Contradictory Ambiguous Revocable Assertion Tracker (CARAT) Encoding

How data is encoded in a knowledge graph directly influences what can be done with that data. A common problem with many encodings is that they have difficulty representing ambiguity and evolution inherent in many real-world data sets. The data encoding represented in this paper (called CARAT) is a graph-level description of our attempt to capture data that is contradictory, ambiguous and evolves over time (including deleting information). The data encoding relies on tracking assertions about subjects rather than directly tracking states. This encoding decision resolves many issues our team had experienced using other data encodings but produces a a larger graph. This is a preliminary presentation of our CARAT data encoding and our experience using it.

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Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825711
Primary Topic
Topic Modeling
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article
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article

Contradictory Ambiguous Revocable Assertion Tracker (CARAT) Encoding

Joseph A. Cottam, Sumit Purohit, Patrick Mackey, George Chin
Topic Modeling
article

Contradictory Ambiguous Revocable Assertion Tracker (CARAT) Encoding

Joseph A. Cottam, Sumit Purohit, Patrick Mackey, George Chin
article en

Abstract

How data is encoded in a knowledge graph directly influences what can be done with that data. A common problem with many encodings is that they have difficulty representing ambiguity and evolution inherent in many real-world data sets. The data encoding represented in this paper (called CARAT) is a graph-level description of our attempt to capture data that is contradictory, ambiguous and evolves over time (including deleting information). The data encoding relies on tracking assertions about subjects rather than directly tracking states. This encoding decision resolves many issues our team had experienced using other data encodings but produces a a larger graph. This is a preliminary presentation of our CARAT data encoding and our experience using it.

Pacific Northwest National Laboratory (US)
Openalex Percentile: Top 77%
Topic Modeling
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