Transactional Knowledge Graph Generation To Model Adversarial Activities

Conference · Wed Jan 12 23:00:00 EST 2022

DOI:https://doi.org/10.1109/BigData52589.2021.9672016· OSTI ID:1843141

Purohit, Sumit ^[1]; Mackey, Patrick S. ^[1]; Smith, William P. ^[1]; Dunning, Madelyn P. ^[1]; Orren, Miquette J. ^[1]; Langlie-Miletich, Trevor M. ^[1]; Deshmukh, Rahul D. ^[1]; Bohra, Ankur ^[1]; Martin, Tonya J. ^[1]; Aimone, Dan J. ^[1]; Chin, George ^[1]

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1. BATTELLE (PACIFIC NW LAB)

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.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1843141
• Report Number(s): PNNL-SA-167380
• Country of Publication: United States
• Language: English