Current Projects
Automating Data Science
Developing technology to automate the creation of machine learning pipelines to solve a wide variety of data driven modeling problems.
Causal Reasoning
A novel knowledge organization system that integrates concepts of causality, factual knowledge and meta-reasoning into a model-driven knowledge graph representation to provide situational awareness.
Commonsense Reasoning
A Multi-modal Open World Grounded Learning and Inference project to build a system that can answer a wide range of common sense questions posed using either an image or natural language, about everyday intuitive phenomena such as abduction, analogy, causality, agency, physics, and social interactions.
Datamart
Creating the largest publicly available knowledge graph to power data-driven models in a wide variety of domains.
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts. We investigate whether and to what extent LLMs can be used for TKG forecasting using in-context learning (ICL).
Integrating Scientific Models
Model Integration through Knowledge-Rich Data and Process Composition.
Karma
An information integration tool that enables users to quickly and easily integrate data from a variety of data sources including databases, spreadsheets, delimited text files, XML, JSON, KML and Web APIs.
Knowledge Graph for Business
Creating a public resource containing knowledge about businesses, their products, and their patents as well as the relationships between them, such as customer, competitor or supplier.
Knowledge Graph Toolkit
Building a comprehensive library of tools.
Linked Maps
Exploiting Context in Cartographic Evolutionary Documents to Extract and Build Linked Spatial-Temporal Datasets.
MARVEL
MARVEL is a multidimensional AVR benchmark with 770 puzzles composed of six core knowledge patterns, geometric and abstract shapes, and five different task configurations.
Scoring Scientific Research
Developing automated techniques for evaluating scientific claims and assessing the confidence of their reproducibility and applicability.
Semantic Modeling
Automatically building semantic descriptions of sources.
Table Understanding
Extracting and Interpreting Time Series for Causal Discovery
