DBLP Publication Search

Search and extract computer science publications from DBLP -- the largest open bibliography database for computer science with over 6 million publications from journals, conferences, and workshops. Filter by keyword, author, venue, year, and publication type. Returns structured citation data including authors, DOIs, pages, volumes, and electronic edition URLs. No API key required.

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What does DBLP Publication Search do?

DBLP Publication Search is an Apify actor that queries the DBLP (Digital Bibliography & Library Project) search API to find and extract structured metadata from computer science publications. DBLP is maintained by Schloss Dagstuhl -- Leibniz Center for Informatics and Universitat Trier in Germany. It indexes publications from all major computer science publishers including ACM, IEEE, Springer, Elsevier, USENIX, and many others, covering over 6 million publication records across thousands of venues.

The actor accepts flexible search parameters -- free-text keyword queries, author names, conference or journal venues, publication years, and publication types -- then returns clean, structured JSON for each matching publication. Each result includes the DBLP key, full title, complete author list, venue name, publication year, type classification, page range, volume and issue numbers, DOI, canonical DBLP URL, electronic edition link, and extraction timestamp.

DBLP uses a field-specific query syntax that the actor constructs automatically from your input parameters. When you provide an author name, it is translated to author:Name: in the query. Venue filters become venue:Name:, year filters become year:YYYY:, and type filters become type:TypeName:. These field qualifiers can be combined with free-text keywords for precise, targeted searches.

This is ideal for literature reviews, bibliometric analysis, author publication tracking, conference proceedings monitoring, citation data extraction, and building academic research datasets at scale.

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Why use DBLP Publication Search on Apify?

Running this actor on Apify gives you several advantages over calling the DBLP API directly:

• No infrastructure to manage. The actor runs in the cloud, handles pagination automatically, and stores results in a dataset you can export in JSON, CSV, or Excel format.
• No API key required. DBLP is a completely free and open API. This actor requires no registration, authentication tokens, or API keys on your part.
• Pagination handled for you. The DBLP API returns results in pages of up to 100 records. The actor manages offset-based pagination transparently, fetching multiple pages with built-in rate limiting until your maxResults limit is reached.
• Structured, clean output. Raw DBLP API responses contain polymorphic data structures where titles, authors, venues, and electronic edition links can be either strings, objects, or arrays depending on the record. The actor normalizes everything into a consistent, flat schema ready for analysis.
• Schedule and automate. Set the actor to run on a schedule to track new publications from a specific author, conference, or research topic. Get notified when new results appear.
• Integrate with anything. Connect results to Google Sheets, Slack, webhooks, or other Apify actors using built-in integrations. Access data programmatically via the Apify REST API or Python/JavaScript client libraries.

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Key features

• Full-text keyword search across publication titles and metadata for any computer science topic.
• Author filtering to find all works by a specific researcher using DBLP's author:Name: field syntax.
• Venue filtering to scope results to a specific conference or journal using DBLP's venue:Name: field syntax (e.g., "NeurIPS", "ICML", "VLDB", "ACM Computing Surveys").
• Year filtering to retrieve publications from a specific year using DBLP's year:YYYY: field syntax.
• Publication type filtering across four categories: Conference and Workshop Papers, Journal Articles, Informal and Other Publications, and Parts in Books or Collections.
• Combinable filters -- all search parameters can be used together in a single query for highly targeted results.
• Up to 500 results per run with automatic multi-page fetching (100 results per API request).
• Rate-limit compliant -- built-in 1-second delay between paginated API requests to respect DBLP usage policies.
• Rich metadata -- each result includes 13 structured fields covering the full citation record.

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How to use DBLP Publication Search

1. Navigate to the DBLP Publication Search actor page on Apify Console.
2. Enter at least one search parameter:
   • Search Query -- free-text keywords such as "deep learning" or "graph neural networks"
   • Author Name -- e.g., "Yoshua Bengio", "Geoffrey Hinton"
   • Venue / Conference / Journal -- e.g., "NeurIPS", "ICML", "VLDB", "IEEE TSE"
   • Publication Year -- e.g., "2024"
3. Optionally select a Publication Type to restrict results to one of the four categories.
4. Set Maximum Results (1 to 500, default 50).
5. Click Start and wait for the run to complete.
6. Download your results from the Dataset tab in JSON, CSV, or Excel format.

