pulls these features from the identified records and adds them to each node as applicable. Each feature may represent a dimension on which the data graph may be visualized or interacted with. For example, a time feature may allow nodes of the data graph to be filtered, queried, or otherwise manipulated based on time. Similarly, a stage feature may allow nodes of the data graph to be filtered, queried, or otherwise manipulated based on stage (for example, pre-production, procedure, assembly, shipping, or the like). [0036] As described above, the data graph created by “walkingthe tree”includes information explicitly recorded for a specified entity. Accordingly, at block 405, the data graph created by the electronic processor 300 includes linked information created from the explicit linkages between data records for the entity. Thus, the data included in the data graph is limited to the data required or elected by the organizations and individuals providing the data, and, thus, is limited in terms of features and, subsequently, dimensions. [0037] Therefore, to expand the original data graph, at block 410, the electronic processor 300 (executing the expansion software 320) determines (infers) one or more additional dimensions for the data graph based on data included in the data graph. The electronic processor 300 may use one or more inference algorithms or rules to infer the additional dimensions. For example, the electronic processor 300 may use a rules-based method that recognizes the most common data formats used to represent various data types, such as geographic coordinates or time and, as described below, look up other data sets including information related to an identified data type that can be linked based on the match. Alternatively or in addition, the electronic processor 300 may use an inference algorithm that predicts the probability of an entity having certain properties, such as temperature, identity, date and time, or the like, to identify inferred data. Such an inference algorithm may be trained using machine learning methods or could use linear interpolation or other forms of interpolation that generate interpolated values that can be used to look up inferred data. For example, as noted above, a node in a data graph may include geographic location information as one feature. Thus, the electronic processor 300 may be configured to recognize the geographical location information in a node of the data graph and identify additional data sources included in the plurality of data sources 115 that store additional information associated with the location. The inferred additional dimension may include weather information (temperature) associated with a location, traffic information associated with a location, or a socioeconomic status associated with a location, such as civil or political unrest, wage or labor laws, news articles or events, or worker satisfaction in a location (or a specific facility).