Concept matching of natural language queries with a database of document concepts

Claims

What is claimed is:

1. A computer-implemented process for forming a search query for searching a document database by a computer-implemented search process, the search process identifying documents likely to match the search query by matching individual terms of the search query to individual terms and sequences of terms in the document database, the process for forming the search query comprising:

a) providing a first database containing a plurality of phrases derived from domain specific natural language phrases, each of said phrases consisting of a plurality of terms in original order;

b) input to a computer an input query composed in natural language and comprising a plurality of terms arranged in a user-selected order;

c) parsing said input query into separate terms in an ordered sequence, the order of the terms in the sequence being the same as the order of the terms in the input query;

d) selecting groups of terms, each group consisting of a plurality of successive terms of the sequence;

e) comparing each group of terms to each phrase in said first database to identify each group of terms of the input query that matches a phrase in said first database; and

f) replacing each identified group of terms of the input query by a representation of the matching phrase from said first database, the search query comprising each representation substituted for groups of terms of the input query and each remaining term of the input query.

2. A computer-implemented process for forming a search query according to claim 1 further including providing a second database containing a plurality of topics having a descriptive topical text and an associated unique numerical key, each topical text being composed of a plurality of terms, comparing the terms of the input query or the search query to each of the terms of the topical texts in the second database, assigning a statistical weight to each topical text reflecting the probability that the topical text matches the query, ranking the topical texts based on the statistical weight, and inserting into the search query the numerical keys associated with up to n highest ranked topical texts, where n is a predetermined integer.

3. A computer-implemented process for forming a search query according to claim 2 wherein the step of inserting the numerical keys into the search query includes comparing the statistical weights of the topical texts to a predetermined threshold, and inserting the numerical keys into the search query which are associated with topical texts having statistical weights which exceed the predetermined threshold.

4. A computer-implemented process for forming a search query according to claim 2 wherein the statistical weight for each topical text is determined by comparing each term of the query to each term of the topical text, determining the probability that the query term is a correct descriptor to the topical text in accordance with the relationship

P(c.sub.i .vertline.d.sub.j)=0.4+0.6.multidot.idf.sub.i .multidot.tf.sub.ij,

where idf.sub.i is based on the frequency of texts in the second database containing the query term and tf.sub.ij is based on the frequency with which the query term appears in the respective topical text, and for each topical text adding the probabilities for all terms of the query and normalizing the sum of the probabilities by the number of terms in the query.

5. A computer-implemented process for forming a search query according to clam 1 wherein the input query may include one or more groups of terms forming citations, each citation including numerical terms, said process further includes:

g) identifying each group of terms forming a citation in said input query, and

h) replacing each identified group of terms forming a citation by a citation word which comprises a representation of the citation.

6. A computer-implemented process for forming a search query according to claim 5 wherein the citation word comprises the numerical terms of the group of terms forming the citation and a predetermined word-level proximity number.

7. A computer-implemented process for forming a search query according to claim 1 further including before step f, removing stopwords from the input query.

8. A computer system for forming a search query according to claim 7 wherein the first database further includes a plurality of stopwords, fifth comparing means for comparing each term in said register means to the stopwords in the first database, and deleting means responsive to the fifth comparing means for deleting each term from said register means that matches a stopword.

9. A computer implemented process for forming a search query according to clam 1 further including, before step (f) for each identified group of terms, identifying those terms which are shared by two successive identified groups of terms, and assigning the shared term to only one of the two successive groups.

10. A computer system for forming a search query according to claim 7 further including third processing means for identifying those terms which are shared by two successive identified groups of terms and assigning the shared term to only one of the two successive groups.

11. A computer-implemented process for forming a search query according to claim 1 further including stemming the terms of said input query.

12. A computer-implemented process according to claim 1 further including, after step

g) comparing each term and representation of the search query to individual terms of a document database containing representations of the contents of texts of a plurality of documents,

h) identifying the number of occurrences of respective terms, representations and partial representations of the search query in the representations for each document,

i) assigning a statistical weight to individual documents based on each occurrence of respective terms, representations and partial representations of the search query in the representations for each document, and

j) identifying the probability that the document matches the search query by summing the statistical weights.

13. A computer-implemented process according to claim 12 wherein the statistical weight for each occurrence of a representation in a document matching a part of a representation of the search query is a fraction of the statistical weight for an occurrence of a representation in the document that matches the corresponding full representation of the search query.

14. A computer-implemented process according to claim 1 further including, after step f,

g) comparing each term and representation of the search query to individual terms of a document database containing representations of the contents of texts of a plurality of documents,

h) identifying terms of a document that at least partially match a representation of the search query, and

i) assigning a statistical weight to the document based on the number of occurrences of the partially matched terms in the document.

