Simplifying License Information Through the Use of a Knowledge Graph

Understanding wireless spectrum allocation is a considerable challenge due to the complexity and volume of information. In the United States, the Federal Communications Commission (FCC) is the agency that grants and maintains information regarding all licenses. This information is fragmented across a number of individual databases, with the majority of information residing in the Universal Licensing System (ULS) database (DB). Within the ULS, gathering information is a challenging task, with the information related to a single license/callsign being distributed across multiple individual webpages. The FCC License View API only provides high level license information such as callsign, service category, status, FRN, and licensee name, but requires going to the ULS web-based system to get additional information such as locations, frequencies, etc. All in all, gathering information on Spectrum Regulation is complex and time consuming and there is a significant learning curve associated with understanding the different sources of information and thereafter the meaning of all the bits of fragmented information. Towards simplifying this process, and flattening the learning curve, in this paper, we present a method for modeling the FCC databases (DBs) licensing information as a knowledge graph and then implement the graph model in the neo4j graph database platform. A knowledge graph is a mechanism for capturing information in a structured fashion that allows us to map real-world objects (or concepts) onto a graph as nodes with links or paths between nodes representing relationships. Additionally, semantic information can be added to a knowledge graph which can result in inference of facts that are not readily apparent in any other structured ways of representing the same data. In our knowledge graph nodes represent various entities such as a callsign, a registrant, a particular frequency, an emission code etc. Relationships between nodes characterize how nodes relate to each other (ex: registrant OWNS a callsign; callsign USES a frequency), but in turn can carry meta-information that provides more insight into the relationship (ex: CALLSIGN uses this FREQUENCY with this ANTENNA at this LOCATION). Transitioning data into a knowledge graph and graph database also helps us extract categorical information (ex: emission code) to be modeled as separate nodes, resulting in simplified querying.Modeling information in the form of a knowledge graph has several advantages: 1) it enables the codification of implied relationships in denormalized data -- as compared to a search on the web based FCC interface, this means that information is readily available from multiple query entry points, instead of requiring a user to make multiple web requests to get subsequent information (ex: getting locations from callsign); 2) in turn the graphical, and therefore visual nature of the knowledge graph allows an individual to better understand the relationships between different pieces of data that are contained in the FCC DBs, and is therefore a valuable visualization tool and a learning aid; 3) it enables the storage and maintenance of categorical information within the database itself -- there are a number of data fields in the FCC databases that contain categorical information that presently require an individual to study the meaning of coded information separately (ex: an emissions designator such as 11K03FE has a specific meaning and contains information on the bandwidth, modulation, and other parameters); 4) other data that is traditional modeled as regular property or attributes of an entity can also be expressed in categorical fashion (ex: zipcode) allowing for much more directed queries beginning at those categorical data entry points (zipcode is also mapped into regional information such as county, state, country); 5) through the spatial capability of neo4j, and through the use of geographical overlay, it allows us to conduct geographical queries (ex: all licensees of a radio service type within a 20 mile radius of a given location); and 6) the knowledge graph and neo4j database presents the opportunity to conduct queries that may present interesting information of interest to the policy community (ex: which consultant has filed the most applications for which client entity), this sort of data gathering would be extremely time consuming in the web-based FCC DB searches. We expect that the community will find many interesting uses of the dataset and knowledge graph that we havenâ€™t even remotely envisioned.We present the results of modeling a subset of the FCC DBs information that we have downloaded. Our data is primarily based on the FCC License View dataset, which contains information from the following databases: Cable Operations and Licensing System (COALS), Consolidated Database System (CDBS), Experimental Licensing System (ELS), International Bureau Filing System (IBFS) and Universal Licensing System (ULS). We have 19.4 million records total, of which 8.3 million records are associated with active records. This amounts to a total of 2.29 million active licenses, with the vast majority, 2.2 million active licenses in the FCC ULS DB. We present work in a pilot/validation phase and are seeking to incorporate the feedback of the research community into further refining this model before releasing a production version. We present a few examples of queries that are possible on our knowledge graph, which will demonstrate its usage and the possibilities for researchers. The pilot system will be made available to the TPRC community prior to the main conference. Our system is a significant advance towards making information regarding spectrum, which is a public resource, and spectrum usage, more accessible to the public.