3 May 2015

SPARQL: the video

Well, a video, but a lot of important SPARQL basics in a short period of time.

SPARQL in 11 minutes

While doing training for a TopQuadrant customer recently, the schedule led to my having ten minutes to explain the basics of writing SPARQL queries. I think I did OK, but on the plane home I thought harder about what to put in those ten minutes, which led to my making the video SPARQL in 11 minutes. While the video is 11 minutes and 14 seconds long, between the opening part about RDF and the plug for Learning SPARQL at the end, the SPARQL introduction is less than eight minutes.

After explaining what RDF triples are and how they're represented in Turtle, the video walks through some simple SELECT queries and how they work with the data. This leads up to a CONSTRUCT query and a list of other things that people will find useful if they learn more about SPARQL. I had a lot of fun making the video's SPARQL engine noise with my Korg Monotron synthesizer and also making more traditional music for the introduction and ending.

I hope this video is helpful for people who are new to SPARQL. The other SPARQL videos on YouTube are mostly real-time classroom lectures. My favorite is an ad for what seems like a Dutch cable TV provider that has nothing to do with the query language but has the excellent domain name sparql.nl. If you skip ahead to 1:03 of this ad for the company, you'll see a finger snap turn into a swirl of flames and then their shining "sparql" logo, all with the most dramatic music possible. My production values were not quite that high, but higher than most of the other SPARQL videos you'll find on YouTube.

some description

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12 April 2015

Running Spark GraphX algorithms on Library of Congress subject heading SKOS

Well, one algorithm, but a very cool one.

GraphX LoC SKOS logos

(This blog entry has also been published on the databricks company blog.)

Last month, in Spark and SPARQL; RDF Graphs and GraphX, I described how Apache Spark has emerged as a more efficient alternative to MapReduce for distributing computing jobs across clusters. I also described how Spark's GraphX library lets you do this kind of computing on graph data structures and how I had some ideas for using it with RDF data. My goal was to use RDF technology on GraphX data and vice versa to demonstrate how they could help each other, and I demonstrated the former with a Scala program that output some GraphX data as RDF and then showed some SPARQL queries to run on that RDF.

Today I'm demonstrating the latter by reading in a well-known RDF dataset and executing GraphX's Connected Components algorithm on it. This algorithm collects nodes into groupings that connect to each other but not to any other nodes. In classic Big Data scenarios, this helps applications perform tasks such as the identification of subnetworks of people within larger networks, giving clues about which products or cat videos to suggest to those people based on what their friends liked.

The US Library of Congress has been working on their Subject Headings metadata since 1898, and it's available in SKOS RDF. Many of the subjects include "related" values; for example, you can see that the subject Cocktails has related values of Cocktail parties and Happy hours, and that Happy hours has related values of Bars (Drinking establishments), Restaurants, and Cocktails. So, while it includes skos:related triples that indirectly link Cocktails to Restaurants, it has none that link these to the subject of Space stations, so the Space stations subject is not part of the same Connected Components subgraph as the Cocktails subject.

After reading the Library of Congress Subject Header RDF into a GraphX graph and running the Connected Components algorithm on the skos:related connections, here are some of the groupings I found near the beginning of the output:

"Hiding places" 
"Secrecy" 
"Loneliness" 
"Solitude" 
"Privacy" 
--------------------------
"Cocktails" 
"Bars (Drinking establishments)" 
"Cocktail parties" 
"Restaurants" 
"Happy hours" 
--------------------------
"Space stations" 
"Space colonies" 
"Large space structures (Astronautics)" 
"Extraterrestrial bases" 
--------------------------
"Inanna (Sumerian deity)" 
"Ishtar (Assyro-Babylonian deity)" 
"Astarte (Phoenician deity)" 
--------------------------
"Cross-cultural orientation" 
"Cultural competence" 
"Multilingual communication" 
"Intercultural communication" 
"Technical assistance--Anthropological aspects" 
--------------------------

(You can find the complete output here, a 565K file.) People working with RDF-based applications already know that this kind of data can help to enhance search. For example, someone searching for media about "Space stations" will probably also be interested in media filed under "Space colonies" and "Extraterrestrial bases". This data can also help other applications, and now, it can help distributed applications that use Spark.

