An Artificial Intelligence Based Assistant for Research, Collaboration, Publishing, and Policy Making
Create your account, and start completing your profile by adding or claiming your papers. Each user is able to see a full list of articles that he/she likes, has bookmarked, has published, or even draft of unpublished articles. Your profile grows as you show interest in articles retireved by the recommender
Search and access full text of literature in your digital library collection or Open Access resources. Supports multi languages including English, French, German, Spanish, Arabic, Persian, Japanese, Chinese etc. Read articles, book mark, and like them
Provide any combination of Title, Abstract, or Keywords and receive valuable recommendations. Authors to connect to, Keywords for your paper, Subject field of your article, and other Papers to read. Add draft of your article and get recommendation on which journal to publish with or which conference to submit to
Top Fields by Country, Top Organizations by Field, Publications by Field, Publications by Author, Publications by Organization, Top Countries by Field, Publications by Country
iKNiTO REC sits on top of a very large pool of research and academic data. The system is able to detect plagiarism extremely well. It uses intelligent similarity measures, considers abstracts and fulltext, and ranks the level of similarity. It works for different languages
iKNiTO REC works with a large volume of qualified data. We have a supporting product whose task is to read all sorts of documents in image and PDF formats and recognize the text content. It then uses its AI-based components to fully detect all metadata and enrich your knowledge base by adding your valuable archived and preserved data
Like no Other Product!
iKNiTO REC is a complex multi-language project which uses Artificial Inteligence, Natural Language Processing, and Deep Learning combined with a large volume of qualified data in order to come up with accurate recommendations.
Researchers who require a better understanding of what is happening in their field, authors who may need help in bringing their papers to a higher standard, and to get advice on where to publish, decisions makers at all levels who need to see the bigger picture in their institutions, country or world to compare and make better decisions, owners of Big Data and National bodies in charge of research are invited to collaborate with us.
As a demo version, we have trained iKNiTO REC on part of Microsoft Academic Graph (MAG). You can find out more information about MAG through here.
Recommendations You Need
With some limited information such as Title, or Abstract, or Keywords or any combination of them, you can receive the following recommendations
It returns a list of best suited journals to publish in or conferences to submit your paper
Connect with other researchers in your field. Find the best researchers in a specified field
Suggests the most probable keywords according to the title/abstract. Retrieve relevant articles by recommended keywords
Identify the subject or topic category of an article
Harvest the most related papers to your field of research. Make reference review more accessible
Graph of Science
iKNiTO REC can show 2D and 3D graphical views of activities of an author. The graph is fully interactive and can be viewed from different angles and by various filters. Thus, giving a 360 degree view of a researcher’s activities and in a wider context.
Generate reports based on your requirement. We currently support the following reports:
- Top Fields by Country
- Top Organizations by Field
- Publications by Field
- Publications by Author
- Publications by Organization
- Top Countries by Field
- Publications by Country
iKNiTO REC uses Natural Language Processing (NLP) and Deep Learning.
NLP forms the heart of our recommender. It works on language detection, text pre-processing, summarization and keyword extraction, document classification, and text similarity.
However, what makes the recommender very unique is its Deep Learning process. It continuously tracks your published papers, your search activities, likes and bookmarks, and even your co-authors’ activities. As a result it is able to give very useful and credible recommendation which become more efficient as the recommender learns from your reactions.
Still Not Convinced?
iKNiTO REC needs big and clean data to be trained on. This is the most important element for its success.
We have currently trained iKNiTO REC by a portion of data in MAG and it is magic! MAG is updated regularly. At a point in time it had information about almost 220,000,000 papers, over 240,000 authors, over 660,000 topics or subjects, more than 4,300 conferences, over 48,000 journals, and more than 25,000 institutions.
iKNiTO REC is a project which requires collaboration of owners of big data. They include:
- Major publishers of A&I databases and Web Scale Discovery (WSD) engines
- National or regional authorities in charge of country wide or large sale research and knowledge development.
While information about internationally published works are more readily available, there is less availability when local languages and articles published in a country are examined. Our Meta Data Extractor is able to read those documents in image or PDF formats, recognize their various parts and extract their metadata to add to your graph of science and learn from them.
Ready when you are …