Using ORKG Ask for search and exploration of scientific articles

ORKG Ask is an open-source scholarly search and exploration system supported by Artificial Intelligence (AI), making it possible to find relevant articles across more than 75 million items. With Ask, researchers are able to find scholarly literature by asking specific research questions. A summary answers the research question based on the most relevant literature. Furthermore, the research question is answered for each listed article. In addition, key insights from research articles are automatically extracted. Finally, powerful filtering options, including semantic concepts, make it possible to narrow down the search and find articles researchers are looking for. In this presentation we will discuss the individual features of the ORKG Ask, focusing on how Ask can be used when doing research, while also mentioning the limitations and challenges when using AI for finding scholarly literature.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-30
DOI
https://doi.org/10.5281/zenodo.16613304
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using ORKG Ask for search and exploration of scientific articles

Zenodo (CERN European Organization for Nuclear Research)
Biomedical Text Mining and Ontologies
article

Using ORKG Ask for search and exploration of scientific articles

article en

Abstract

ORKG Ask is an open-source scholarly search and exploration system supported by Artificial Intelligence (AI), making it possible to find relevant articles across more than 75 million items. With Ask, researchers are able to find scholarly literature by asking specific research questions. A summary answers the research question based on the most relevant literature. Furthermore, the research question is answered for each listed article. In addition, key insights from research articles are automatically extracted. Finally, powerful filtering options, including semantic concepts, make it possible to narrow down the search and find articles researchers are looking for. In this presentation we will discuss the individual features of the ORKG Ask, focusing on how Ask can be used when doing research, while also mentioning the limitations and challenges when using AI for finding scholarly literature.

Zenodo (CERN European Organization for Nuclear Research)
Technische Informationsbibliothek (TIB) (DE)
Quality Education
Openalex Percentile: Top 53%
Biomedical Text Mining and Ontologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.