Term suggestion for multi-sense query

1. A computer-implemented method for related term suggestion, the method comprising:

mining search results via a multi-sense query, wherein the multi-sense query comprises:

determining terms/phrases semantically related to submitted terms/phrases, wherein semantic relationships are discovered by mining a context of the terms/phrases to determine meaning;

configuring a threshold frequency of occurrence (FOO) value;

assigning historical queries to high FOO or low FOO based on the configured threshold value;

generating term vectors from the search results associated with a set of high FOO historical queries previously submitted to a search engine; and

generating term clusters as a function of calculated similarity of term vectors, wherein calculated similarity, sim(q j , q k ), is determined as follows:

sim

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(

q

j

,

q

k

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=

∑

i

=

1

d

⁢

⁢

w

ij

·

w

ik

;

wherein d represents vector dimension, q represents a query, k is a dimension index, and wherein weight w for the i th vector's j th term is calculated as follows:

w ij =TF ij ×log( N/DF j ); and

wherein TF ij represents term frequency, N is a total number of query terms, and DF j is a number of extracted feature records that contain the i th vector's j th term;

responsive to receiving a term/phrase from an entity, evaluating the term/phrase via the multi-sense query in view of terms/phrases in the term clusters to identify one or more related term suggestions, wherein the identifying is based on a combination of FOO and a confidence value; and

returning at least one suggested term list ordered by the combination of FOO and confidence value, wherein multiple suggested term lists are generated when the term/phrase matches terms in more than one term cluster.

2. The method of claim 1 , and wherein the entity is a computer-program application or an end-user.

3. The method of claim 1 , further comprising:

collecting historic query from a query log; and

determining ones of the historic query terms with the high FOO.

4. The method of claim 1 , further comprising before creating the term clusters:

reducing dimensionality of the term vectors; and

normalizing the term vectors.

5. The method of claim 1 , wherein evaluating further comprises:

identifying a match between die term/phrase and term(s)/phrase(s) from one or more term clusters; and

responsive to identifying, generating related term suggestion(s) comprising the term(s)/phase(s).

6. The method of claim 5 , wherein the related term suggestion(s) further comprise for each term/phrase of the term(s)/phrase(s), a frequency of occurrence value indicating a number of times the term/phrase occurs in a set of mined historical queries.

7. The method as recited in claim 1 , wherein generating the term clusters further comprises:

sending respective ones of the high FOO historical queries to the search engine to obtain the search results;

extracting features from at least a subset of search results corresponding to the respective ones;

producing the term vectors from the features as a function of the term and inverted document frequencies.

8. The method of claim 7 , and wherein the features comprise a title, description, and/or context for the respective ones of the high FOO historical query terms.

9. The method of claim 7 , and wherein the respective ones comprise top ranked ones of the search results.

10. The method of claim 1 , wherein the term clusters are a first set of term clusters, and wherein the method further comprises:

determining that there is no match between the term/phrase and the terms/phrases; and

responsive to the determining:

making a second set of term clusters from calculated similarity of term vectors, each term vector being generated from search results associated with a set of low FOO historical queries previously submitted to the search engine; and

evaluating the term/phrase in view of terms/phrases of the second set of term clusters to identify one or more related term suggestions.

11. The method of claim 10 , wherein making further comprises:

identifying the low FOO historical queries from historical queries mined from the query log;

sending respective ones of at least a subset of the low FOO historical queries to the search engine to obtain search results;

extracting features from at least a subset of search results

producing the term vectors from the features as a function of the term and inverted term frequencies.

12. The method of claim 11 , and further comprising after clustering:

determining that there is no match between the term/phrase and term(s)/phrase(s) from the first set of term clusters, the first set being based on the high FOO historical queries; and

responsive to the determining, identifying a match between the term/phrase and term(s)/phrase(s) from one or more of the second set of term clusters, the second set being based on the low FOO historical queries; and

responsive to identifying, generating related term suggestion(s) comprising the term(s)/phrase(s).

