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It is a common belief within the Intelligence Community (IC) that data residing in disparate information systems can be mined in useful ways by means of artificial intelligence (AI) and natural language processing (NLP) methods working alone, which is to say, without the aid of some kind of integrating framework. Here, in contrast, we argue that the sort of integration and analysis that is required if we are to connect data and information deriving from heterogeneous sources in useful ways needs semantic integration, in other words integration that rests on the ability to identify shared meanings across different bodies of data. To achieve such integration requires what we shall call an Integrating Semantic Framework (ISF). A framework of this sort is based on ontologies, which are controlled structured vocabularies designed to foster interoperability in the collection and curation of data and thereby to prevent the sorts of siloing of information that arise where there is inconsistency in the use of terms.

Philosophical foundations of intelligence collection and analysis: a defense of ontological realism