Storing RDF in GraphX data structures

First, as I mentioned in the earlier blog entry, GraphX development currently means coding with the Scala programming language, so I have been learning Scala. My old friend from XML days Tony Coates wrote A Scala API for RDF Processing, which takes better advantage of native Scala data structures than I ever could, and the banana-rdf Scala library also looks interesting, but although I was using Scala my main interest was storing RDF in Spark GraphX data structures, not in Scala particularly.

The basic Spark data structure is the Resilient Distributed Dataset, or RDD. The graph data structure used by GraphX is a combination of an RDD for vertices and one for edges. Each of these RDDs can have additional information; the Spark website's Example Property Graph includes (name, role) pairs with its vertices and descriptive property strings with its edges. The obvious first step for storing RDF in a GraphX graph would be to store predicates in the edges RDD, subjects and resource objects in the vertices RDD, and literal properties as extra information in these RDDs like the (name, role) pairs and edge description strings in the Spark website's Example Property Graph.

But, as I also wrote last time, a hardcore RDF person would ask these questions:

  • What about properties of edges? For example, what if I wanted to say that an xp:advisor property was an rdfs:subPropertyOf the Dublin Core property dc:contributor?

  • The ability to assign properties such as a name of "rxin" and a role of "student" to a node like 3L is nice, but what if I don't have a consistent set of properties that will be assigned to every node—for example, if I've aggregated person data from two different sources that don't use all the same properties to describe these persons?

The Example Property Graph can store these (name, role) pairs with the vertices because that RDD is declared as RDD[(VertexId, (String, String))]. Each vertex will have two strings stored with it; no more and no less. It's a data structure, but you can also think of it as a proscriptive schema, and the second bullet above is asking how to get around that.

I got around both issues by storing the data in three data structures—the two RDDs described above and one more:

  • For the vertex RDD, along with the required long integer that must be stored as each vertex's identifier, I only stored one extra piece of information: the URI associated with that RDF resource. I did this for the subjects, the predicates (which may not be "vertices" in the GraphX sense of the word, but damn it, they're resources that can be the subjects or objects of triples if I want them to), and the relevant objects. After reading the triple { <http://id.loc.gov/authorities/subjects/sh85027617> <http://www.w3.org/2004/02/skos/core#related> <http://id.loc.gov/authorities/subjects/sh2009010761>} from the Library of Congress data, the program will create three vertices in this RDD whose node identifiers might be 1L, 2L, and 3L, with each of the triple's URIs stored with one of these RDD vertices.

  • For the edge RDD, along with the required two long integers identifying the vertices at the start and end of the edge, each of my edges also stores the URI of the relevant predicate as the "description" of the edge. The edge for the triple above would be (1L, 3L, http://www.w3.org/2004/02/skos/core#related).

  • To augment the graph data structure created from the two RDDs above, I created a third RDD to store literal property values. Each entry stores the long integer representing the vertex of the resource that has the property, a long integer representing the property (the integer assigned to that property in the vertex RDD), and a string representing the property value. For the triple { <http://id.loc.gov/authorities/subjects/sh2009010761> <http://www.w3.org/2004/02/skos/core#prefLabel> "Happy hours"} it might store (3L, 4L, "Happy hours"), assuming that 4L had been stored as the internal identifier for the skos:prefLabel property. To run the Connected Components algorithm and then output the preferred label of each member of each subgraph, I didn't need this RDD, but it does open up many possibilities for what you can do with RDF in an a Spark GraphX program.

Creating a report on Library of Congress Subject Heading connecting components

After loading up these data structures (plus another one that allows quick lookups of preferred labels) my program below applies the GraphX Connected Components algorithm to the subset of the graph that uses the skos:related property to connect vertices such as "Cocktails" and "Happy hours". Iterating through the results, it uses them to load a hash map with a list for each subgraph of connected components. Then, it goes through each of these lists, printing the label associated with each member of each subgraph and a string of hyphens to show where each list ends, as you can see in the excerpt above.

I won't go into more detail about what's in my program because I commented it pretty heavily. (I do have to thank my friend Tony, mentioned above, for helping me past one point where I was stuck on a Scala scoping issue. Also, as I've warned before, my coding style will probably make experienced Scala programmers choke on their Red Bull. I'd be happy to hear about suggested improvements.)

