User Guide for Sesame 2.0

A memory store is an RDF repository that stores its data in main memory. Apart from the standard ID and title parameters, this type of repository has a Persist and Sync delay parameter.

A native store stores and retrieves its data directly to/from disk. The advantage of this over the memory store is that it scales much better as it isn't limited to the size of available memory. Of course, since it has to access the disk, it is also slower than the in-memory store, but it is a good solution for larger data sets.

An HTTP repository isn't an actual store, but serves as a proxy for a store on a (remote) Sesame server. Apart from the standard ID and title parameters, this type of repository has a Sesame server location and a Remote repository ID parameter.

One of the simplest configurations is a repository that just stores RDF data in main memory without applying any inferencing or whatsoever. This is also by far the fastest type of repository that can be used. The following code creates and initialize a non-inferencing main-memory repository:

import org.openrdf.repository.Repository;
import org.openrdf.repository.sail.SailRepository;
import org.openrdf.sail.memory.MemoryStore;


...

Repository myRepository = new SailRepository(new MemoryStore());
myRepository.initialize();

The constructor of the SailRepository class accepts any object of type Sail, so we simply pass it a new main-memory store object (which is, of course, a Sail implementation). Following this, the repository needs to be initialized to prepare the Sail(s) that it operates on, which includes operations such as restoring previously stored data, setting up connections to a relational database, etc.

The repository that is created by the above code is volatile: its contents are lost when the object is garbage collected or when the program is shut down. This is fine for cases where, for example, the repository is used as a means for manipulating an RDF model in memory.

Different types of Sail objects take parameters in their constructor that change their behaviour. The MemoryStore for example takes a data directory parameter that specifies a data directory for persisent storage. If specified, the MemoryStore will write its contents to this directory so that it can restore it when it is initialized in a future session:

File dataDir = new File("c:\\temp\\myRepository\\");
Repository myRepository = new SailRepository( new MemoryStore(dataDir) );
myRepository.initialize();

As you can see, we can fine-tune the configuration of our repository by passing parameters to the constructor of the Sail object. Some Sail types may offer additional configuration methods, all of which need to be called before the repository is initialized. The MemoryStore currently has one such method: setSyncDelay(long), which can be used to control the strategy that is used for writing to the data file, e.g.:

File dataDir = new File("c:\\temp\\myRepository\\");
MemoryStore memStore = new MemoryStore(dataDir);
memStore.setSyncDelay(1000L);

Repository myRepository = new SailRepository(memStore);
myRepository.initialize();

A Native RDF Repository does not keep its data in main memory, but instead stores it directly to disk (in a binary format optimized for compact storage and fast retrieval). It is an efficient, scalable and fast solution for RDF storage of datasets that are too large to keep entirely in memory.

The code for creation of a Native RDF repository is almost identical to that of a main memory repository:

import org.openrdf.repository.Repository;
import org.openrdf.repository.sail.SailRepository;
import org.openrdf.sail.nativerdf.NativeStore;


...
File dataDir = new File("/path/to/datadir/");
Repository myRepository = new SailRepository(new NativeStore(dataDir));
myRepository.initialize();

By default, the Native store creates a set of two indexes (see Section 7.6.2, “Native store configuration”). To configure which indexes it should create, we can either use the NativeStore.setTripleIndexes(String) method, or we can directly supply a index configuration string to the constructor:

import org.openrdf.repository.Repository;
import org.openrdf.repository.sail.SailRepository;
import org.openrdf.sail.nativerdf.NativeStore;


...
File dataDir = new File("/path/to/datadir/");
String indexes = "spoc,posc,cosp";
Repository myRepository = new SailRepository(new NativeStore(dataDir, indexes));
myRepository.initialize();

As we have seen, we can create Repository objects for any kind of back-end store by passing them a reference to the appropriate Sail object. We can pass any stack of Sails this way, allowing all kinds of repository configurations to be created quite easily. For example, to stack an RDF Schema inferencer on top of a memory store, we simply create a repository like so:

import org.openrdf.repository.Repository;
import org.openrdf.repository.sail.SailRepository;
import org.openrdf.sail.memory.MemoryStore;
import org.openrdf.sail.inferencer.fc.ForwardChainingRDFSInferencer;

...

