Your scientific knowledge provider
Artificial Researcher is an information technology company and a start-up in the text mining industry. We provide industry and academia with unified platforms that increases the productivity.
Our system solutions offer our customers and clients a market advantage,
we reduce their efforts in terms of time spent searching and reviewing information.
We are specialists in developing novel and innovative scientific knowledge management systems tailored to the needs of researchers and information search professionals. We provide unified platforms that can increase the productivity of industry and academia end-users, by exploring and implementing innovative text mining technologies.
We know how to help our customers find the relevant information and tailor it to the needs of our customers.
We develop scientific knowledge management systems by exploring and implementing innovative text mining technologies.
Our systems provide our customers with information and recomendations for purchase and research strategies.
Passage Retrieval Service
Try our new service
On this platform we provide a demo version of our unique passage retrieval service. Our service uses innovative text mining technologies based on 15 years of collected know-how and research that gives you state of the art machine learning.
The data collections available to you in this demo are: COVID-19 Open Research Dataset (CORD-19) data set, a sample set from the EP Full-Text Data for Text Analytics, and a sample composed of different scientific publications within technical and medical science provided by CORE.uk (Science).
Please give us feedback and let us know what you think to help us improve and develop a service you will love to use. For access to our larger collections, which we provide via a APIs, please contact firstname.lastname@example.org
Projects and Partners
iFAIR: Identifying datasets by mining research papers to make more data FAIR
FAIR research data shall be Findable, Accessible, Interoperable, and Reusable.
This co-creation project aims to develop a new proof-of-concept technology for automatic identification of datasets by mining research publications. More specifically, we aim to improve the current open source state-of-the-art tool GROBID (grobid.readthedocs.io) for parsing raw research papers.
This work will contribute to closing the gap between datasets that are mentioned but never properly registered. For instance, our tool will make it possible to:
- Analyse and monitor the proportion of datasets mentioned in scientific papers but are not yet FAIR.
- Develop tools to notify authors of scientific papers containing references to non-FAIR datasets, and of the need to make these datasets FAIR. This could be then connected to services helping to find an appropriate data repository, such as re3data.org.
The work in this project will find its use in the processing pipelines of tools creating research graphs, scholarly search engines, open repository systems, publishing systems, etc., and will support the needs of researchers and students by contributing to making research datasets more discoverable.
Artificial Researcher - Ontology for Covid-19
Supported by European Open Science Cloud - website
AR-Onto-Covid: A Knowledge-Based Resource for Covid-19
In the current crisis of the Covid-19 pandemic we focus our expertise on the development of novel and innovative scientific knowledge management systems that cater to the global needs of researchers and information search professionals. We reduce their effort in terms of time spent searching for information and reviewing publications, making it possible for researches and medical professionals to intensify the work on the development of vaccines and treatments.
By combining our expertise in Natural Language Processing and Artificial Intelligence/Machine Learning with human expert annotations we create high-quality knowledge-based resources starting from the openly available Covid-19 resources. At the end of this project we will have provided a benchmark data set that can be used for further Machine Learning experiments, together with an Covid-19 ontology and an API-based solution to query it.
In this project we develop a solution that increases access to the medical knowledge published as Open Access, as well as to the relevant bio-medical patent documents. Our solution will make the bio-medical data related to Covid-19 freely searchable and openly available to the EOSC community and researchers at large.
Artificial Researcher in Science
Artificial Research in Science: Efficient Scientific Publication Mining (AR-Science)
In the project AR-Science we aim to develop a novel and innovative platform, which automatically handles scientific information needs and presents the information to the users according to their requests. The result of this work will be a software prototype that, after passing series of user testing conducted by the TU WIEN Library (UB TU), will be commercialised by the start-up AR-IT. The AR-Science prototype has a two-year development cycle since this is a collaboration with TUW University Library and TUW IFS. The AR-Science solution is a significant long-term commitment and investment for the academic libraries
Artificial Researcher in Open Access
Supported by Austria Wirtschaftservice - website
Artificial Researcher in Open Access (AR-OpenAccess)
With AR-OpenAccess we give institutions the possibility to test and evaluate our technical solution before they agree to a commitment. The AR-OpenAccess solution is similar to the AR-Science solution and will automatically handle information needs and present the information to the users according to their requests. By first introducing the AR-OpenAccess for demos and test trial to the research community – we can reach more universities and libraries worldwide.
This project is completed - please contact us for more information.
What makes our solution innovative?
Within our product and services, we explore a new text mining technology to retrieve scientific publications better fitted to the information needs of students and researchers.
Part of the text mining technology was develop during our CEO Linda Andersson's PhD work at Technical University of Vienna (TU Wien), the paragraph retrieval, the automatic query formulation, and a merging method of document index and paragraph index.
The traditional search technology requires keywords, however, keywords are a limited representation of information needs. The future scientific knowledge management system ought to be a dialogue between the user and the system, requiring integration of language, scientific domain knowledge, and understanding of user information needs. Instead of requiring the user to convert her information need into a set of keywords, the system should aid the user to represent the information need on a conceptual level by means of user contexts. The text mining research must go towards categorizing the information needs and sub discipline of scientific fields. We argue that, by integrating an eLearning system and combining the benefits of supervised learning with reinforcement learning, we obtain partly self-learned annotated data that will boost the efficiency of customized solutions for each specific information need, and, at the same time, will provide users with added knowledge values
What is our solution technology?
The AR-Science software is integrated into the libraries existing system as an add- on and hosted on the libraries infrastructure. We provide the services of indexing in-house, open access, as well as closed access if the library has the text mining permission in the contract with the information providers. Our solution does not require full-text storage, only a digital fingerprint is stored, and the in-house and the closed publication indices, as well as the usage information is owned and hosted by the library. We provide services in terms of maintenance, index update, functionalities update and optimization - i.e. we make the searching different type of resources more time efficient for students and researchers, but the libraries have the control of the data resources storage and the usage information. AR-Science provide more precise usage information in comparison with COUNTER standard. For example, the libraries have access to search statistics such as open access versus closed publications for each specific scientific field
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