ASME

Journal of Computing and Information Science in Engineering

Engineering Knowledge Graph From Patent Database

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Abstract

We propose a large, scalable engineering knowledge graph, comprising sets of real-world engineering “facts” as < entity, relationship, entity > triples that are found in the patent database. We apply a set of rules based on the syntactic and lexical properties of claims in a patent document to extract facts. We aggregate these facts within each patent document and integrate the aggregated sets of facts across the patent database to obtain an engineering knowledge graph. Such a knowledge graph is expected to support inference, reasoning, and recalling in various engineering tasks. The knowledge graph has a greater size and coverage in comparison with the previously used knowledge graphs and semantic networks in the engineering literature.

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June 18, 2024 Spotlight: “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset”

A recording is now available on Youtube for the June 18, 2024 Spotlight talk by Professor Christian Soize (Université Gustave Eiffel) on his paper “Updating Nonlinear Stochastic Dynamics of an Uncertain Nozzle Model Using Probabilistic Learning With Partial Observability and Incomplete Dataset,”

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