A simple linked data tool
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.



Build Status

EDN-LD is a set of conventions and a library for working with Linked Data (LD) using Extensible Data Notation (EDN) and the Clojure programming language. EDN-LD builds on EDN and JSON-LD, but is not otherwise affiliated with those projects.

Try EDN-LD online!

This project is in early development!

Linked Data

Linked data is an approach to working with data on the Web:

  • instead of tables we have graphs -- networks of data
  • instead of rows we have resources -- nodes in the graph
  • the values in our cells are also nodes -- either resources or literals: strings, numbers, dates
  • and instead of columns we have named relations that link nodes to form the graph

Just think of your tables as big sets of row-column-cell "triples". By switching from rigid tables to flexible graphs, we can easily merge data from across the web.

Linked data is simple. The tools for working with it are powerful: big Java libraries such as Jena, Sesame, OWLAPI, etc. Unfortunately, most of the tools are not simple.

EDN-LD is a simple linked data tool.


EDN-LD is a Clojure library. The easiest way to get started is to use Leiningen and add this to your project.clj dependencies:

[edn-ld "0.2.2"]


Try out EDN-LD with our interactive online tutorial, or by cloning this project and starting a REPL:

$ git clone https://github.com/ontodev/edn-ld.git
$ cd edn-ld
$ lein repl
nREPL server started ...
user=> (use 'edn-ld.core 'edn-ld.common)
user=> (require '[clojure.string :as string])
user=> "Ready!"

Say we have a (very small) table of books and their authors called books.tsv:

Title Author
The Iliad Homer

A common way to represent this in Clojure is as a list of maps, with the column names as the keys. We can slurp and split the data until we get what we want:

user=> (defn split-row [row] (string/split row #"\t"))
user=> (defn read-tsv [path] (->> path slurp string/split-lines (drop 1) (mapv split-row)))
user=> (def rows (read-tsv "test-resources/books.tsv"))
user=> rows
[["The Iliad" "Homer"]]

Now we use zipmap to associate keys with values:

user=> (def data (mapv (partial zipmap [:title :author]) rows))
user=> data
[{:title "The Iliad", :author "Homer"}]

We have the data in a convenient shape, but what does it mean? Well, there's some resource that has "The Iliad" as its title, and some guy named "Homer" who is the author of that resource. We also know from the context that it's a book.

The first thing to do is give names to our resources. Linked data names are IRIs: globally unique identifiers that generalize the familiar URL you see in your browser's location bar. We can use some standard names for our relations from the Dublin Core metadata standard, and we'll make up some more.

Name IRI
title http://purl.org/dc/elements/1.1/title
author http://purl.org/dc/elements/1.1/author
The Iliad http://example.com/the-iliad
Homer http://example.com/Homer
book http://example.com/book

IRIs can be long and cumbersome, so let's define some prefixes that we can use to shorten them:

Prefix IRI
dc http://purl.org/dc/elements/1.1/
ex http://example.com/

The ex prefix will be our default. We use strings for full IRIs and keywords when we're using some sort of contraction.

IRI Contraction
http://purl.org/dc/elements/1.1/title :dc:title
http://purl.org/dc/elements/1.1/author :dc:author
http://example.com/the-iliad :the-iliad
http://example.com/Homer :Homer
http://example.com/book :book

We'll put this naming information in a context map:

user=> (def context {:dc "http://purl.org/dc/elements/1.1/", :ex "http://example.com/", nil :ex, :title :dc:title, :author :dc:author})

The nil key indicates the default prefix :ex. Now we can use the context to expand contractions and to contract IRIs:

user=> (expand context :title)
user=> (expand context :Homer)
user=> (contract context "http://purl.org/dc/elements/1.1/title")
user=> (contract context "http://purl.org/dc/elements/1.1/foo")
user=> (expand-all context data)
[{"http://purl.org/dc/elements/1.1/title" "The Iliad", "http://purl.org/dc/elements/1.1/author" "Homer"}]

Sometimes we also want to resolve a name to an IRI. We can define a resources map from string to IRIs or contractions:

user=> (def resources {"Homer" :Homer, "The Iliad" :the-iliad})

We should include this information in our data by assigning a special :subject-iri to each of our maps. We can do this one at a time with assoc:

user=> (def book (assoc (first data) :subject-iri :the-iliad))
user=> book
{:title "The Iliad", :author "Homer", :subject-iri :the-iliad}

Or we can use a higher-order function to find the title from the resources map:

user=> (def books (mapv #(assoc % :subject-iri (get resources (:title %))) data))
user=> books
[{:title "The Iliad", :author "Homer", :subject-iri :the-iliad}]

Now it's time to convert our book data to "triples", i.e. statements about things to put in our graph. A triple consists of a subject, a predicate, and an object:

  • the subject is the name of a resource: an IRI
  • the predicate is the name of a relation: also an IRI
  • the object can either be an IRI or literal data.