Repository myRepository = new SailRepository(
                          new ForwardChainingRDFSInferencer(
                          new MemoryStore()));
myRepository.initialize();

Each layer in the Sail stack is created by a constructor that takes the underlying Sail as a parameter. Finally, we create the SailRepository object as a functional wrapper around the Sail stack.

The ForwardChainingRDFSInferencer that is used in this example is a generic RDF Schema inferencer; it can be used on top of any Sail that supports the methods it requires. Both MemoryStore and NativeStore support these methods.

Working with remote repositories is just as easy as working with local ones. We can simply use a different Repository object, the HTTPRepository, instead of the SailRepository class.

A requirement is of course that there is a Sesame 2 server running on some remote system, which is accessible over HTTP. For example, suppose that at http://example.org/sesame2/ a Sesame server is running, which has a repository with the identification 'example-db'. We can access this repository in our code as follows:

import org.openrdf.repository.Repository;
import org.openrdf.repository.http.HTTPRepository;

...

String sesameServer = "http://example.org/sesame2";
String repositoryID = "example-db";

Repository myRepository = new HTTPRepository(sesameServer, repositoryID);
myRepository.initialize();

The Repository API offers various methods for adding data to a repository. Data can be added by specifying the location of a file that contains RDF data, and statements can be added individually or in collections.

We perform operations on a repository by requesting a RepositoryConnection from the repository. On this RepositoryConnection object we can the various operations, such as query evaluation, getting, adding, or removing statements, etc.

The following example code adds two files, one local and one available through HTTP, to a repository:

import org.openrdf.OpenRDFException;
import org.openrdf.repository.Repository;
import org.openrdf.repository.RepositoryConnection;
import org.openrdf.rio.RDFFormat;
import java.io.File;
import java.net.URL;

...

File file = new File("/path/to/example.rdf");
String baseURI = "http://example.org/example/local";

try {
   RepositoryConnection con = myRepository.getConnection();
   try {
      con.add(file, baseURI, RDFFormat.RDFXML);

      URL url = new URL("http://example.org/example/remote");
      con.add(url, url.toString(), RDFFormat.RDFXML);
   }
   finally {
      con.close();
   }
}
catch (OpenRDFException e) {
   // handle exception
}
catch (java.io.IOEXception e) {
   // handle io exception
}

More information on other available methods can be found in the javadoc reference of the RepositoryConnection interface.

The Repository API has a number of methods for creating and evaluating queries. Three types of queries are distinguished: tuple queries, graph queries and boolean queries. The query types differ in the type of results that they produce.

The result of a tuple query is a set of tuples (or variable bindings), where each tuple represents a solution of a query. This type of query is commonly used to get specific values (URIs, blank nodes, literals) from the stored RDF data.

The result of graph queries is an RDF graph (or set of statements). This type of query is very useful for extracting sub-graphs from the stored RDF data, which can then be queried further, serialized to an RDF document, etc.

The result of boolean queries is a simple boolean value, i.e. true of false. This type of query can be used to check if a repository contains specific information.

Note: Sesame 2 currently supports two query languages: SeRQL and SPARQL. The former is explained in Chapter 9, The SeRQL query language (revision 3.0), the specification for the latter is available online.

import java.util.List;
import org.openrdf.OpenRDFException;
import org.openrdf.repository.RepositoryConnection;
import org.openrdf.query.TupleQuery;
import org.openrdf.query.TupleQueryResult;
import org.openrdf.query.BindingSet;
import org.openrdf.query.QueryLanguage;

...

