Publications

• Teaching machines to understand data science code by semantic enrichment of dataflow graphs, 2018. Evan Patterson, Ioana Baldini, Aleksandra Mojsilovic, Kush R. Varshney.

  Abstract

  Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasingly asked to write and understand code. They would benefit greatly from intelligent tools that reveal the connections between their code and its subject matter. Towards this prospect, we present an AI system that forms semantic representations of computer programs, using techniques from knowledge representation and program analysis. These representations are created through a novel algorithm for the semantic enrichment of dataflow graphs. We illustrate its workings with examples and applications from the field of data science. The algorithm is undergirded by a new ontology language for modeling computer programs and a new ontology about data science, written in this language.

  We refine our method for creating semantic representations of data science programs. We implement the method for both Python and R and we also release the first version of the Data Science Ontology. The ontology is written in a new ontology language called Monocl, for which we give a categorical semantics.

  We will demo the software behind this work at IJCAI-ECAI 2018 and we will present a short paper at the 2018 KDD Fragile Earth Workshop: Theory Guided Data Science to Enhance Scientific Discovery.

  Full-length manuscript and software forthcoming.

• Knowledge representation in bicategories of relations, 2017. Evan Patterson.

  Abstract

  We introduce the relational ontology log, or relational olog, a knowledge representation system based on the category of sets and relations. It is inspired by Spivak and Kent's olog, a recent categorical framework for knowledge representation. Relational ologs interpolate between ologs and description logic, the dominant formalism for knowledge representation today. In this paper, we investigate relational ologs both for their own sake and to gain insight into the relationship between the algebraic and logical approaches to knowledge representation. On a practical level, we show by example that relational ologs have a friendly and intuitive—yet fully precise—graphical syntax, derived from the string diagrams of monoidal categories. We explain several other useful features of relational ologs not possessed by most description logics, such as a type system and a rich, flexible notion of instance data. In a more theoretical vein, we draw on categorical logic to show how relational ologs can be translated to and from logical theories in a fragment of first-order logic. Although we make extensive use of categorical language, this paper is designed to be self-contained and has considerable expository content. The only prerequisites are knowledge of first-order logic and the rudiments of category theory.

  BibTeX

  @article{patterson2017,
    title={Knowledge Representation in Bicategories of Relations},
    author={Patterson, Evan},
    archivePrefix={arXiv},
    eprint={1706.00526},
    journal={arXiv preprint},
    year={2017}
  }

  I explain how to represent relational knowledge in a mathematical structure called a bicategory of relations. I compare this structure to the more familiar systems of first-order logic and description logic. Despite the heavy dose of monoidal category theory, the paper is supposed to be readable by non-category-theorists. Many pictures and informal explanations are provided.

  I talked briefly about this project at the 2017 CSLI Workshop (slides) and later at the 2017 AMS Special Session on Applied Category Theory (slides).

• Dataflow representation of data analyses: Toward a platform for collaborative data science, 2017. Evan Patterson, Robert McBurney, Hollie Schmidt, Ioana Baldini, Aleksandra Mojsilovic, Kush R. Varshney.

  Abstract

  Data science plays an increasingly important role in solving today's scientific and social challenges. To promote progress toward a cure for multiple sclerosis, the Accelerated Cure Project has created an open repository of biological and survey data on patients with multiple sclerosis. Similar large-scale repositories are being created in other domains. As the open, data-driven model of science proliferates, the research community faces a growing need for a cloud platform for collaborative data science. Such a platform should facilitate collaboration between domain experts and data scientists and possess artificial intelligence capabilities for organizing, recommending, and manipulating data analyses. In this paper, we present some foundational technologies motivated by this vision. Our system automatically extracts a high-level dataflow graph from a data analysis. This graph describes how data flows through an analysis pipeline, including which statistical methods are used and how they fit together. The system requires no special annotations from the data analyst and consumes analyses written in Python using standard tools, such as Scikit-learn and Statsmodels. In this paper, we explain how our system works and how it fits into our larger vision for a collaborative data science platform.

  BibTeX

  @article{patterson2017,
    author={Evan Patterson and Robert McBurney and Hollie Schmidt and Ioana Baldini and Aleksandra Mojsilović and Kush R. Varshney},
    journal={IBM Journal of Research and Development},
    title={Dataflow representation of data analyses: Toward a platform for collaborative data science},
    volume={61},
    number={6},
    pages={9:1-9:13},
    doi={10.1147/JRD.2017.2736278},
    year={2017}
  }

  My collaborators and I argue for two theses:

  1. A cloud platform for data science, powered by artificial intelligence, could accelerate scientific discovery by helping data scientists and domain scientists collaborate. Existing data science platforms do not meet this need.

  2. To enable these AI capabilities, we must create useful, machine-interpretable representations of data analyses. We develop a first version of this technology (largely superseded by our later work).

  We presented a shorter paper (slides) at the 2017 AAAI Spring Symposium on Artificial Intelligence for the Social Good.