Beyond correlation: Bringing artificial intelligence to events data
John C. Mallery · International Interactions · 1994
The Feature Vector Editor offers a user‐extensible environment for exploratory data analysis. Several empirical studies have applied this environment to the SHERFACS International Conflict Management dataset. Current analysis techniques include boolean analysis, temporal analysis, and automatic rule learning. Implemented portably in ANSI Common Lisp and the Common Lisp Interface Manager (CLIM), the system features an advanced interface that makes it intuitive for people to manipulate data and discover significant relationships. The system encapsulates data within objects and defines generic protocols that mediate all interactions between data, users and analysis algorithms. Generic data protocols make possible rapid integration of new datasets and new analysis algorithms with heterogeneous data formats. More sophisticated research reformulates SHERFACS conflict codings as machine‐parsable narratives suitable for processing into semantic representations by the RELATUS Natural Language System. Experiments with 244 SHERFACS cases demonstrated the feasibility of building knowledge bases from synthetic texts exceeding 600 pages. Therefore, the Feature Vector Editor plus the RELATUS system allow operationally rigorous and manageable transitions between complex qualitative textual accounts and quantitatively oriented data representations.