After getting the program to run properly with a small subset of the data, I ran it on the 1 GB subjects-skos-2014-0306.nt file that I downloaded from the Library of Congress with its 7,705,147 triples. Spark lets applications scale up by giving you an infrastructure to distribute program execution across multiple machines, but the 8GB on my single machine wasn't enough to run this, so I used two grep commands to create a version of the data that only had the skos:related and skos:prefLabel triples. At this point I had a total of 439,430 triples. Because my code didn't account for blank nodes, I removed the 385 triples that used them, leaving 439,045 to work with in a 60MB file. This ran successfully and you can follow the link shown earlier to see the complete output.

Other GraphX algorithms to run on your RDF data

Other GraphX algorithms besides Connected Components include Page Rank and Triangle Counting. Graph theory is an interesting world, in which my favorite phrase so far is "strangulated graph".

One of the greatest things about RDF and Linked Data technology is the growing amount of interesting data being made publicly available, and with new tools such as these algorithms to work with this data—tools that can be run on inexpensive, scalable clusters faster than typical Hadoop MapReduce jobs—there are a lot of great possibilities.

//////////////////////////////////////////////////////////////////
// readLoCSH.scala: read Library of Congress Subject Headings into
// Spark GraphX graph and apply connectedComponents algorithm to those
// connected by skos:related property.

import scala.io.Source 
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD
import scala.collection.mutable.ListBuffer
import scala.collection.mutable.HashMap

object readLoCSH {

    val componentLists = HashMap[VertexId, ListBuffer[VertexId]]()
    val prefLabelMap =  HashMap[VertexId, String]()

    def main(args: Array[String]) {
        val sc = new SparkContext("local", "readLoCSH", "127.0.0.1")

        // regex pattern for end of triple
        val tripleEndingPattern = """\s*\.\s*$""".r    
        // regex pattern for language tag
        val languageTagPattern = "@[\\w-]+".r    

        // Parameters of GraphX Edge are subject, object, and predicate
        // identifiers. RDF traditionally does (s, p, o) order but in GraphX
        // it's (edge start node, edge end node, edge description).

        // Scala beginner hack: I couldn't figure out how to declare an empty
        // array of Edges and then append Edges to it (or how to declare it
        // as a mutable ArrayBuffer, which would have been even better), but I
        // can append to an array started like the following, and will remove
        // the first Edge when creating the RDD.

        var edgeArray = Array(Edge(0L,0L,"http://dummy/URI"))
        var literalPropsTriplesArray = new Array[(Long,Long,String)](0)
        var vertexArray = new Array[(Long,String)](0)

        // Read the Library of Congress n-triples file
        //val source = Source.fromFile("sampleSubjects.nt","UTF-8")  // shorter for testing
        val source = Source.fromFile("PrefLabelAndRelatedMinusBlankNodes.nt","UTF-8")

        val lines = source.getLines.toArray

        // When parsing the data we read, use this map to check whether each
        // URI has come up before.
        var vertexURIMap = new HashMap[String, Long];

        // Parse the data into triples.
        var triple = new Array[String](3)
        var nextVertexNum = 0L
        for (i <- 0 until lines.length) {
            // Space in next line needed for line after that. 
            lines(i) = tripleEndingPattern.replaceFirstIn(lines(i)," ")  
            triple = lines(i).mkString.split(">\\s+")       // split on "> "
            // Variables have the word "triple" in them because "object" 
            // by itself is a Scala keyword.
            val tripleSubject = triple(0).substring(1)   // substring() call
            val triplePredicate = triple(1).substring(1) // to remove "<"
            if (!(vertexURIMap.contains(tripleSubject))) {
                vertexURIMap(tripleSubject) = nextVertexNum
                nextVertexNum += 1
            }
            if (!(vertexURIMap.contains(triplePredicate))) {
                vertexURIMap(triplePredicate) = nextVertexNum
                nextVertexNum += 1
            }
            val subjectVertexNumber = vertexURIMap(tripleSubject)
            val predicateVertexNumber = vertexURIMap(triplePredicate)

