February 4, 2022
Conference Paper

Transactional Knowledge Graph Generation To Model Adversarial Activities


A Knowledge Graph (KG) is a formal and structured representation of facts, relationships, and semantic descriptions of a set of entities. Traditionally, KGs are used to describe metadata about entities and to provide additional context to target application results. Many real-world domains also involve temporal interactions between entities in addition to the metadata data. Modeling these attributed transactions is a critical requirement when using KGs in complex real-world applications. Modeling adversarial activities is one such application that develops methodology and tools to produce realistic large-scale background activity graphs that include embedded Weapons of Mass Destruction (WMD) activity patterns. We present a novel platform for constructing a transactional knowledge graph from a diverse set of sources. We present the core components and architecture of the framework, and a use case for generating a background knowledge graph and WMD activity template to evaluate network alignment and subgraph matching algorithms.

Published: February 4, 2022


Purohit S., P.S. Mackey, W.P. Smith, M.P. Dunning, M.J. Orren, T.M. Langlie-Miletich, and R.D. Deshmukh, et al. 2022. Transactional Knowledge Graph Generation To Model Adversarial Activities. In IEEE International Conference on Big Data (Big Data 2021), December 15-18, 2021, Orlando, FL, 2662-2671. Piscataway, New Jersey:IEEE. PNNL-SA-167380. doi:10.1109/BigData52589.2021.9672016

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