try {
   RepositoryConnection con = myRepository.getConnection();
   try {
      String queryString = "SELECT x, y FROM {x} p {y}";
      TupleQuery tupleQuery = con.prepareTupleQuery(QueryLanguage.SERQL, queryString);
      TupleQueryResult result = tupleQuery.evaluate();
      try {
         .... // do something with the result
      }
      finally {
         result.close();
      }
   }
   finally {
      con.close();
   }
}
catch (OpenRDFException e) {
   // handle exception
}

while (result.hasNext()) {
   BindingSet bindingSet = result.next();
   Value valueOfX = bindingSet.getValue("x");
   Value valueOfY = bindingSet.getValue("y");

   // do something interesting with the values here...
}

List<String> bindingNames = result.getBindingNames();
while (result.hasNext()) {
   BindingSet bindingSet = result.next();
   Value firstValue = bindingSet.getValue(bindingNames.get(0));
   Value secondValue = bindingSet.getValue(bindingNames.get(1));

   // do something interesting with the values here...
}

import org.openrdf.query.resultio.sparqlxml.SPARQLResultsXMLWriter;

...

FileOutputStream out = new FileOutputStream("/path/to/result.srx");
try {
   SPARQLResultsXMLWriter sparqlWriter = new SPARQLResultsXMLWriter(out);

   RepositoryConnection con = myRepository.getConnection();
   try {
      String queryString = "SELECT * FROM {x} p {y}";
      TupleQuery tupleQuery = con.prepareTupleQuery(QueryLanguage.SERQL, queryString);
      tupleQuery.evaluate(sparqlWriter);
   }
   finally {
      con.close();
   }
}
finally {
   out.close();
}

import org.openrdf.query.GraphQueryResult;

GraphQueryResult graphResult = con.prepareGraphQuery(
      QueryLanguage.SERQL, "CONSTRUCT * FROM {x} p {y}").evaluate();

while (graphResult.hasNext()) {
   Statement st = graphResult.next();
   // ... do something with the resulting statement here.
}

import org.openrdf.rio.turtle.TurtleWriter;

...

RepositoryConnection con = myRepository.getConnection();
try {
   TurtleWriter turtleWriter = new TurtleWriter(System.out);

   con.prepareGraphQuery(QueryLanguage.SERQL,
         "CONSTRUCT * FROM {x} p {y}").evaluate(turtleWriter);
}
finally {
   con.close();
}

RepositoryConnection con = myRepository.getConnection();

try {
   // First, prepare a query that retrieves all names of persons
   TupleQuery nameQuery = con.prepareTupleQuery(QueryLanguage.SERQL,
         "SELECT name FROM {person} ex:name {name}");

   // Then, prepare another query that retrieves all e-mail addresses of persons:
   TupleQuery mailQuery = con.prepareTupleQuery(QueryLanguage.SERQL,
         "SELECT mail FROM {person} ex:mail {mail}; ex:name {name}");

   // Evaluate the first query to get all names
   TupleQueryResult nameResult = nameQuery.evaluate();
   try {
      // Loop over all names, and retrieve the corresponding e-mail address.
      while (nameResult.hasNext()) {
         BindingSet bindingSet = nameResult.next();
         Value name = bindingSet.get("name");

         // Retrieve the matching mailbox, by setting the binding for
         // the variable 'name' to the retrieved value:
         mailQuery.setBinding("name", name);

         TupleQueryResult mailResult = mailQuery.evaluate();

         // mailResult now contains the e-mail addresses for one particular person
         try {
            ....
         }
         finally {
            // after we are done, close the result
            mailResult.close();
         }
      }
   }
   finally {
      nameResult.close();
   }
}
finally {
   con.close();
}

ValueFactory factory = myRepository.getValueFactory();

// In this example, we specify the keyword string. Of course, this
// could just as easily be obtained by user input, or by reading from
// a file, or...
String keyword = "foobar";

// We prepare a query that retrieves all documents for a keyword.
// Notice that in this query the 'keyword' variable is not bound to
// any specific value yet.
TupleQuery keywordQuery = con.prepareTupleQuery(QueryLanguage.SERQL,
      "SELECT document FROM {document} ex:keyword {keyword}");

// Then we set the binding to a literal representation of our keyword.
// Evaluation of the query object will now effectively be the same as
// if we had specified the query as follows:
//   SELECT document FROM {document} ex:keyword {"foobar"}
keywordQuery.setBinding("keyword", factory.createLiteral(keyword));

// We then evaluate the prepared query and can process the result:
TupleQueryResult keywordQueryResult = keywordQuery.evaluate();

The RepositoryConnection can also be used for adding, retrieving, removing or otherwise manipulating individual statements, or sets of statements.

