  Recent developments in support for constructing knowledge graphs have led to
a rapid rise in their creation both on the Web and within organisations. Added
to existing sources of data, including relational databases, APIs, etc., there
is a strong demand for techniques to query these diverse sources of knowledge.
While formal query languages, such as SPARQL, exist for querying some knowledge
graphs, users are required to know which knowledge graphs they need to query
and the unique resource identifiers of the resources they need. Although
alternative techniques in neural information retrieval embed the content of
knowledge graphs in vector spaces, they fail to provide the representation and
query expressivity needed (e.g. inability to handle non-trivial aggregation
functions such as regression). We believe that a lingua franca, i.e. a
formalism, that enables such representational flexibility will increase the
ability of intelligent automated agents to combine diverse data sources by
inference.
  Our work proposes a flexible representation (alists) to support intelligent
federated querying of diverse knowledge sources. Our contribution includes (1)
a formalism that abstracts the representation of queries from the specific
query language of a knowledge graph; (2) a representation to dynamically curate
data and functions (operations) to perform non-trivial inference over diverse
knowledge sources; (3) a demonstration of the expressiveness of alists to
represent the diversity of representational formalisms, including SPARQL
queries, and more generally first-order logic expressions.