            // If the first character of the third part is a <, it's a URI;
            // otherwise, a literal value. (Needs more code to account for
            // blank nodes.)
            if (triple(2)(0) == '<') { 
                val tripleObject = triple(2).substring(1)   // Lose that <.
                if (!(vertexURIMap.contains(tripleObject))) {
                    vertexURIMap(tripleObject) = nextVertexNum
                    nextVertexNum += 1
                }
                val objectVertexNumber = vertexURIMap(tripleObject)
                edgeArray = edgeArray :+
                    Edge(subjectVertexNumber,objectVertexNumber,triplePredicate)
            }
            else {
                literalPropsTriplesArray = literalPropsTriplesArray :+
                    (subjectVertexNumber,predicateVertexNumber,triple(2))
            }
        }

        // Switch value and key for vertexArray that we'll use to create the
        // GraphX graph.
        for ((k, v) <- vertexURIMap) vertexArray = vertexArray :+  (v, k)   

        // We'll be looking up a lot of prefLabels, so create a hashmap for them. 
        for (i <- 0 until literalPropsTriplesArray.length) {
            if (literalPropsTriplesArray(i)._2 ==
                vertexURIMap("http://www.w3.org/2004/02/skos/core#prefLabel")) {
                // Lose the language tag.
                val prefLabel =
                    languageTagPattern.replaceFirstIn(literalPropsTriplesArray(i)._3,"")
                prefLabelMap(literalPropsTriplesArray(i)._1) = prefLabel;
            }
        }

        // Create RDDs and Graph from the parsed data.

        // vertexRDD Long: the GraphX longint identifier. String: the URI.
        val vertexRDD: RDD[(Long, String)] = sc.parallelize(vertexArray)

        // edgeRDD String: the URI of the triple predicate. Trimming off the
        // first Edge in the array because it was only used to initialize it.
        val edgeRDD: RDD[Edge[(String)]] =
            sc.parallelize(edgeArray.slice(1,edgeArray.length))

        // literalPropsTriples Long, Long, and String: the subject and predicate
        // vertex numbers and the the literal value that the predicate is
        // associating with the subject.
        val literalPropsTriplesRDD: RDD[(Long,Long,String)] =
            sc.parallelize(literalPropsTriplesArray)

        val graph: Graph[String, String] = Graph(vertexRDD, edgeRDD)

        // Create a subgraph based on the vertices connected by SKOS "related"
        // property.
        val skosRelatedSubgraph =
            graph.subgraph(t => t.attr ==
                           "http://www.w3.org/2004/02/skos/core#related")

        // Find connected components  of skosRelatedSubgraph.
        val ccGraph = skosRelatedSubgraph.connectedComponents() 

        // Fill the componentLists hashmap.
        skosRelatedSubgraph.vertices.leftJoin(ccGraph.vertices) {
        case (id, u, comp) => comp.get
        }.foreach
        { case (id, startingNode) => 
          {
              // Add id to the list of components with a key of comp.get
              if (!(componentLists.contains(startingNode))) {
                  componentLists(startingNode) = new ListBuffer[VertexId]
              }
              componentLists(startingNode) += id
          }
        }

        // Output a report on the connected components. 
        println("------  connected components in SKOS \"related\" triples ------\n")
        for ((component, componentList) <- componentLists){
            if (componentList.size > 1) { // don't bother with lists of only 1
                for(c <- componentList) {
                    println(prefLabelMap(c));
                }
                println("--------------------------")
            }
        }

        sc.stop
    }
}

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29 March 2015

Spark and SPARQL; RDF Graphs and GraphX

Some interesting possibilities for working together.

some description

In Spark Is the New Black in IBM Data Magazine, I recently wrote about how popular the Apache Spark framework is for both Hadoop and non-Hadoop projects these days, and how for many people it goes so far as to replace one of Hadoop's fundamental components: MapReduce. (I still have trouble writing "Spar" without writing "ql" after it.) While waiting for that piece to be copyedited, I came across 5 Reasons Why Spark Matters to Business by my old XML.com editor Edd Dumbill and 5 reasons to turn to Spark for big data analytics in InfoWorld, giving me a total of 10 reasons that Spark... is getting hotter.