To be able to add new statements, we can use a ValueFactory to create the Values out of which the statements consist. For example, we want to add a few statements about two resources, Alice and Bob:

import org.openrdf.model.vocabulary.RDF;
import org.openrdf.model.vocabulary.RDFS;
...

ValueFactory f = myRepository.getValueFactory();

// create some resources and literals to make statements out of
URI alice = f.createURI("http://example.org/people/alice");
URI bob = f.createURI("http://example.org/people/bob");
URI name = f.createURI("http://example.org/ontology/name");
URI person = f.createURI("http://example.org/ontology/Person");
Literal bobsName = f.createLiteral("Bob");
Literal alicesName = f.createLiteral("Alice");

try {
   RepositoryConnection con = myRepository.getConnection();

   try {
      // alice is a person
      con.add(alice, RDF.TYPE, person);
      // alice's name is "Alice"
      con.add(alice, name, alicesName);

      // bob is a person
      con.add(bob, RDF.TYPE, person);
      // bob's name is "Bob"
      con.add(bob, name, bobsName);
   }
   finally {
      con.close();
   }
}
catch (OpenRDFException e) {
   // handle exception
}

Of course, it will not always be necessary to use a ValueFactory to create URIs. In practice, you will find that you quite often retrieve existing URIs from the repository (for example, by evaluating a query) and then use those values to add new statements.

As you can see in the above code, for the default RDF and RDF Schema properties (such as 'rdf:type' and 'rdfs:subClassOf') it is not necessary to create new URI objects. Instead, you can import the vocabulary classes org.openrdf.model.vocabulary.RDF and RDFS which provide you static references to the vocabulary primitives.

Retrieving statements works in a very similar way. One way of retrieving statements we have already seen actually: we can get a GraphQueryResult containing statements by evaluating a graph query. However, we can also use direct method calls to retrieve (sets of) statements. For example, to retrieve all statements about Alice, we could do:

RepositoryResult<Statement> statements = con.getStatements(alice, null, null, true);

The additional boolean parameter at the end (set to 'true' in this example) indicates whether inferred triples should be included in the result. Of course, this parameter only makes a difference if your repository uses an inferencer.

The RepositoryResult is an iterator-like object that lazily retrieves each matching statement from the repository when its next() method is called. Note that, like is the case with QueryResult objects, iterating over a RepositoryResult may result in exceptions which you should catch to make sure that the RepositoryResult is always properly closed after use:

RepositoryResult<Statement> statements = con.getStatements(alice, null, null, true);

try {
   while (statements.hasNext()) {
      Statement st = statements.next();

      ... // do something with the statement
   }
}
finally {
   statements.close(); // make sure the result object is closed properly
}

In the above method invocation, we see four parameters being passed. The first three represent the subject, predicate and object of the RDF statements which should be retrieved. A null value indicates a wildcard, so the above method call retrieves all statements which have as their subject Alice, and have any kind of predicate and object. The fourth parameter indicates whether or not inferred statements should be included or not.

Removing statements again works in a very similar fashion. Suppose we want to retract the statement that the name of Alice is "Alice"):

con.remove(alice, name, alicesName);

Or, if we want to erase all statements about Alice completely, we can do:

con.remove(alice, null, null);

Most of these examples have been on the level of individual statements. However, the Repository API offers several methods that work with Collections of statements, allowing more batch-like update operations.