13. A tangible computer-readable data storage medium comprising computer-executable instructions for executing a method, the method comprising:

mining search results via a multi-sense query, wherein the multi-sense query comprises:

determining terms/phrases semantically related to submitted terms/phrases, wherein semantic relationships are discovered by mining a context of the terms/phrases to determine meaning;

configuring a threshold frequency of occurrence (FOO) value;

assigning historical queries to high FOO or low FOO based on the configured threshold value;

generating term vectors from the search results associated with a set of high FOO historical queries previously submitted to a search engine; and

generating term clusters as a function of calculated similarity of term vectors, wherein calculated similarity, sim(q j , q k ), is determined as follows:

sim

⁡

(

q

j

,

q

k

)

=

∑

i

=

1

d

⁢

⁢

w

ij

·

w

ik

;

wherein d represents vector dimension, q represents a query, k is a dimension index, and wherein weight w for the i th vector's j th term is calculated as follows:

w ij =TF ij ×log( N/DF j ); and

wherein TF j represents term frequency, N is a total number of query terms, and DF j is a number of extracted feature records that contain the i th vector's j th term;

responsive to receiving a term/phrase from an entity, evaluating the term/phrase via the multi-sense query in view of terms/phrases in the term clusters to identify one or more related term suggestions, wherein the identifying is based on a combination of FOO and a confidence value; and

returning at least one suggested term list ordered by the combination of FOO and confidence value, wherein multiple suggested term lists are generated when the term/phrase matches terms in more than one term cluster.

14. The computer-readable data storage medium of claim 13 , and wherein the entity is a computer-program application and/or an end-user.

15. The computer-readable data storage medium of claim 13 , wherein the method further comprises:

collecting historic query from a query log; and

determining ones of the historic query terms with the high FOO.

16. The computer-readable data storage medium of claim 13 , wherein the method further comprises:

reducing dimensionality of the term vectors; and

normalizing the term vectors,

wherein the reducing and normalizing are executed before creating the term clusters.

17. The computer-readable data storage medium of claim 13 , wherein evaluating the term/phrase via the multi-sense query comprises:

identifying a match between the term/phrase and term(s)/phrase(s) from one or more term clusters; and

responsive to identifying, generating related term suggestion(s) comprising the term(s)/phrase(s).

18. The computer-readable data storage medium of claim 17 , wherein the related term suggestion(s) comprise for each term/phrase of the term(s)/phrase(s), a frequency of occurrence value indicating a number of times the term/phrase occurs in a set of mined historical queries.

19. The computer-readable data storage medium of claim 13 , wherein generating the term clusters further comprises:

sending respective ones of the high FOO historical queries to the search engine to obtain the search results;

extracting features from at least a subset of search results corresponding to the respective ones;

producing the term vectors from the features as a function of the term and inverted document frequencies.

20. The computer-readable data storage medium of claim 19 , and wherein the features comprise a title, description, and/or context for the respective ones of the high FOO historical query terms.

21. The computer-readable data storage medium of claim 19 , and wherein the respective ones comprise top ranked ones of the search results.

22. The computer-readable data storage medium of claim 13 , wherein the term clusters are a first set of term clusters, and wherein the method further comprises:

determining that there is no match between the term/phrase and the terms/phrases; and

responsive to determining that there is no match between the term/phrase and the terms/phrases:

making a second set of term clusters from calculated similarity of term vectors, each term vector being generated from search results associated with a set of low FOO historical queries previously submitted to the search engine; and

evaluating the term/phrase in view of terms/phrases of the second set of term clusters to identify one or more related term suggestions.

23. The computer-readable data storage medium of claim 22 , wherein making a second set of term clusters comprises:

identifying the low FOO historical queries from historical queries mined from the query log;

sending respective ones of at least a subset of the low FOO historical queries to the search engine to obtain search results;

extracting features from at least a subset of search results

producing the term vectors from the features as a function of the term and inverted term frequencies.

