Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine.
We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts.
Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task.
We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.
To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery.
We also demonstrate an approach for displaying information about authors, boosting the ability to understand the work of new, unfamiliar scholars.
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes.
Determining coreference of concept mentions across multiple documents is a fundamental task in natural language understanding.
Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations.
The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge.
The COVID-19 pandemic has sparked unprecedented mobilization of scientists, generating a deluge of papers that makes it hard for researchers to keep track and explore new directions.
While natural language understanding (NLU) is advancing rapidly, today's technology differs from human-like language understanding in fundamental ways, notably in its inferior efficiency, interpretability, and generalization.
We present a deep learning system for testing graphics units by detecting novel visual corruptions in videos.
For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge.
By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine learning models in a cost-effective way.
The availability of large idea repositories (e. g., the U. S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems.
We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information.