For example, in the following bit of code, we first retrieve all statements about Alice, put them in a Collection and then remove them:

import info.aduna.iteration.Iterations;

// Retrieve all statements about Alice and put them in a list
RepositoryResult<Statement> statements = con.getStatements(alice, null, null, true));
List<Statement> aboutAlice = Iterations.addAll(statements, new ArrayList<Statement>());

// Then, remove them from the repository
con.remove(aboutAlice);

As you can see, the info.aduna.iteration.Iterations class provides a convenient method that takes an Iteration (of which RepositoryResult is a subclass) and a Collection as input, and returns the Collection with the contents of the iterator added to it. It also automatically closes the Iteration for you.

In the above code, you first retrieve all statements, put them in a list, and then remove them. Although this works fine, it can be done in an easier fashion, by simply supplying the resulting object directly:

con.remove(con.getStatements(alice, null, null, true));

The RepositoryConnection interface has several variations of add, retrieve and remove operations. See the Javadoc API documentation for a full overview of the options.

Sesame 2 supports the notion of context, which you can think of as a way to group sets of statements together through a single group identifier (this identifier can be a blank node or a URI).

A very typical way to use context is tracking provenance of the statements in a repository, that is, which file these statements originate from. For example, consider an application where you add RDF data from different files to a repository, and then one of those files is updated. You would then like to replace the data from that single file in the repository, and to be able to do this you need a way to figure out which statements need to be removed. The context mechanism gives you a way to do that.

In the following example, we add an RDF document from the Web to our repository, in a context. In the example, we make the context identifier equal to the Web location of the file being uploaded.

String location = "http://example.org/example/example.rdf";
String baseURI = location;
URL url = new URL(location);
URI context = f.createURI(location);

con.add(url, baseURI, RDFFormat.RDFXML, context);

We can now use the context mechanism to specifically address these statements in the repository for retrieve and remove operations:

// Get all statements in the context
RepositoryResult<Statement> result = con.getStatements(null, null, null, true, context);

try {
   while (result.hasNext()) {
      Statement st = result.next();
      ... // do something interesting with the result
   }
}
finally {
   result.close();
}

// Export all statements in the context to System.out, in RDF/XML format
RDFHandler rdfxmlWriter = new RDFXMLWriter(System.out);
con.export(context, rdfxmlWriter);

// Remove all statements in the context from the repository
con.clear(context);

In most methods in the Repository API, the context parameter is a vararg, meaning that you can specify an arbitrary number (zero, one, or more) of context identifiers. This way, you can combine different contexts together. For example, we can very easily retrieve statements that appear in either 'context1' or 'context2'.

In the following example we add information about Bob and Alice again, but this time each has their own context. We also create a new property called 'creator' that has as its value the name of the person who is the creator a particular context. The knowledge about creators of contexts we do not add to any particular context, however:

URI context1 = f.createURI("http://example.org/context1");
URI context2 = f.createURI("http://example.org/context2");
URI creator = f.createURI("http://example.org/ontology/creator");

// Add stuff about Alice to context1
con.add(alice, RDF.TYPE, person, context1);
con.add(alice, name, alicesName, context1);

// Alice is the creator of context1
con.add(context1, creator, alicesName);

// Add stuff about Bob to context2
con.add(bob, RDF.TYPE, person, context2);
con.add(bob, name, bobsName, context2);

// Bob is the creator of context2
con.add(context2, creator, bobsName);

Once we have this information in our repository, we can retrieve all statements about either Alice or Bob by using the context vararg:

// Get all statements in either context1 or context2
RepositoryResult<Statement> result =
      con.getStatements(null, null, null, true, context1, context2);

You should observe that the above RepositoryResult will not contain the information that context1 was created by Alice and context2 by Bob. This is because those statements were added without any context, thus they do not appear in context1 or context2, themselves.

To explicitly retrieve statements that do not have an associated context, we do the following:

// Get all statements that do not have an associated context
RepositoryResult<Statement> result =
      con.getStatements(null, null, null, true, (Resource)null);

This will give us only the statements about the creators of the contexts, because those are the only statements that do not have an associated context. Note that we have to explicitly cast the null argument to Resource, because otherwise it is ambiguous whether we are specifying a single value or an entire array that is null (a vararg is internally treated as an array). Simply invoking getStatements(s, p, o, true, null) without an explicit cast will result in an IllegalArgumentException.

