如何使用java从RDF文件中获取特定的标签相关信息

我使用Jena读取RDF文件，但我想获取信息只是特定的语句。 下面是我读取sample.rdf示例代码。

sample.rdf ：

  4 6588  network, specific mechanisms for implementing VoIP applications. Topics include imple-menting a  [applications. Topics include imple- menting a ]VoIP[ network, specific mechanisms for implementing]      Cisco IOS 2 Cisco IOS     

将sample.rdf加载到Jena模型中的Java代码：

 public class FirstRDFReader extends Object { public static void main (String args[]) { String inputFile="C://Sample.rdf"; Model model = ModelFactory.createDefaultModel(); try{ InputStream in =new FileInputStream(inputFile); if (in == null) { System.out.println("File not found"); } model.read(in," "); model.write(System.out); }catch(Exception e){} } } 

期望的输出：

  2 Cisco IOS  

谢谢

Answer 1

工作数据

这是我们可以实际使用的一些数据。 它基于您的数据，定义了rdf前缀，以及一些用于制作ctag:SocialTag附加前缀ctag:SocialTag和csys:InstanceInfo工作。 这些是可选的，但是需要c的前缀定义，因为它已在您的数据中使用。 为方便起见，我只使用了http://example.org/c# ，但您可能已经定义了其他内容。

   Cisco IOS 2 Cisco IOS     4 6588  network, specific mechanisms for implementing VoIP applications. Topics include imple-menting a  [applications. Topics include imple- menting a ]VoIP[ network, specific mechanisms for implementing]     

一旦你在某处获得了这些数据，有两种简单的方法可以使用Jena来获取它。 第一种是使用Jena的Model API，它提供了检索语句的方法。 第二种是使用SPARQL查询。 您可以使用Jena的命令行工具运行SPARQL查询，也可以从Java程序运行。

使用Model API

这是创建results模型以存储所需输出的Java代码，从input检索SocialTag和定义其名称和重要性的语句，并将语句复制到results 。

 public static Model queryWithAPI() { // Create a model for the output, and add the prefix mappings // from the input model. This step isn't necessary, but it // makes the output easier to read. final Model results = ModelFactory.createDefaultModel(); results.setNsPrefixes( input ); // Iterate through the SocialTags in the data, and for each SocialTag s, retrieve // the statements [s, name, ?name] and [s, importance, ?importance] from the input // model, and add them to the results. for ( final ResIterator it = input.listResourcesWithProperty( RDF.type, SocialTag ); it.hasNext(); ) { final Resource socialTag = it.next(); results.add( socialTag.getProperty( importance )); results.add( socialTag.getProperty( name )); } return results; } 

使用SPARQL

以下SPARQL construct查询还检索SocialTags，然后构造所需的图形。

 prefix rdf:  prefix ctag:  prefix c:  construct { ?tag c:name ?name ; c:importance ?importance . } where { ?tag a ctag:SocialTag ; c:name ?name ; c:importance ?importance . } 

这是在input模型上执行该查询的Java代码。

 public static Model queryWithSPARQL() { // A SPARQL query that retrieves each SocialTag and its name // and importance, and constructs a model containing just the // name and importance statements. final String query = "" + "prefix rdf: <"+RDF.getURI()+">\n" + "prefix ctag: <"+CTAG+">\n" + "prefix c: <"+C+">\n" + "construct {\n" + " ?tag c:name ?name ;\n" + " c:importance ?importance .\n" + "}\n" + "where {\n" + " ?tag a ctag:SocialTag ;\n" + " c:name ?name ;\n" + " c:importance ?importance .\n" + "}"; // Create and execute the query on the input model. return QueryExecutionFactory.create( query, input ).execConstruct(); } 

现在都在一起了

上面的列表只是一个定义input和读取数据的工作示例的片段。 这是整个列表：

 import com.hp.hpl.jena.query.QueryExecutionFactory; import com.hp.hpl.jena.rdf.model.Model; import com.hp.hpl.jena.rdf.model.ModelFactory; import com.hp.hpl.jena.rdf.model.Property; import com.hp.hpl.jena.rdf.model.ResIterator; import com.hp.hpl.jena.rdf.model.Resource; import com.hp.hpl.jena.rdf.model.ResourceFactory; import com.hp.hpl.jena.vocabulary.RDF; public class CalaisExample { static final String C = "http://example.org/c#"; static final String CTAG = "http://s.opencalais.com/1/type/tag/"; static final Resource SocialTag = ResourceFactory.createResource( CTAG+"SocialTag" ); static final Property importance = ResourceFactory.createProperty( C+"importance" ); static final Property name = ResourceFactory.createProperty( C+"name" ); // Create a model for the input and read in the data. static final Model input = ModelFactory.createDefaultModel() .read( "file:///home/taylorj/tmp/jena-calais/calais.rdf" ); public static void main(String[] args) { System.out.println( "== Using API ==" ); queryWithAPI().write( System.out ); System.out.println(); System.out.println( "== Using SPARQL ==" ); queryWithSPARQL().write( System.out ); } public static Model queryWithAPI() { // Create a model for the output, and add the prefix mappings // from the input model. This step isn't necessary, but it // makes the output easier to read. final Model results = ModelFactory.createDefaultModel(); results.setNsPrefixes( input ); // Iterate through the SocialTags in the data, and for each SocialTag s, retrieve // the statements [s, name, ?name] and [s, importance, ?importance] from the input // model, and add them to the results. for ( final ResIterator it = input.listResourcesWithProperty( RDF.type, SocialTag ); it.hasNext(); ) { final Resource socialTag = it.next(); results.add( socialTag.getProperty( importance )); results.add( socialTag.getProperty( name )); } return results; } public static Model queryWithSPARQL() { // A SPARQL query that retrieves each SocialTag and its name // and importance, and constructs a model containing just the // name and importance statements. final String query = "" + "prefix rdf: <"+RDF.getURI()+">\n" + "prefix ctag: <"+CTAG+">\n" + "prefix c: <"+C+">" + "construct {\n" + " ?tag c:name ?name ;\n" + " c:importance ?importance .\n" + "}\n" + "where {\n" + " ?tag a ctag:SocialTag ;\n" + " c:name ?name ;\n" + " c:importance ?importance .\n" + "}"; // Create and execute the query on the input model. return QueryExecutionFactory.create( query, input ).execConstruct(); } } 

这是输出：

 == Using API ==   Cisco IOS 2   == Using SPARQL ==   2 Cisco IOS