Detecting misinformation and knowledge conflicts in relational data

Georgiy Levchuk; Matthew Jackobsen; Brian Riordan

doi:10.1117/12.2050842

20 June 2014 Detecting misinformation and knowledge conflicts in relational data

Georgiy Levchuk, Matthew Jackobsen, Brian Riordan

Proceedings Volume 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII; 90910P (2014) https://doi.org/10.1117/12.2050842
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

Information fusion is required for many mission-critical intelligence analysis tasks. Using knowledge extracted from various sources, including entities, relations, and events, intelligence analysts respond to commander’s information requests, integrate facts into summaries about current situations, augment existing knowledge with inferred information, make predictions about the future, and develop action plans. However, information fusion solutions often fail because of conflicting and redundant knowledge contained in multiple sources. Most knowledge conflicts in the past were due to translation errors and reporter bias, and thus could be managed. Current and future intelligence analysis, especially in denied areas, must deal with open source data processing, where there is much greater presence of intentional misinformation. In this paper, we describe a model for detecting conflicts in multi-source textual knowledge. Our model is based on constructing semantic graphs representing patterns of multi-source knowledge conflicts and anomalies, and detecting these conflicts by matching pattern graphs against the data graph constructed using soft co-reference between entities and events in multiple sources. The conflict detection process maintains the uncertainty throughout all phases, providing full traceability and enabling incremental updates of the detection results as new knowledge or modification to previously analyzed information are obtained. Detected conflicts are presented to analysts for further investigation. In the experimental study with SYNCOIN dataset, our algorithms achieved perfect conflict detection in ideal situation (no missing data) while producing 82% recall and 90% precision in realistic noise situation (15% of missing attributes).

Citation Download Citation

Georgiy Levchuk, Matthew Jackobsen, and Brian Riordan "Detecting misinformation and knowledge conflicts in relational data", Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90910P (20 June 2014); https://doi.org/10.1117/12.2050842

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