Iris.ai is building a set of innovative artificial intelligence-tools for chemical research. Using the latest breakthroughs in text understanding these tools will allow researchers to automatically do what today not only needs to be done manually, but often is so tedious and time consuming it can not be done. These tasks include identifying novel application areas for existing compounds from scanning millions of research papers and patents, both finding applications that are described directly and finding applications that can be inferred from several sources. The key R&D challenge to achieve this is to develop an artificial intelligence algorithmic core engine within natural language understanding, mainly concerning understanding similarity, compositionality, causality and ranking metrics. More specifically, the research challenges for this project is to research and develop domain-specific knowledge discovery with context aware word-embeddings as well as domain specific entity embeddings. The engine should be able to build unique representation of the provided chemical element, link it to existing written knowledge available (patents, science articles, etc.) about the element or similar such elements and finally organize that knowledge into application areas and presented it to the user. Additionally we will extend the functionality of that engine to be able to infer application areas that are not explicitly derived from the literature, but are linked based on linkages in between connected elements in the body of knowledge. We will verify those objectives in close collaboration with clients from the Chemical Industry, which will provide us with an Ontology of interest, and examples from their day-to-day work. We will also use available public open access repositories of Chemistry related textual information and elements, molecules and compounds registries and databases for validating the embeddings space.
Project leader: Victor Botev
Institution: IRIS AI AS