We can also get everything that either has no context or is in context1:

// Get all statements that do not have an associated context, or that are in context1
RepositoryResult<Statement> result =
      con.getStatements(null, null, null, true, (Resource)null, context1);

So as you can see, you can freely combine contexts in this fashion.

Important:

getStatements(null, null, null, true);

is not the same as:

getStatements(null, null, null, true, (Resource)null);

The former (without any context id parameter) retrieves all statements in the repository, ignoring any context information. The latter, however, only retrieves statements that explicitly do not have any associated context.

So far, we have shown individual operations on repositories: adding statements, removing them, etc. By default, a RepositoryConnection runs in autoCommit mode, meaning that each operation on a RepositoryConnection is immediately sent to the store and committed.

The RepositoryConnection interface supports a full transactional mechanism that allows one to group modification operations together and treat them as a single update: before the transaction is committed, none of the operations in the transaction has taken effect, and after, they all take effect. If something goes wrong at any point during a transaction, it can be rolled back so that the state of the repository is the same as before the transaction started. Bundling update operations in a single transaction often also improves update performance compared to multiple smaller transactions.

We can achieve this behaviour by switching off the RepositoryConnection's autoCommit mode. In the following example, we use a non-autocommit connection to bundle two file addition operations in a single transaction:

File inputFile1 = new File("/path/to/example1.rdf");
String baseURI1 = "http://example.org/example1/";

File inputFile2 = new File("/path/to/example2.rdf");
String baseURI2 = "http://example.org/example2/";

RepositoryConnection con = myRepository.getConnection();
try {
   con.setAutoCommit(false);

   // Add the first file
   con.add(inputFile1, baseURI1, RDFFormat.RDFXML);

   // Add the second file
   con.add(inputFile2, baseURI2, RDFFormat.RDFXML);

   // If everything went as planned, we can commit the result
   con.commit();
}
catch (RepositoryException e) {
   // Something went wrong during the transaction, so we roll it back
   con.rollback();
}
finally {
   // Whatever happens, we want to close the connection when we are done.
   con.close();
}

In the above example, we use a transaction to add two files to the repository. Only if both files can be successfully added will the repository change. If one of the files can not be added (for example because it can not be read), then the entire transaction is cancelled and none of the files is added to the repository.

SeRQL revision 1.1 is a syntax revision (see issue tracker item SES-75). This document describes the revised syntax. From Sesame release 1.2-RC1 onwards, the old syntax is no longer supported.

SeRQL revision 1.2 covers a set of new functions and operators:

New operations have been marked with (R1.2) where appropriate in this document.

SeRQL revision 2.0 is an extension of SeRQL that offers functionality for querying contexts. See Section 9.15, “Querying context (R2.0)” for details.

SeRQL revision 3.0 modifies SeRQL to be more like SPARQL, adopting its semantics and operators. Main backwards compatiblity issues with revision 2.0 are:

Variables are identified by names. These names must start with a letter or an underscore ('_') and can be followed by zero or more letters, numbers, underscores, dashes ('-') or dots ('.'). Examples variable names are:

SeRQL keywords are not allowed to be used as variable names. Currently, the following keywords are used or reserved for future use in SeRQL: select, construct, from, where, using, namespace, true, false, not, and, or, like, label, lang, datatype, null, bound, isresource, isliteral, sort, in, union, intersect, minus, exists, forall, distinct, limit, offset.

Keywords in SeRQL are all case-insensitive, this in contrast to variable names; these are case-sensitive.