We represent an IRI with a string, or a contracted IRI with a keyword. We represent literal data as a map with special keys:

  • :value is the string value ("lexical value") of the data, e.g. "The Iliad", "100.31"
  • :type is the IRI of a data type, with xsd:string as the default
  • :lang is an optional language code, e.g. "en", "en-uk"

The literal function is a convenient way to create a literal map:

user=> (literal "The Iliad")
{:value "The Iliad"}
user=> (literal 100.31)
{:value "100.31", :type :xsd:float}

The objectify function takes a resource map and a value, and determines whether to convert the value to an IRI or a literal:

user=> (objectify resources "Some string")
{:value "Some string"}
user=> (objectify resources "Homer")

Now we can treat each map as a set of statements about a resources, and triplify it to a lazy sequence of triples. The format will be "flat triples", a list with slots for: subject, predicate, object, type, and lang.

The triplify function takes our resource map and a map of data that includes a :subject-iri key. It returns a lazy sequence of triples.

user=> (def triples (triplify resources book))
user=> (vec triples)
[[:the-iliad :title {:value "The Iliad"}] [:the-iliad :author :Homer]]

You'll notice that the subject :the-iliad is repeated here. With a larger set of triples the redundancy will be greater. Instead we can use a nested data structure:

user=> (def subjects (subjectify triples))
user=> subjects
{:the-iliad {:title #{{:value "The Iliad"}}, :author #{:Homer}}}

From the inside out, it works like this:

  • object-set: the set of object with the same subject and predicate
  • predicate-map: a map from predicate IRIs to object sets
  • subject-map: map from subject IRIs to predicate sets

We work with these data structures like any other Clojure data, using merge, assoc, update, and the rest of the standard Clojure toolkit:

user=> (def context+ (merge default-context context))
user=> (def subjects+ (assoc-in subjects [:the-iliad :rdf:type] #{:book}))
user=> (def triples+ (conj triples [:the-iliad :rdf:type :book]))

Now, we can write to standard linked data formats, such as Turtle:

user=> (def prefixes (assoc (get-prefixes context) :rdf rdf :xsd xsd))
user=> (def expanded-triples (map #(expand-all context+ %) triples+))
user=> (edn-ld.jena/write-triple-string prefixes expanded-triples)
@prefix dc:    <http://purl.org/dc/elements/1.1/> .
@prefix ex:    <http://example.com/> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
ex:the-iliad  a    ex:book ;
        dc:author  ex:Homer ;
        dc:title   "The Iliad"^^xsd:string .

One more thing before we're done: named graphs. A graph is just a set of triples. When we want to talk about a particular graph, we give it a name: an IRI, of course. Then we can talk about sets of named graphs when we want to compare them, merge them, etc. The official name for a set of graphs is an "RDF dataset". A dataset includes "default graph" with no name.

By adding the name of a graph, our triples become quads ("quadruples"). We define a quad and some new functions to handle them.

user=> (def library [(assoc book :graph-iri :library)])
user=> library
[{:title "The Iliad", :author "Homer", :subject-iri :the-iliad, :graph-iri :library}]
user=> (def quads (quadruplify-all resources library))
user=> (vec quads)
[[:library :the-iliad :title {:value "The Iliad"}] [:library :the-iliad :author :Homer]]
user=> (graphify quads)
{:library {:the-iliad {:title #{{:value "The Iliad"}}, :author #{:Homer}}}}


  • Conference paper about EDN-LD (PDF, source)

Change Log

  • 0.2.1
    • fix bug in edn-ld.jena/make-node
  • 0.2.0
    • use Apache Jena for reading and writing
    • fix triplify functions to use :subject-iri key
    • add quadruplify and graphify functions, using :graph-iri key
    • rename squash functions to flatten
    • fix flatten functions
    • many more unit tests
    • prefer Triples to FlatTriples
  • 0.1.0
    • first release

To Do

  • finish streaming RDFXML reader and writer
  • ClojureScript support? Would require different libraries for reading and writing


Copyright © 2015 James A. Overton

Distributed under the BSD 3-Clause License.