I originally became interested in Spark because one of its key libraries is GraphX, Spark's API for working with graphs of nodes and arcs. The "GraphX: Unifying Data-Parallel and Graph-Parallel Analytics" paper by GraphX's inventors (pdf) has a whole section on RDF as related work, saying "we adopt some of the core ideas from the RDF work including the triples view of graphs." The possibility of using such a hot new Big Data technology with RDF was intriguing, so I decided to look int it.

I thought it would be interesting to output a typical GraphX graph as RDF so that I could perform SPARQL queries on it that were not typical of GraphX processing, and then to go the other way: read a good-sized RDF dataset into GraphX and do things with it that would not be typical of SPARQL processing. I have had some success at both, so I think that RDF and GraphX systems have much to offer each other.

This wouldn't have been very difficult if I wasn't learning the Scala programming language as I went along, but GraphX libraries are not available for Python or Java yet, so what you see below is essentially my first Scala program. A huge help in my attempts to learn Scala, Spark, and GraphX were the class handouts of Swedish Institute of Computer Science senior researcher Amir H. Payberah. I just stumbled across them in some web searches while trying to get a Scala GraphX program to compile, and his PDFs introducing Scala, Spark, and graph processing (especially the GraphX parts) lit a lot of "a-ha" lightbulbs for me, and I had already looked through several introductions to Scala and Spark. He has since encouraged me to share the link to course materials for his current course on cloud computing.

While I had a general idea of how functional programming languages worked, one of the lightbulbs that Dr. Payberah's work lit for me was why they're valuable, at least in the case of using Spark from Scala: Spark provides higher-order functions that can hand off your own functions and data to structures that can be stored in distributed memory. This allows the kinds of interactive and iterative (for example, machine learning) tasks that generally don't work well with Hadoop's batch-oriented MapReduce model. Apparently, for tasks that would work fine with MapReduce, Spark versions also run much faster because their better use of memory lets them avoid all the disk I/O that is typical of MapReduce jobs.

Spark lets you use this distributed memory by providing a data structure called a Resilient Distributed Dataset, or RDD. When you store your data in RDDs, you can let Spark take care of their distribution across a computing cluster. GraphX lets you store a set of nodes, arcs, and—crucially for us RDF types—extra information about each in RDDs. To output a "typical" GraphX graph structure as RDF, I took the Example Property Graph example in the Apache Spark GraphX Programming Guide and expanded it a bit. (If experienced Scala programmers don't gag when they see my program, they will in my next installment, where I show how I read RDF into GraphX RDDs. Corrections welcome.)

My Scala program below, like the Example Property Graph mentioned above, creates an RDD called users of nodes about people at a university and an RDD called relationships that stores information about edges that connect the nodes. RDDs use long integers such as the 3L and 7L values shown below as identifiers for the nodes, and you'll see that it can store additional information about nodes—for example, that node 3L is named "rxin" and has the title "student"—as well as additional information about edges—for example, that the user represented by 5L has an "advisor" relationship to user 3L. I added a few extra nodes and edges to give the eventual SPARQL queries a little more to work with.

Once the node and edge RDDs are defined, the program creates a graph from them. After that, I added code to output RDF triples about node relationships to other nodes (or, in RDF parlance, object property triples) using a base URI that I defined at the top of the program to convert identifiers to URIs when necessary. This produced triples such as <http://snee.com/xpropgraph#istoica> <http://snee.com/xpropgraph#colleague> <http://snee.com/xpropgraph#franklin> in the output. Finally, the program outputs non-relationship values (literal properties), producing triples such as <http://snee.com/xpropgraph#rxin> <http://snee.com/xpropgraph#role> "student".

import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.graphx._
import org.apache.spark.rdd.RDD

object ExamplePropertyGraph {
    def main(args: Array[String]) {
        val baseURI = "http://snee.com/xpropgraph#"
	val sc = new SparkContext("local", "ExamplePropertyGraph", "127.0.0.1")