Example use cases

• Search for all NeurIPS 2024 papers on "transformer architecture"
• Track a specific researcher's complete publication history (e.g., all papers by "Yann LeCun")
• Monitor new conference papers in top AI venues like ICML, CVPR, or AAAI
• Build a dataset of journal articles on "reinforcement learning" for bibliometric analysis
• Export DBLP citation data to a spreadsheet for grant application literature reviews

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Input parameters

Parameter	Type	Required	Default	Description
query	String	No*	-	Keywords to search for in publication titles and metadata
author	String	No*	-	Filter results by author name (e.g., "Yoshua Bengio")
venue	String	No*	-	Filter by venue name such as a conference or journal (e.g., "NeurIPS", "ICML", "VLDB")
year	String	No*	-	Filter by publication year (e.g., "2024")
type	String	No	All Types	Publication type filter: Conference and Workshop Papers, Journal Articles, Informal and Other Publications, or Parts in Books or Collections
maxResults	Integer	No	50	Maximum number of publications to return (1--500)

*At least one of query, author, venue, or year must be provided.

Input examples

Basic topic search -- find the 50 most relevant papers on deep learning:

{
    "query": "deep learning",
    "maxResults": 50
}

Author search -- find all indexed publications by a specific researcher:

{
    "author": "Yoshua Bengio",
    "maxResults": 200
}

Conference and year filter -- find all NeurIPS papers from 2024:

{
    "venue": "NeurIPS",
    "year": "2024",
    "type": "Conference and Workshop Papers",
    "maxResults": 500
}

Combined keyword and venue search -- find papers on graph neural networks in ICML:

{
    "query": "graph neural networks",
    "venue": "ICML",
    "maxResults": 100
}

Journal article search -- find journal articles on formal verification:

{
    "query": "formal verification",
    "type": "Journal Articles",
    "year": "2025",
    "maxResults": 100
}

Tips for best results

• Combine filters for precision. Using both a keyword query and a venue or year filter returns much more relevant results than a broad keyword search alone.
• Use exact author names. DBLP maintains canonical author names. Search for "Yoshua Bengio" rather than "Y. Bengio" for the best match quality.
• Venue abbreviations work. You can use common abbreviations like "NeurIPS", "ICML", "CVPR", "VLDB", "SIGMOD", "AAAI", "ICLR", or full names like "ACM Computing Surveys".
• Understand the four publication types. Conference and Workshop Papers covers peer-reviewed conference proceedings. Journal Articles covers periodical publications. Informal and Other Publications includes arXiv preprints and CoRR entries. Parts in Books or Collections covers book chapters and edited volume contributions.
• Start with fewer results. Use the default of 50 results to validate your query, then increase maxResults once you confirm the results match your expectations.
• Schedule for monitoring. Use Apify's scheduling feature to run the actor daily or weekly to track new publications in your research area.

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Programmatic access

You can call DBLP Publication Search programmatically using the Apify API. Here are examples in Python, JavaScript, and cURL.

Python (using the apify-client package):

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("eWBl1oo2MNg11IUA8").call(run_input={
    "query": "large language models",
    "venue": "NeurIPS",
    "year": "2024",
    "type": "Conference and Workshop Papers",
    "maxResults": 100,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    authors = ", ".join(item["authors"])
    print(f"{item['title']} -- {authors} ({item['year']})")

JavaScript (using the apify-client package):

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: "YOUR_API_TOKEN" });

const run = await client.actor("eWBl1oo2MNg11IUA8").call({
    query: "large language models",
    venue: "NeurIPS",
    year: "2024",
    type: "Conference and Workshop Papers",
    maxResults: 100,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    const authors = item.authors.join(", ");
    console.log(`${item.title} -- ${authors} (${item.year})`);
});

cURL (start a run and retrieve results):

# Start the actor run
curl -X POST "https://api.apify.com/v2/acts/eWBl1oo2MNg11IUA8/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "large language models",
    "venue": "NeurIPS",
    "year": "2024",
    "maxResults": 50
  }'

# Retrieve results from the dataset (use the defaultDatasetId from the response above)
curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json"

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Output example

Each item in the output dataset follows this structure:

