Lucene使用项向量进行模糊查询

TermVector是Lucene 1.4新增的 它提供一种向量机制来进行模糊查询,TermVector保存Token.getPositionIncrement() 和Token.startOffset() 以及Token.endOffset() 信息.

 

Field.TermVector.NO:不保存term vectors
Field.TermVector.YES:保存term vectors
Field.TermVector.WITH_POSITIONS:保存term vectors.(保存值和token位置信息)
Field.TermVector.WITH_OFFSETS:保存term vectors.(保存值和Token的offset)
Field.TermVector.WITH_POSITIONS_OFFSETS:保存term vectors.(保存值、token位置信息和Token的offset)

 Analyzer analyzer = new StandardAnalyzer();   
  
RAMDirectory directory = new RAMDirectory();   
  
/**   
 * 创建索引  
 *   
 * @throws IOException  
 */  
public void index() throws IOException{   
       
    IndexWriter indexWriter = new IndexWriter(directory,analyzer,true);   
       
    Document doc1 = new Document();   
       
    doc1.add(new Field("title","java",Store.YES,Index.TOKENIZED));   
       
    doc1.add(new Field("author","callan",Store.YES,Index.TOKENIZED));   
       
    doc1.add(new Field("subject", "java一门编程语言",   
            Store.YES, Index.TOKENIZED,TermVector.WITH_POSITIONS_OFFSETS));   
       
    indexWriter.addDocument(doc1);   
       
    Document doc2 = new Document();   
       
    doc2.add(new Field("title","english",Store.YES,Index.TOKENIZED));   
       
    doc2.add(new Field("author","wcq",Store.YES,Index.TOKENIZED));   
       
    doc2.add(new Field("subject", "英语用的人很多",   
            Store.YES, Index.TOKENIZED,TermVector.WITH_POSITIONS_OFFSETS));   
       
    indexWriter.addDocument(doc2);   
       
    Document doc3 = new Document();   
               
    doc3.add(new Field("title","asp",Store.YES,Index.TOKENIZED));   
               
    doc3.add(new Field("author","ca",Store.YES,Index.TOKENIZED));   
               
    doc3.add(new Field("subject", "asp很多人用",   
            Store.YES, Index.TOKENIZED,TermVector.WITH_POSITIONS_OFFSETS));   
       
    indexWriter.addDocument(doc3);   
       
    indexWriter.optimize();   
       
    indexWriter.close();   
}   
  
// 进行搜索   
public void searcher() throws IOException{   
       
    IndexSearcher searcher = new IndexSearcher(directory);   
       
    // 搜索书名为java的索引   
    TermQuery query = new TermQuery(new Term("title","java"));   
       
    Hits hits = searcher.search(query);   
       
    // 能找到一条记录   
    for(int i = 0; i < hits.length(); i++){   
           
        Document doc = hits.doc(i);   
           
        System.out.println("书名:" + doc.get("title") + "  " + "作者:" + doc.get("author") + "简介:" + doc.get("subject"));   
           
        System.out.println("相关的书:");   
           
        docsLike(hits.id(i));   
           
    }   
       
}   
  
// 在subject中模糊搜索与doc相进的索引   
public void docsLike(int id) throws IOException {   
       
    IndexReader reader = IndexReader.open(directory);   
       
    TermFreqVector  vector = reader.getTermFreqVector(id, "subject");   
  
    BooleanQuery query = new BooleanQuery();   
       
    for (int j = 0; j < vector.size(); j++) {   
           
        TermQuery tq = new TermQuery(new Term("subject",   
                vector.getTerms()[j]));   
           
         query.add(tq, BooleanClause.Occur.SHOULD);   
    }   
       
    IndexSearcher searcher = new IndexSearcher(directory);   
       
    Hits hits = searcher.search(query);   
       
    printResult(hits);   
       
}   
  
// 显示结果   
public void printResult(Hits hits) throws IOException{   
       
    for(int i = 0; i < hits.length(); i++){   
           
        Document d = hits.doc(i);   
           
        System.out.println("书名:" + d.get("title")+"  " + "作者:" + d.get("author") +"  " + "简介:" + d.get("subject"));   
           
    }   
}   
  
  
public static void main(String[] args) throws IOException {   
       
    TermFreqVectorTest3 test = new TermFreqVectorTest3();   
       
    test.index();   
       
    test.searcher();   
}  

 搜索结果:

书名:java  作者:callan简介:java一门编程语言
相关的书:
书名:java  作者:callan  简介:java一门编程语言
书名:english  作者:wcq  简介:英语用的人很多

搜索书名为java 的索引,并且搜索与java的简介相关的索引.
将书<<java>>的subject分词为java/一/门/编/程/语/言/

在subject中搜索包含java/一/门/编/程/语/言/的索引

<<english>>包含语