There are two ways to write down URIs in SeRQL: either as full URIs or as abbreviated URIs. Full URIs must be surrounded with "<" and ">". Examples of this are:

As URIs tend to be long strings with the first part being shared by several of them (i.e. the namespace), SeRQL allows one to use abbreviated URIs (or QNames) by defining (short) names for these namespaces which are called "prefixes". A QName always starts with one of the defined prefixes and a colon (":"). After this colon, the part of the URI that is not part of the namespace follows. The first part, consisting of the prefix and the colon, is replaced by the full namespace by the query engine. Some example QNames are:

RDF literals consist of three parts: a label, a language tag, and a datatype. The language tag and the datatype are optional and at most one of these two can accompany a label (a literal can not have both a language tag and a datatype). The notation of literals in SeRQL has been modelled after their notation in N-Triples; literals start with the label, which is surrounded by double quotes, optionally followed by a language tag with a "@" prefix or by a datatype URI with a "^^" prefix. Example literals are:

The SeRQL notation for abbreviated URIs can also be used. When the prefix rdf is mapped to the namespace http://www.w3.org/1999/02/22-rdf-syntax-ns#, the last example literal could also have been written down like:

SeRQL has also adopted the character escapes from N-Triples; special characters can be escaped by prefixing them with a backslash. One of the special characters is the double quote. Normally, a double quote would signal the end of a literal's label. If the double quote is part of the label, it needs to be escaped. For example, the sentence John said: "Hi!" can be encoded in a SeRQL literals as: "John said: \"Hi!\"".

As the backslash is a special character itself, it also needs to be escaped. To encode a single backslash in a literal's label, two backslashes need to be written in the label. For example, a Windows directory would be encoded as: "C:\\Program Files\\Apache Tomcat\\".

SeRQL has functions for extracting each of the three parts of a literal. These functions are label, lang, and datatype. label("foo"@en) extracts the label "foo", lang("foo"@en) extracts the language tag "en", and datatype("foo"^^rdf:XMLLiteral) extracts the datatype rdf:XMLLiteral. The use of these functions is explained later.

RDF has a notion of blank nodes. These are nodes in the RDF graph that are not labeled with a URI or a literal. The interpretation of such blank nodes is as a form of existential quantification: it allows one to assert that "there exists a node such that..." without specifying what that particular node is. Blank nodes do in fact often have identifiers, but these identifiers are assigned internally by whatever processor is processing the graph and they are only valid in the local context, not as global identifiers (unlike URIs).

Strictly speaking blank nodes are only addressable indirectly, by querying for one or more properties of the node. However, SeRQL, as a practical shortcut, allows blank node identifiers to be used in queries. The syntax for blank nodes is adopted from N-Triples, using a QName-like syntax with "_" as the namespace prefix, and the internal blank node identifier as the local name. For example:

This identifies the blank node with internal identifier "bnode1". These blank node identifiers can be used in the same way that normal URIs or QNames can be used.

Caution: It is important to realize that addressing blank nodes in this way makes SeRQL queries non-portable across repositories. There is no guarantee that in two repositories, even if they contain identical datasets, the blank node identifiers will be identical. It may well be that "bnode1" in repository A is a completely different blank node than "bnode1" in repository B. Even in the same repository, it is not guaranteed that blank node identifiers are stable over updates: if certain statements are added to or removed from a repository, it is not guaranteed "bnode1" still identifies the same blank node that it did before the update operation.

Imagine that we want to query an RDF graph for persons who work for companies that are IT companies. Querying for this information comes down to finding the following pattern in the RDF graph (gray nodes denote variables):

Figure 9.1. A basic path expression

The SeRQL notation for path expressions resembles the picture above; it is written down as:

{Person} foo:worksFor {Company} rdf:type {foo:ITCompany}

The parts surrounded by curly brackets represent the nodes in the RDF graph, the parts between these nodes represent the edges in the graph. The direction of the arcs (properties) in SeRQL path expressions is always from left to right.

In SeRQL queries, multiple path expressions can be specified by seperating them with commas. For example, the path expression show before can also be written down as two smaller path expressions:

{Person} foo:worksFor {Company},
{Company} rdf:type {foo:ITCompany}

The nodes and edges in the path expressions can be variables, URIs and literals. Also, a node can be left empty in case one is not interested in the value of that node. Here are some more example path expressions to illustrate this:

Each and every path can be constructed using a set of basic path expressions. Sometimes, however, it is nicer to use one of the available short cuts. There are three types of short cuts:

9.4.2.1. Multi-value nodes

In situations where one wants to query for two or more statements with identical subject and predicate, the subject and predicate do not have to be repeated over and over again. Instead, a multi-value node can be used:

{subj1} pred1 {obj1, obj2, obj3}

A built-in constraint on this construction is that each value for the variables in the multi-value node is unique (i.e. they are pairwise disjoint). Therefore, this path expression is equivalent to the following combination of path expressions and boolean constraints:

FROM
  {subj1} pred1 {obj1},
  {subj1} pred1 {obj2},
  {subj1} pred1 {obj3}
WHERE obj1 != obj2 AND obj1 != obj3 AND obj2 != obj3

Or graphically:

Figure 9.2. Multi-value nodes

Multi-value nodes can also be used when statements share the predicate and object, e.g.:

{subj1, subj2, subj3} pred1 {obj1}

When used in a longer path expression, multi-value nodes apply to both the part left of the node and the part right of the node. The following path expression:

{first} pred1 {middle1, middle2} pred2 {last}

matches the following graph:

Figure 9.3. Multi-value nodes in a longer path expression

9.4.2.2. Branches

One of the shorts cuts that is probably used most, is the notation for branches in path expressions. There are lots of situations where one wants to query multiple properties of a single subject. Instead of repeating the subject over and over again, one can use a semi-colon to attach a predicate-object combination to the subject of the last part of a path expression, e.g.:

{subj1} pred1 {obj1};
        pred2 {obj2}

Which is equivalent to:

{subj1} pred1 {obj1},
{subj1} pred2 {obj2}

Or graphically:

Figure 9.4. Branches in a path expression

A more advanced example is:

{first} pred {} pred1 {obj1};
                pred2 {obj2} pred3 {obj3}

Which matches the following graph:

Figure 9.5. Branches in a longer path expression

Note that an anonymous variable is used in the middle of the path expressions.

9.4.2.3. Reified statements

The last short cut is a short cut for reified statements. A path expression representing a single statement (i.e. {node} edge {node}) can be written between the curly brackets of a node, e.g.:

{ {reifSubj} reifPred {reifObj} } pred {obj}

This would be equivalent to querying (using "rdf:" as a prefix for the RDF namespace, and "Statement" as a variable for storing the statement's URI):

{Statement} rdf:type {rdf:Statement},
{Statement} rdf:subject {reifSubj},
{Statement} rdf:predicate {reifPred},
{Statement} rdf:object {reifObj},
{Statement} pred {obj}

Again, graphically:

Figure 9.6. A reification path expression

Optional path expressions differ from 'normal' path expressions in that they do not have to be matched to find query results. The SeRQL query engine will try to find paths in the RDF graph matching the path expression, but when it cannot find any paths it will skip the expression and leave any variables in it unbound.

Consider an RDF graph that contains information about people that have names, ages, and optionally e-mail addresses. This is a situation that is very common in RDF data. A logical query on this data is a query that yields all names, ages and, when available, e-mail addresses of people, e.g.:

{Person} ex:name {Name};
         ex:age  {Age};
         ex:email {EmailAddress}

However, using normal path expressions like in the query above, people without e-mail address will not be included in the query result. With optional path expressions, one can indicate that a specific (part of a) path expression is optional. This is done using square brackets, i.e.:

{Person} ex:name {Name};
         ex:age  {Age};
        [ex:email {EmailAddress}]

Or alternatively:

 {Person} ex:name {Name};
          ex:age  {Age},
[{Person} ex:email {EmailAddress}]

In contrast to the first path expressions, this expression will also match people without an e-mail address. For these people, the variable EmailAddress will be unbound.

Optional path expressions can also be nested. This is useful in situations where the existence of a specific path is dependent on the existence of another path. For example, the following path expression queries for the titles of all known documents and, if the author of the document is known, the name of the author (if it is known) and his e-mail address (if it is known):

{Document} ex:title {Title};
          [ex:author {Author} [ex:name {Name}];
                              [ex:email {Email}]]

With this path expression, the SeRQL query engine will not try to find the name and e-mail address of an author when it cannot even find the resource representing the author.