{
    "dblpKey": "conf/nips/VaswaniSPUJGKP17",
    "title": "Attention is All you Need.",
    "authors": [
        "Ashish Vaswani",
        "Noam Shazeer",
        "Niki Parmar",
        "Jakob Uszkoreit",
        "Llion Jones",
        "Aidan N. Gomez",
        "Lukasz Kaiser",
        "Illia Polosukhin"
    ],
    "venue": "NIPS",
    "year": "2017",
    "type": "Conference and Workshop Papers",
    "pages": "5998-6008",
    "volume": "",
    "number": "",
    "doi": "",
    "url": "https://dblp.org/rec/conf/nips/VaswaniSPUJGKP17",
    "electronicEdition": "https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html",
    "extractedAt": "2026-02-21T12:00:00.000Z"
}

---

Output fields reference

Field	Type	Description
dblpKey	String	Unique DBLP record key identifying the publication (e.g., conf/nips/VaswaniSPUJGKP17)
title	String	Full title of the publication
authors	Array of Strings	List of author names in order of appearance
venue	String	Publication venue -- conference abbreviation or journal short name (e.g., "NIPS", "Nat.", "CoRR")
year	String	Year of publication
type	String	DBLP publication type: Conference and Workshop Papers, Journal Articles, Informal and Other Publications, or Parts in Books or Collections
pages	String	Page range (e.g., "5998-6008") or empty string if unavailable
volume	String	Volume number or identifier (e.g., "521", "abs/2401.04088")
number	String	Issue number within a volume, or empty string if unavailable
doi	String	Digital Object Identifier for the publication, or empty string if unavailable
url	String	Canonical DBLP URL for the publication record
electronicEdition	String	Direct link to the electronic edition (publisher page, PDF, or preprint)
extractedAt	String	ISO 8601 timestamp of when the data was extracted

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How it works

The actor follows a straightforward pipeline to query the DBLP publication search API, paginate through results, and transform raw metadata into clean output:

DBLP Publication Search
 ┌─────────────────────────────────────────────────────────────────┐
 │                                                                 │
 │  INPUT                                                          │
 │  query, author, venue, year, type, maxResults                   │
 │                                                                 │
 │  ┌──────────────┐    ┌──────────────┐    ┌──────────────────┐   │
 │  │  1. Validate  │───>│ 2. Build     │───>│ 3. Fetch page    │   │
 │  │  input params │    │  DBLP query  │    │  (100 per req)   │   │
 │  └──────────────┘    │  with field   │    └───────┬──────────┘   │
 │                      │  qualifiers   │            │              │
 │                      └──────────────┘    ┌───────v──────────┐   │
 │                                          │ 4. More pages?   │   │
 │                                          │ offset < total   │   │
 │                                          │ count < maxResults│──>│ Loop (1s delay)
 │                                          └───────┬──────────┘   │
 │                                                  │ Done         │
 │  ┌──────────────────┐    ┌───────────────────┐   │              │
 │  │ 6. Push to Apify │<───│ 5. Normalize      │<──┘              │
 │  │    dataset       │    │    polymorphic     │                  │
 │  └──────────────────┘    │    DBLP data       │                  │
 │                          └───────────────────┘                  │
 │  OUTPUT                                                         │
 │  Flat JSON with 13 fields per publication                       │
 │                                                                 │
 └─────────────────────────────────────────────────────────────────┘

Field-specific query syntax

DBLP supports field qualifiers in its search query language. Rather than requiring you to learn this syntax, the actor constructs the query string automatically from your input parameters:

• An author name of "Yoshua Bengio" becomes author:Yoshua Bengio: in the query
• A venue of "NeurIPS" becomes venue:NeurIPS: in the query
• A year of "2024" becomes year:2024: in the query
• A type of "Journal Articles" becomes type:Journal Articles: in the query

These field qualifiers are concatenated with any free-text keywords you provide. For example, entering query "deep learning", author "Geoffrey Hinton", and year "2024" produces the combined query: deep learning author:Geoffrey Hinton: year:2024:.

Polymorphic data normalization

The DBLP API returns data in inconsistent formats depending on the record. The actor handles all of these variations transparently:

• Titles can be a plain string or an object with a text property. The actor extracts the string in both cases.
• Authors can be a single author object or an array of author objects. Each author object can be a string or an object with a text property and an optional @pid identifier. The actor always returns a flat array of author name strings.
• Venues can be a single string or an array of strings. The actor joins multiple venues with commas.
• Electronic editions (the ee field) can be a single URL string or an array of URLs. The actor returns the first URL.

