[0049] FIG. 5 illustrates an example user interface displaying a visualization of a data graph  such as a provenance chain. A user may select a node with the data graph (for example  via an input device) to view additional information of the node (features of the node). Furthermore  a user can query or filter on these features. For example  in FIG. 5  a user can query or filter on an entity identifier (stock keeping unit (SKU)) 505  a stage identifier 510 (for example  manufacturing  packaging  and assembly)  a supplier identifier 515  and a date range 520. As noted above  in some embodiments  additional features are available for use in querying  filtering  and pivoting that were identified through the expansion process  such as  for example  country of origin  weather  traffic  and the like. Accordingly  using this expanded information  a user can query on a particular country  manufacturing facility  temperature  air quality  employee satisfaction  and the like. Also  in some embodiments  a user can access information regarding the source of particular data included in the provenance chain  such as viewing whether information was included in the original provenance chain or was inferred and a source where the data (inferred or otherwise) was retrieved or generated from (including  for example  details of equipment used to by the source  a location of the source  a certification level of the source  the date the source was last accessed or last updated  an algorithm used to generate the data). For example  when the provenance chain includes temperature information  details regarding the sensors used to detect the temperatures (make or model) and the location of such sensors may be accessible through the provenance chain. [0050] Accordingly  embodiments described herein create navigable data graphs  such as provenance chains  that  rather than being limited to arbitrary spaces or dimensions that are limited based on direct data recorded for an entity  are dynamic and include expanded information from various  often disconnected  data sources to improve the completeness and usefulness of the data graph to a end user. For example  when a provenance chain relates to a product  an expanded version of this chain generated using the methods and systems described herein may allow a user to not only trace a detected contamination back to source but also identify other products that may have been effected as well as identify causes for the contamination  such as particular weather conditions  working conditions  or the like. In particular  as described above  the systems and methods identify various dimensions that a data graph may be rendered in or related to and collect the information needed to provide such a rendering from one or more data sources. These relationships (dimensions) are stored with the data graph  which allows a user to quickly and efficiently (in terms of computing resource and bandwidth usage) query  filter  pivot  and generally