Stardog’s Inference Engine associates related information stored in disparate sources, and then uses this rich web of relationships to discover new relationships within your data. By inferring new connections between concepts in the Knowledge Graph, the resulting network of information becomes increasingly more valuable. Furthermore, this represents the breadth of organizational knowledge in a machine-understandable format. With knowledge digitized, Knowledge Graphs support better decision-making and faster time to insight.
Inference creates new relationships by interpreting your source data against your data model. By expressing all the implied relationships and connections between your data sources, you create a richer, more accurate view of your data. This includes the ability to represent multiple definitions for the same data, empowering collaboration between stakeholders and expressing situational or theoretical truths.
Capture your business and domain rules in the data model; the Inference Engine intelligently applies these rules at query time. Organizations often already have data models sitting in database schemas, data dictionaries, or Excel documents; Stardog centralizes these fragments into one data model that consists of business logic or declarative rules. This low-code centralized data model dramatically cuts down project-specific data preparation and stitch code.
Inference capabilities are unique to semantic (RDF) graphs and are a major differentiator to property graphs. One benefit of Stardog’s support of the RDF Graph open standards is there is a public ecosystem of data models that describe different domains. These data models are called ontologies or vocabularies and lay out common relationships between entities. Inference’s expressivity allows knowledge engineers to describe complex domains, such as medicine, in which multiple facts, axioms, and rules interact with each other to infer new facts.
Among providers of RDF graph, Stardog’s best-in-class Inference Engine has the most advanced capabilities on the market for processing complex ontologies. This allows customers to accelerate their Knowledge Graph development, while offering the flexibility to edit the data model to capture proprietary or unique needs.
Inference is also highly explainable in comparison to black box algorithms that don’t offer transparency into how results are derived. By using properties of our query rewriting reasoning algorithm, Stardog is able to present explanations of inferences in a tree-like form so the user can review the logic that led to their answer.
NIH’s Models for Infectious Disease Studies (MIDAS) Digital Commons facilitates collaborative epidemiological research to respond to disease outbreaks. Associating related research through an ontology allows researchers to search across a number of attributes, including pathogen type, host data, and disease forecasters. Users can now query over 700 mapped data sets, 62 indexed software applications, and over 200 data-related websites in 28 different formats.