Pagination and rate limiting

The DBLP search API supports offset-based pagination through the h (hits per page) and f (first result offset) parameters. The actor requests up to 100 results per page and advances the offset after each page. A 1-second delay is enforced between paginated requests to comply with DBLP's usage policies. Pagination stops when the total available results are exhausted or the maxResults limit is reached.

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How much does it cost to run?

DBLP Publication Search is very lightweight because it only makes REST API calls -- there is no browser rendering or web scraping involved. The DBLP API itself is completely free with no usage fees.

Scenario	Results	Approx. time	Approx. cost
Quick literature check	50	~5 seconds	$0.001
Author bibliography	200	~15 seconds	$0.003
Full conference extraction	500	~50 seconds	$0.005

The actor runs on 256 MB of memory by default, which is more than sufficient. Costs are based on Apify platform compute units and may vary slightly depending on network latency. Running this actor daily for a year on the default settings would cost well under $1 in total.

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Limitations and responsible use

• Maximum 500 results per run. The actor caps output at 500 publications per run. For broader data collection, run multiple queries with different filter combinations (e.g., different years or venues).
• No full-text access. DBLP provides bibliographic metadata only. The actor does not download or return the full text of papers. Use the doi, url, or electronicEdition fields to access the paper through the publisher's website or preprint server.
• Computer science focus. DBLP indexes publications in computer science and closely related fields. For broader academic searches across all disciplines, use actors that query Crossref, Semantic Scholar, OpenAlex, or PubMed.
• No year ranges. The DBLP API's year filter accepts a single year, not a range. To search across multiple years, either omit the year filter and filter results after extraction, or run separate queries for each year.
• Rate limiting. The actor enforces a 1-second delay between paginated API requests to respect DBLP's usage policies. Very large extractions will take proportionally longer. Do not schedule excessively frequent runs.
• Venue name matching. DBLP uses its own venue abbreviations which may differ from common usage. For example, NeurIPS papers may appear under "NIPS" or "NeurIPS" depending on the year. Try alternate venue names if results seem incomplete.
• DOI availability. Not all DBLP records include a DOI. Older publications and some workshops may have empty DOI fields. Use the url or electronicEdition fields as alternatives.
• Respectful use. DBLP is a community-funded academic infrastructure. Do not schedule excessively frequent runs or extract data beyond what you need for legitimate research or analysis.

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FAQ

What is DBLP? DBLP (Digital Bibliography & Library Project) is a free, open bibliographic database for computer science maintained by Schloss Dagstuhl and Universitat Trier in Germany. It indexes over 6 million publications from journals, conferences, and workshops, making it the most comprehensive open bibliography in computer science.

Does this actor require an API key? No. The DBLP API is completely open and free to use. No API key, registration, or authentication is needed. The actor works out of the box with no additional configuration.

What are the four publication types available? DBLP classifies publications into four types: (1) Conference and Workshop Papers -- peer-reviewed conference and workshop proceedings, (2) Journal Articles -- periodical publications in journals, (3) Informal and Other Publications -- arXiv preprints, technical reports, and CoRR entries, and (4) Parts in Books or Collections -- book chapters and contributions to edited volumes.

Can I search for publications outside computer science? DBLP focuses exclusively on computer science and closely related fields. For broader academic searches, consider actors that query Crossref (all disciplines), Semantic Scholar (broad coverage with AI features), OpenAlex (open scholarly metadata), or PubMed (biomedical and life sciences).

How current is the DBLP data? DBLP is continuously updated. New publications from major venues typically appear within days to weeks of official publication. Preprints from arXiv (indexed under CoRR) are also included.

What is the DBLP key and how do I use it? The DBLP key is a unique identifier for each publication in the DBLP database (e.g., conf/nips/VaswaniSPUJGKP17). The key encodes the venue type, venue abbreviation, and author/year information. You can construct the full DBLP record URL by prepending https://dblp.org/rec/ to the key.

Can I search by DOI? There is no dedicated DOI filter, but you can enter a DOI as a keyword in the Search Query field. DBLP will often return the matching publication if it is indexed.

Why do some results have empty DOI or pages fields? Not all publications in DBLP have complete bibliographic metadata. Older records, workshop papers, and informal publications may lack DOIs, page numbers, or volume information. The actor returns empty strings for unavailable fields so the output schema is always consistent.

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