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Let s go through our third example to demonstrate querying by more than one term and the resulting changes that occur to the explain method s printout. This will utilize a BooleanQuery to effect the change.
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SEARCHING ON MULTIPLE TERMS
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When querying on more than one term, documents matching more of the query s terms will normally receive a higher score than those matching on a lesser number. In other words, high match counts increase scores, while low match counts do not. This
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Exploring Lucene s scoring approach and the DefaultSimilarity class
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does not have to be the case. Since these values can be whatever the developer chooses, their ratio could actually be reduced with a high match count. To demonstrate this we ll look at the coord(int overlap, maxOverlap) calculation and how changing it can affect document scoring. Listing 12.6 is the query code we re going to use for this example.
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Listing 12.6 Querying on more than one term with DefaultSimilarity
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BooleanQuery query = new BooleanQuery(); query.add(new BooleanClause( new TermQuery(new Term(FIELD_NAME, "spielberg")), BooleanClause.Occur.MUST)); query.add(new BooleanClause( new TermQuery(new Term(FIELD_NAME, "war")), BooleanClause.Occur.SHOULD)); System.out.println(query.toString()); org.hibernate.search.FullTextQuery hibQuery = session.createFullTextQuery(query, Product.class); hibQuery.setProjection(FullTextQuery.DOCUMENT, FullTextQuery.SCORE, FullTextQuery.DOCUMENT_ID); List<Object[]> results = hibQuery.list();
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B Query description
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for spielberg
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C Query description
for war
D Print the explanation
assert results.size() > 0: "no results returned"; for (Object[] result : results) { System.out.println("score => " + result[1]); System.out.println(hibQuery .explain((Integer)result[2])); }
D Print the explanation
This query searches for two terms: the description field for the term spielberg B and the description for war C. The explanation is printed at D. Once we execute this query, we receive the explanation printout shown in listing 12.7.
Listing 12.7 Results of a query on two terms with DefaultSimilarity unchanged
The raw score score => 1.0 1.009682 = (MATCH) sum of: 0.72382677 = (MATCH) weight(description:spielberg in 230), product of: 0.7705941 = queryWeight(description:spielberg), product of: 4.338487 = idf(docFreq=16) 0.17761816 = queryNorm 0.93931 = (MATCH) fieldWeight(description:spielberg in 230), product of: 1.7320508 = tf(termFreq(description:spielberg)=3)
C The score for
spielberg
Document ranking
4.338487 = idf(docFreq=16) 0.125 = fieldNorm(field=description, doc=230) The score for war 0.2858553 = (MATCH) weight(description:war in 230), product of: 0.63732624 = queryWeight(description:war), product of: 3.5881817 = idf(docFreq=35) 0.17761816 = queryNorm 0.44852272 = (MATCH) fieldWeight(description:war in 230), product of: 1.0 = tf(termFreq(description:war)=1) 3.5881817 = idf(docFreq=35) 0.125 = fieldNorm(field=description, doc=230)
Listing 12.7 shows that each queried term has it own calculation set, as you d expect. The individual term scores C and D are composed of the product of the query weight and the field weight. These scores are in turn summed to produce the raw score B. We have space for one last example. We ll override the coord factor to demonstrate how scoring multiterm matches can be altered.
CHANGING THE COORD(INT OVERLAP, INT MAXOVERLAP)
Now we ll modify the coord function that we introduced in section 12.2. Our ScoringTestSimilarity will override the coord(int overlap, int maxOverlap) method of DefaultSimilarity in listing 12.8 so that the higher the overlap value becomes, the lower the value that is returned.
Listing 12.8 Overriding coord(int overlap, int maxOverlap)
package org.apache.lucene.search; public class ScoringTestSimilarity extends DefaultSimilarity { @Override public float coord(int overlap, int maxOverlap) { if (overlap == 2) { return 0.5F; } Increasing overlap; if (overlap == 1) { decreasing value return 2.0F; } return 0.0F; }
Executing the code in listing 12.4 produces the explanation printout of listing 12.9.
Listing 12.9 Explanation for document 230 after changing the coord method
score => 0.427106 The document score The basic score sum 0.504841 = (MATCH) product of: The raw score of the field scores 1.009682 = (MATCH) sum of: 0.72382677 = (MATCH) weight(description:spielberg in 230), Field score for spielberg product of: 0.7705941 = queryWeight(description:spielberg),
Exploring Lucene s scoring approach and the DefaultSimilarity class
product of: 4.338487 = idf(docFreq=16) 0.17761816 = queryNorm 0.93931 = (MATCH) fieldWeight(description:spielberg in 230), product of: 1.7320508 = tf(termFreq(description:spielberg)=3) 4.338487 = idf(docFreq=16) 0.125 = fieldNorm(field=description, doc=230) 0.2858553 = (MATCH) weight(description:war in 230), Field score product of: for war 0.63732624 = queryWeight(description:war), product of: 3.5881817 = idf(docFreq=35) 0.17761816 = queryNorm 0.44852272 = (MATCH) fieldWeight(description:war in 230), product of: 1.0 = tf(termFreq(description:war)=1) The coord(int 3.5881817 = idf(docFreq=35) overlap, int 0.125 = fieldNorm(field=description, doc=230) maxOverlap)factor 0.5 = coord(2/2)
Document 230 in listing 12.7 was the top result returned. Applying our new ScoringTestSimilarity class moved it all the way to fourteenth place. Looking at listing 12.9 you can see that the basic score D is the sum of the weight for the spielberg query term E and the war query term F, but the raw score is where our change to the Similarity class comes into effect. The raw score C is the product of the basic score D and our returned value G. The fact that the score was reduced because more of the terms matched (two) cut the raw score in half, which is exactly what we wanted to accomplish. Did you happen to notice the difference between the score contained in the Hit object of this result B and the raw score C The raw score of the first document returned was 1.1820041. If the top document s raw score is greater than 1, we utilize this value to normalize the score of all returned documents by dividing all of their scores by this value. Dividing C in listing 12.9 by this value yields 0.4271012. If you look at listing 12.7 you ll notice the same situation. The only difference is that this is the top document returned for this query. Because its raw score is greater than 1.0, it is divided by itself, yielding 1.0, and all other returned documents raw scores are divided by it in turn. Enough on the Similarity class. Before we move on to other classes that are used to affect scoring, we d like to briefly discuss one more thing, query boosting.
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