24. The computer-readable data storage medium of claim 23 , wherein the method further comprising:

after clustering:

determining that there is no match between the term/phrase and term(s)/phrase(s) from the first set of term clusters, the first set being based on the high FOO historical queries; and

responsive to the determining, identifying a match between the term/phrase and term(s)/phrase(s) from one or more of the second set of term clusters, the second set being based on the low FOO historical queries; and

responsive to identifying, generating related term suggestion(s) comprising the term(s)/phrase(s).

25. A computing device comprising:

a processor; and

a memory couple to the processor, the memory comprising computer-program instructions executable by the processor for:

mining search results via a multi-sense query, wherein the multi-sense query comprises:

determining terms/phrases semantically related to submitted terms/phrases, wherein semantic relationships are discovered by mining a context of the terms/phrases to determine meaning;

configuring a threshold frequency of occurrence (FOO) value;

assigning historical queries to high FOO or low FOO based on the configured threshold value;

generating term vectors from the search results associated with a set of high FOO historical queries previously submitted to a search engine; and

generating term clusters as a function of calculated similarity of term vectors, wherein calculated similarity, sim(q i , q k ), is determined as follows:

sim

⁡

(

q

j

,

q

k

)

=

∑

i

=

1

d

⁢

⁢

w

ij

·

w

ik

;

wherein d represents vector dimension, q represents a query, k is a dimension index, and wherein weight w for the i th vector's j th term is calculated as follows:

w ij =TF ij ×log( N/DF j ); and

wherein TF j represents term frequency, N is a total number of query terms, and DF j is a number of extracted feature records that contain the i th vector's j th term;

responsive to receiving a term/phrase from an entity, evaluating the term/phrase via the multi-sense query in view of terms/phrases in the term clusters to identify one or more related term suggestions, wherein the identifying is based on a combination of FOO and a confidence value; and

returning at least one suggested term list ordered by the combination of FOO and confidence value, wherein multiple suggested term lists are generated when the term/phrase matches terms in more than one term cluster.

26. The computing device of claim 25 , wherein the entity is a computer-program application or an end-user.

27. The computer device of claim 25 , further comprising computer-program instructions executable by the processor for:

collecting historic query from a query log; and

determining ones of the historic query terms with the high FOO.

28. The computing device of claim 25 , before creating the term clusters, further comprising computer-program instructions executable by the processor for:

reducing dimensionality of the term vectors; and

normalizing the term vectors.

29. The computing device of claim 25 , wherein evaluating comprises computer-program instructions executable by the processor for:

identifying a match between the term/phrase and term(s)/phrase(s) from one or more term clusters; and

responsive to identifying, generating related term suggestion(s) comprising the term(s)/phrase(s).

30. The computing device of claim 29 , wherein the related term suggestion(s) further comprise for each term/phrase of the term(s)/phrase(s), a frequency of occurrence value indicating a number of times the term/phrase occurs in a set of mined historical queries.

31. The method of claim 25 , wherein generating the term clusters further comprises computer-program instructions executable by the processor for:

sending respective ones of the high FOO historical queries to the search engine to obtain the search results;

extracting features from at least a subset of search results corresponding to the respective ones;

producing the term vectors from the features as a function of the term and inverted document frequencies.

32. The computing device of claim 31 , and wherein the features comprise a title, description, and/or context for the respective ones of the high FOO historical query terms.

33. The computing device of claim 31 , and wherein the respective ones comprise top ranked ones of the search results.

34. The computing device of claim 25 , wherein the term clusters are a first set of term clusters, and wherein the computer-executable instructions further comprise instructions for:

determining that there is no match between the term/phrase and the terms/phrases; and

responsive to the determining:

making a second set of term clusters from calculated similarity of term vectors, each term vector being generated from search results associated with a set of low FOO historical queries previously submitted to the search engine; and

evaluating the term/phrase in view of terms/phrases of the second set of term clusters to identify one or more related term suggestions.

