route.
Try this
Evidence question: Which decision requires opening the full paper? Answer in three parts: identify the visible field or file, explain how it bears on the question, and state the boundary of that evidence. Preserve the database’s own object names and accession rather than flattening everything into the word ‘data’.
Try this: Build a Semantic Scholar research receipt for a school review. Turn the result into a compact claim-evidence-reasoning note, then ask a partner to retrace it. Keep the identifier, retrieval date and source page in the note so the route can be repeated.
Evidence checkpoint
Record the evidence object for literature-graph skills support careers, its identifier or version context, and one limit that must remain visible.
Contents · Previous chapter · Continue at the Science Learning Hub
Use intelligent discovery to decide what to read, not what to believe
Semantic Scholar can make the literature easier to navigate through structured paper, author and citation records plus recommendation tools. The graph is most useful when queries, fields, identifiers and retrieval windows stay visible.
Keep the paperId, external identifiers, requested fields, filters, sort, paging, citation context, recommendation seeds and access date together. Then inspect the full publications and their current status before evaluating the science.
| Reading object | What it can support | Question to keep visible |
|---|---|---|
| Paper ID | Semantic Scholar graph identity | Which DOI or external ID confirms it? |
| Citation edge | Matched reference relationship | What does the full citing context say? |
| API request | Selected fields and result window | Can another reader repeat it? |
| Recommendation | Candidate from seed papers | Which seeds and limits shaped it? |
Sources and next reading
Substantive resource claims in this article were checked against the primary pages below. A structured record is a route into evidence; it does not by itself prove a scientific conclusion. Recheck the live record, evidence labels and release context before using it in assessed work or research.
For a classroom enquiry, save a short research receipt containing the question, search terms, selected object, exact identifier, version, access date and the evidence boundary you observed. Add the file or page that supports each statement. This small routine turns browsing into accountable science learning and gives a teacher, classmate or future analyst a route they can independently follow.
Before sharing the conclusion, ask another reader to retrace the route without verbal hints. If they reach a different version, file or interpretation, improve the receipt and narrow the claim. Reproducibility begins with this practical check. It teaches careful reading, supports fair collaboration and keeps uncertainty visible while evidence moves from an online service into notes, analysis and a final explanation.
A useful final review separates five questions. What object was found? Which organisation or repository maintains it? What exact evidence was inspected? Which statement does that evidence support? What remains uncertain? Answer each question in one sentence, then connect every answer to a visible field, file or source page. This review catches common mistakes: citing a collection when a file supplied the value, treating the newest release as the analysed release, confusing permission with scientific suitability, or turning a discovery count into a conclusion. It also gives students a calm way to improve work. A missing detail becomes a next research step instead of an invitation to guess. When the route is complete, the final explanation can stay simple because the identifiers, dates, versions, limitations and supporting evidence are already organised underneath it.
Primary sources
- Semantic Scholar Academic Graph API — accessed 25 October 2026.
- Semantic Scholar API product page — accessed 25 October 2026.
- Semantic Scholar API tutorial — accessed 25 October 2026.
- Semantic Scholar Recommendations API — accessed 25 October 2026.
- Semantic Scholar Datasets API — accessed 25 October 2026.