Company or Institution

ConexusAI

Industry

Computer Software

Website

http://conexus.com

Country

United States

Sustainable Development Goals (SDGs)

General description of the AI solution

This proposal specifically addresses a key research problem to better address the needs of data scientists: how to make recommendations on knowledge graphs — which are “category algebras'', aka “categories'' of a special form — in a way that conforms to arbitrary given symbolic logical constraints specified by domain experts. For example, how to take a knowledge graph about cleaning products that may associate Chlorox and Formula 409, but not recommend that pairing for sale because bleach and ammonia are a toxic combination. Currently, data scientists have no such “safety net" and must account for domain rules manually during e.g. feature engineering, a tedious and error-prone process not known to even be possible outside of some particular special cases (e.g. transitive relationships) — a situation we aim to address with this proposal. That is, this research project will draw from the fields of knowledge graphs and graph neural networks in order to evaluate the technical feasibility of (machine) learning “graph embeddings'', that are guaranteed to respect (given) first-order logic constraints — and to apply these new techniques to graph learning projects across domains. In addition to obvious applications in e-tailing for product recommendations, this mechanisms can also be used to explicitly correct bias while learning over graph data.

Publications

https://arxiv.org/pdf/1102.1889.pdf

https://arxiv.org/abs/2001.00338

https://arxiv.org/abs/1903.10579

https://www.youtube.com/watch?v=bk36__qkhrk

https://www.categoricaldata.net/cql/Kensho-CategoricalDatabases_20190227.pdf

Needs