Java Code Examples for com.hp.hpl.jena.rdf.model.RDFNode#isLiteral()

public DataEntry getDataEntryFromRS(ResultSet rs) {
	DataEntry dataEntry = new DataEntry();
	QuerySolution soln = rs.nextSolution();
	String colName, value;
	boolean useColumnNumbers = this.isUsingColumnNumbers();
	/* for each column get the colName and colValue and add to the data entry */
	for (int i = 0; i < rs.getResultVars().size(); i++) {
		colName = rs.getResultVars().get(i);
		RDFNode node = soln.get(colName) ;  			
		if (node.isLiteral()) {
			value = convertRSToString(soln, colName);
		} else {
			value = soln.getResource(colName).getURI();
		}			
		dataEntry.addValue(useColumnNumbers ? Integer.toString(i + 1) : 
			colName, new ParamValue(value));
	}
	return dataEntry;
}
 

private DataEntry getDataEntryFromRS(ResultSet rs) {
	DataEntry dataEntry = new DataEntry();
	QuerySolution soln = rs.nextSolution();
	String colName, value;
	boolean useColumnNumbers = this.isUsingColumnNumbers();
	/* for each column get the colName and colValue and add to the data entry */
	for (int i = 0; i < rs.getResultVars().size(); i++) {
		colName = rs.getResultVars().get(i);
		RDFNode node = soln.get(colName) ;  			
		if (node.isLiteral()) {
			value = convertRSToString(soln, colName);
		} else {
			value = soln.getResource(colName).getURI();
		}			
		dataEntry.addValue(useColumnNumbers ? Integer.toString(i + 1) : 
			colName, new ParamValue(value));
	}
	return dataEntry;
}
 

private String toTurtle(RDFNode r) {
	if (r.isURIResource()) {
		return PrettyPrinter.qNameOrURI(relativize(r.asResource().getURI()), prefixes);
	} else if (r.isLiteral()) {
		StringBuffer result = new StringBuffer(quote(r.asLiteral().getLexicalForm()));
		if (!"".equals(r.asLiteral().getLanguage())) {
			result.append("@");
			result.append(r.asLiteral().getLanguage());
		} else if (r.asLiteral().getDatatype() != null) {
			result.append("^^");
			result.append(toTurtle(ResourceFactory.createResource(r.asLiteral().getDatatypeURI())));
		}
		return result.toString();
	} else {
		if (!blankNodeMap.containsKey(r)) {
			blankNodeMap.put(r.asResource(), "_:b" + blankNodeCounter++);
		}
		return blankNodeMap.get(r);
	}
}
 

public int compare(RDFNode n1, RDFNode n2) {
	if (n1.isURIResource()) {
		if (!n2.isURIResource()) return -1;
		return n1.asResource().getURI().compareTo(n2.asResource().getURI());
	}
	if (n1.isAnon()) {
		if (n2.isURIResource()) return 1;
		if (n2.isLiteral()) return -1;
		return n1.asResource().getId().getLabelString().compareTo(n2.asResource().getId().getLabelString());
	}
	if (!n2.isLiteral()) return 1;
	int cmpLex = n1.asLiteral().getLexicalForm().compareTo(n2.asLiteral().getLexicalForm());
	if (cmpLex != 0) return cmpLex;
	if (n1.asLiteral().getDatatypeURI() == null) {
		if (n2.asLiteral().getDatatypeURI() != null) return -1;
		return n1.asLiteral().getLanguage().compareTo(n2.asLiteral().getLanguage());
	}
	if (n2.asLiteral().getDatatypeURI() == null) return 1;
	return n1.asLiteral().getDatatypeURI().compareTo(n2.asLiteral().getDatatypeURI()); 
}
 

private String object(RDFNode node) {
    if (node.isLiteral()) {
        Literal lit = node.asLiteral();
        String text = lit.getString();
        String lang = lit.getLanguage();
        String type = lit.getDatatypeURI();

        if (lang == null || "".equals(lang)) {
            lang = "";
        } else {
            lang = " xml:lang='" + escapeXml(lang) + "'";
        }

        if ("".equals(lang)) {
            if (type == null) {
                type = "http://www.w3.org/2001/XMLSchema#string";
            }
            type = " datatype='" + escapeXml(type) + "'";
        } else {
            type = "";
        }

        return "<sem:object" + type + lang + ">" + escapeXml(text) + "</sem:object>";
    } else if (node.isAnon()) {
        return "<sem:object>http://marklogic.com/semantics/blank/" + Long.toHexString(
                hash64(fuse(scramble(milliSecs),randomValue), node.toString()))
                +"</sem:object>";
    } else {
        return "<sem:object>" + escapeXml(node.toString()) + "</sem:object>";
    }
}
 

public boolean isTypeOf(RDFNode node) {
	if (!node.isLiteral()) return false;
	Literal l = node.asLiteral();
	return XSD.xstring.getURI().equals(l.getDatatypeURI())
			|| (l.getDatatypeURI() == null && "".equals(l.getLanguage()));
}
 

public boolean isTypeOf(RDFNode node) {
	return (node.isURIResource() || node.isLiteral());
}
 

public RDFNode coerce(RDFNode in) {
	return in.isLiteral() ? ResourceFactory.createResource() : in;
}
 

/**
 * Find the possible lexical types of a name.
 * tag("New York") for example might be:
 *  {"populated place", "place", "municipality"}..
 */
public List<String> viaDBPedia(String text) {
	/*
	 * ABOUT THE QUERY
	 * ===============
	 * Most of these results are not really excellent.
	 * The recall is pretty high but the precision is low because it
	 * matches every name that _contains_ the candidate answer.
	 * 
	 * So, we should probably trim the results to the most popular names.
	 * 
	 * BUT many queries have thousands of names so this will probably be
	 * slow. Meaning we probably need to compromise or develop our own
	 * solution.
	 * 
	 * ABOUT THE RESULTS
	 * =================
	 * A lot of the results are generic, like "place". And "city" is also
	 * a place, so it may just be inadequately tagged. We probably need
	 * some graph algorithm to help with this.
	 * Some results have synonyms. "country" is a real tag, but "nation" is
	 * not. WordNet can help with this.
	 * 
	 */

	rdf.begin(ReadWrite.READ);
	List<String> types = new ArrayList<>();
	try (QueryExecution qe = QueryExecutionFactory.create(getQuery(text), 
			rdf.getDefaultModel())) {
		ResultSet rs = qe.execSelect();
		while (rs.hasNext()) {
			QuerySolution s = rs.next();
			RDFNode node = s.get("?kind");
			if (node == null) {}
			else if (node.isLiteral())
				types.add(node.asLiteral().getLexicalForm().toLowerCase());
			else if (node.isResource())
				types.add(node.asResource().getLocalName().toLowerCase());
		}
	} finally {
		rdf.end();
	}

	return types;
}