35. The method of claim 34 , wherein making a second set of term clusters comprises computer-executable instructions for:

identifying the low FOO historical queries from historical queries mined from the query log;

sending respective ones of at least a subset of the low FOO historical queries to the search engine to obtain search results;

extracting features from at least a subset of search results

producing the term vectors from the features as a function of the term and inverted term frequencies.

36. The computing device of claim 35 , and further comprising computer-program instructions executable by the processor after clustering, for:

determining that there is no match between the term/phrase and term(s)/phrase(s) from the first set of term clusters, the first set being based on the high FOO historical queries; and

responsive to the determining, identifying a match between the term/phrase and term(s)/phrase(s) from one or more of the second set of term clusters, the second set being based on the low FOO historical queries; and

responsive to identifying, generating related term suggestion(s) comprising the term(s)/phrase(s).

37. A computing device comprising at least one processor, the device further comprising:

means for mining search results via a multi-sense query, wherein the multi-sense query comprises:

means for determining terms/phrases semantically related to submitted terms/phrases, wherein semantic relationships are discovered by mining a context of the terms/phrases to determine meaning;

means for configuring a threshold frequency of occurrence (FOO) value;

means for assigning historical queries to high FOO or low FOO based on the configured threshold value;

means for generating term vectors from the search results associated with a set of high FOO historical queries previously submitted to a search engine; and

means for generating term clusters as a function of calculated similarity of term vectors, wherein calculated similarity, sim(q i , q k ), is determined as follows:

sim

⁡

(

q

j

,

q

k

)

=

∑

i

=

1

d

⁢

⁢

w

ij

,

w

ik

;

wherein d represents vector dimension, q represents a query, k is a dimension index, and wherein weight w for the i th vector's j th term is calculated as follows:

w ij =TF ij ×log( N/DF j ); and

wherein TF j represents term frequency, N is a total number of query terms, and DF j is a number of extracted feature records that contain the i th vector's j th term;

responsive to receiving a term/phrase from an entity, means for evaluating the term/phrase via the multi-sense query in view of terms/phrases in the term clusters to identify one or more related term suggestions, wherein the identifying is based on a combination of FOO and a confidence value; and

means for returning at least one suggested term list ordered by the combination of FOO and confidence value, wherein multiple suggested term lists are generated when the term/phrase matches terms in more than one term cluster.

38. The computing device of claim 37 and wherein the entity is a computer-program application and/or an end-user.

39. The computing device of claim 37 , further comprising computer-program instructions executable by the processor for:

collecting means to collect historic query terms from a query log; and

determining means to determine ones of the historic query terms with the high FOO.

40. The computing device of claim 37 , wherein the evaluating means further comprise:

identifying means to identify a match between the term/phrase and term(s)/phrase(s) from one or more term clusters; and

responsive to identifying, generating means to generate related term suggestion(s) comprising the term(s)/phrase(s).

41. The computing device of claim 37 , wherein the generating means to generate the term clusters further comprise:

sending means to send respective ones of the high FOO historical queries to the search engine to obtain the search results;

extracting means to extract features from at least a subset of search results corresponding to the respective ones;

producing means to produce the term vectors from the features.

42. The computing device as recited in claim 37 , wherein the term clusters are a first set of term clusters, and wherein the computing device further comprises:

determining means to determine that there is no match between the term/phrase and the terms/phrases; and

responsive to the determining:

making means to make a second set of term clusters from calculated similarity of term vectors, each term vector being generated from search results associated with a set of low FOO historical queries previously submitted to the search engine; and

evaluating means to evaluate the term/phrase in view of terms/phrases of the second set of term clusters to identify one or more related term suggestions.

43. The computing device of claim 42 , and further comprising:

calculating means to calculate that there is no match between the term/phrase and term(s)/phrase(s) from the first set of term clusters, the first set being based on the high FOO historical queries; and

responsive to the calculating, identifying means to identify a match between the term/phrase and term(s)/phrase(s) from one or more of the second set of term clusters, the second set being based on the low FOO historical queries; and

responsive to identifying, generating means to generate related term suggestion(s) comprising the term(s)/phrase(s).