        // Create an RDD for the vertices
        val users: RDD[(VertexId, (String, String))] =
            sc.parallelize(Array(
                (3L, ("rxin", "student")),
                (7L, ("jgonzal", "postdoc")),
                (5L, ("franklin", "prof")),
                (2L, ("istoica", "prof")),
                // Following lines are new data
                (8L, ("bshears", "student")),
                (9L, ("nphelge", "student")),
                (10L, ("asmithee", "student")),
                (11L, ("rmutt", "student")),
                (12L, ("ntufnel", "student"))
            ))
        // Create an RDD for edges
        val relationships: RDD[Edge[String]] =
            sc.parallelize(Array(
                Edge(3L, 7L, "collab"),
                Edge(5L, 3L, "advisor"),
                Edge(2L, 5L, "colleague"),
                Edge(5L, 7L, "pi"),
                // Following lines are new data
                Edge(5L, 8L, "advisor"),
                Edge(2L, 9L, "advisor"),
                Edge(5L, 10L, "advisor"),
                Edge(2L, 11L, "advisor")
            ))
        // Build the initial Graph
        val graph = Graph(users, relationships)

        // Output object property triples
        graph.triplets.foreach( t => println(
            s"<$baseURI${t.srcAttr._1}> <$baseURI${t.attr}> <$baseURI${t.dstAttr._1}> ."
        ))

        // Output literal property triples
        users.foreach(t => println(
            s"""<$baseURI${t._2._1}> <${baseURI}role> \"${t._2._2}\" ."""
        ))

        sc.stop

    }
}

The program writes out the RDF with full URIs for each every resource, but I'm showing a Turtle version here that uses prefixes to help it fit on this page better:

@prefix xp: <http://snee.com/xpropgraph#> . 

xp:istoica  xp:colleague xp:franklin .
xp:istoica  xp:advisor   xp:nphelge .
xp:istoica  xp:advisor   xp:rmutt .
xp:rxin     xp:collab    xp:jgonzal .
xp:franklin xp:advisor   xp:rxin .
xp:franklin xp:pi        xp:jgonzal .
xp:franklin xp:advisor   xp:bshears .
xp:franklin xp:advisor   xp:asmithee .
xp:rxin     xp:role      "student" .
xp:jgonzal  xp:role      "postdoc" .
xp:franklin xp:role      "prof" .
xp:istoica  xp:role      "prof" .
xp:bshears  xp:role      "student" .
xp:nphelge  xp:role      "student" .
xp:asmithee xp:role      "student" .
xp:rmutt    xp:role      "student" .
xp:ntufnel  xp:role      "student" .

My first SPARQL query of the RDF asked this: for each person with advisees, how many do they have?

PREFIX xp: <http://snee.com/xpropgraph#>

SELECT ?person (COUNT(?advisee) AS ?advisees)
WHERE {
  ?person xp:advisor ?advisee
}
GROUP BY ?person

Here is the result:

--------------------------
| person      | advisees |
==========================
| xp:franklin | 3        |
| xp:istoica  | 2        |
--------------------------

The next query asks about the roles of rxin's collaborators:

PREFIX xp: <http://snee.com/xpropgraph#>

SELECT ?collaborator ?role
WHERE {
  xp:rxin xp:collab ?collaborator . 
  ?collaborator xp:role ?role . 
}

As it turns out, there's only one:

----------------------------
| collaborator | role      |
============================
| xp:jgonzal   | "postdoc" |
----------------------------

Does nphelge have a relationship to any prof, and if so, who and what relationship?

PREFIX xp: <http://snee.com/xpropgraph#>

SELECT ?person ?relationship
WHERE {

  ?person xp:role "prof" . 

  { xp:nphelge ?relationship ?person }
  UNION
  { ?person ?relationship xp:nphelge }

}

And here is our answer:

-----------------------------
| person     | relationship |
=============================
| xp:istoica | xp:advisor   |
-----------------------------

A hardcore RDF person will have two questions about the sample data:

  • What about properties of edges? For example, what if I wanted to say that an xp:advisor property was an rdfs:subPropertyOf the Dublin Core property dc:contributor?

  • The ability to assign properties such as a name of "rxin" and a role of "student" to a node like 3L is nice, but what if I don't have a consistent set of properties that will be assigned to every node—for example, if I've aggregated person data from two different sources that don't use all the same properties to describe these persons?

Neither of those were difficult with GraphX, and next month I'll show my approach. I'll also show how I applied that approach to let a GraphX program read in any RDF and then perform GraphX operations on it.


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