Datalog based rules engine.
Naga allows users to load data, and define rules to entailed new data. Once rules have been executed, the database will be populated with new inferences which can be queried.
Naga can use its internal database, or wrap an external graph database. A command line utility to demonstrate Naga will load rules, into memory, run them, and print all the inferred results.
To run Naga on a Datalog script, provide the script via stdin or in a filename argument.
While Naga is still in development, the easiest way to run it is with the Leiningen build tool.
lein run pabu/family.lg
This runs the program found in the family.lg
file. It loads the data specified in the file,
executes the rules, and finally prints the final results database, without the input data.
--init
Initialization data for the configured storage.--json
Input path/url for JSON to be loaded and processed.--out
Output file when processing JSON data (ignored when JSON not used).--uri
URI describing a database to connect to. (default: mem
. Datomic supported).The language being implemented is called "Pabu" and it strongly resembles Prolog. It is simply a series of statements, which are of two types: assertions and rules.
To declare facts, specify a unary or binary predicate.
man(fred).
friend(fred,barney).
The first statement declares that fred
is of type man
. The second declares that fred
has
a friend
who is barney
.
Nothing needs to be declared about man
or friend
. The system doesn't actually care what
they mean, just that they can be used in this way. The use of these predicates is all the
declaration that they need.
Rules are declared in 2 parts.
head :- body .
The body of the rule, defines the data that causes the rule to be executed. This is a comma separated series of predicates, each typically containing one or more variables. The predicate itself can also be variable (this is very unusual in logic systems).
The head of the rule uses some of the variables in the body to declare new information that the rule will create. It is comprised of a single predicate.
Variables are words that begin with a capital letter (yes, Prolog really does look like this).
Here is a rule that will infer an uncle
relationship from existing data:
uncle(Nibling,Uncle) :- parent(Nibling,Parent), brother(Parent,Uncle).
In the above statement, Nibling, Parent, and Uncle are all variables. Once variables
have been found to match the predicates after the :- symbol, then they can be substituted
into the uncle
predicate in the head of the rule.
Both assertions and rules end with a period.
Pabu (and Prolog) uses "C" style comments:
/* This is a comment */
Any element can be given a namespace by using a colon separator. Only 1 colon may appear in an identifier.
owl:SymmetricProperty(sibling).
To see this in use, look in pabu/family-2nd-ord.lg, and try running it:
lein run pabu/family-2nd-ord.lg
Naga defines a data access API to talk to storage. This is a Clojure protocol or Java interface
called Storage
, found in naga.store
. It should be possible to wrap most graph database APIs
in the Storage
API.
For the moment, the only configured implementation is an in-memory store.
Naga is designed to operate against any graph database. The interface for this is the Storage
protocol described above, and is defined in the project naga-store.
Implementations of this protocol exist for Datomic, and a local
in-memory graph database called Asami. Asami is used
by default. Asami has a relatively capable query planner, and internal operations for inner joins and projection.
More operations are in the works.
Queries may be executed directly against the database, but for the moment they require API access.
We also have some partial implementations for on-disk storage, which we hope to use. These are based on the same architecture as the indexes in the Mulgara Database.
Copyright © 2016-2019 Cisco Systems
Copyright © 2011-2016 Paula Gearon
Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.
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