15. A computer system for forming a search query for searching a document database by a computer-implemented search process, the search process identifying documents likely to match the search query by matching individual terms of the search query to individual terms and sequences of terms in the document database, said system comprising:

a) a first database consisting of a plurality of phrases derived from domain specific natural language phrases, each of said phrases consisting of a plurality of terms in original order;

b) register means for storing an input query composed in natural language, the input query comprising a plurality of terms arranged in a user-selected order;

c) parsing means responsive to said register means for parsing said input query into separate terms;

d) first processing means for forming an ordered sequence of terms, the order of the terms being the same as the order of the terms in the input query;

e) selecting means for selecting groups of terms, each group consisting of a plurality of successive terms of the sequence;

f) first comparing means for comparing each group of terms in said register means to each phrase in said first database to identify each group of terms in the register means which matches a phrase in said first database; and

g) second processing means for replacing each identified group of terms in said register means by a representation of the matching phrase in said first database.

16. A computer system for forming a search query according to claim 15 wherein said read only memory further contains a second database consisting of a plurality of topics each having a descriptive topical text and an associated unique numerical key, each topical text being composed of a plurality of terms, second comparing means for comparing the terms of the input query or the search query to each of the terms of the topical texts in the second database, third processing means for assigning a statistical weight to each topical text reflecting the probability that the topical text matches the query, ranking means for ranking the topical texts based on the statistical weight, said register means being responsive to the ranking means to store the numerical keys associated with up to n highest ranked topical texts, where n is a predetermined integer.

17. A computer system for forming a search query according to claim 16 further including third comparing means for comparing the statistical weight of the topical texts to a predetermined threshold, said register means being responsive to the third comparing means to store numerical keys which are associated with topical texts having statistical weights which exceed the predetermined threshold.

18. A computer system for forming a search query according to claim 16 further including fourth comparing means for comparing each term of the query to each term of the topical text, fourth processing means for determining the probability that the query term is a correct descriptor of the topical text in accordance with the relationship

P(c.sub.i .vertline.d.sub.j)=0.4+0.6.multidot.idf.sub.i .multidot.tf.sub.ij,

where idf.sub.i is based on the frequency of texts in the second database containing the query term and tf.sub.ij is based on the frequency with which the query term appears in the respective topical text, adding means for adding for each topical text the probabilities for all terms of the query, and normalizing means responsive to the adding means for normalizing the sum of the probabilities by the number of terms in the query.

19. A computer system for forming a search query according to claim 15 wherein said input query may include one or more groups of terms forming citations, each citation having numerical terms said computer system further including:

h) fifth processing means for identifying each group of terms forming a citation in said input query, and

i) sixth processing means for replacing each identified group of terms forming a citation by a citation word which comprises a representation of the citation.

20. A computer system for forming a search query according to claim 19 wherein the citation word formed by the sixth processing means comprises the numerical terms of the group of terms forming the citation and a predetermined word-level proximity number.

21. A computer system for forming a search query according to claim 15 further including means for stemming the terms of said input query.

22. A computer system according to claim 15 further including,

h) a second database containing representations of the contents of texts of a plurality of documents, each of said representations comprising a plurality of terms,

i) fifth comparing means responsive to the second processing means and the second database for comparing each term and representation of the search query to individual terms of the second database,

j) seventh processing means responsive to the fifth comparing means for identifying the number of occurrences of respective terms, representations and partial representations of the search query in the representations for each document,

k) eighth processing means responsive to the seventh processing means for assigning a statistical weight to individual documents based on each occurrence of respective terms, representations and partial representations of the search query in the representations for each document, and

l) summing means responsive to the eighth processing means for identifying the probability that the document matches the search query by summing the statistical weights.

23. A computer system according to claim 22 wherein the eighth processing means assigns a statistical weight for each occurrence of a representation in a document matching a part of a representation of the search query as a fraction of the statistical weight for an occurrence of a representation in the document that matches the corresponding full representation of the search query.

24. A computer system according to claim 15 further including

h) a second database containing representations of the contents of texts of a plurality of documents, each of said representations comprising a plurality of terms,

i) fifth comparing means responsive to the second processing means and the second database for comparing each term and representation of the search query to individual terms of the second database,

j) seventh processing means responsive to the fifth comparing means for identifying terms of a document that at least partially match a representation of the search query, and

k) eighth processing means responsive to the seventh processing means for assigning a statistical weight to the document based on each occurrence of matched terms in the document.

25. A computer-implemented process for forming a search query for searching a document database by a computer-implemented search process, the search process identifying documents likely to match the search query by matching individual terms of the search query to individual terms and sequences of terms in the document database, the process for forming the search query comprising:

a) providing a database containing a plurality of topics each having a descriptive topical text and an associated unique numerical key, each topical text being composed of a plurality of terms;

b) input to a computer an input query composed in natural language;

c) comparing the terms of the input query or the search query to each of the terms of the topical texts in the database;

d) assigning a statistical weight to each topical text reflecting the probability that the topical text matches the query;

e) ranking the topical texts based on the statistical weight; and

f) inserting into the search query the numerical keys associated with up to n highest ranked topical texts, where n is a predetermined integer.

26. A computer-implemented process for forming a search query according to claim 25 wherein the step of inserting the numerical keys into the search query includes comparing the statistical weights of the topical texts to a predetermined threshold, and inserting the numerical keys into the search query which are associated with topical texts having statistical weights which exceed the predetermined threshold.

27. A computer-implemented process for forming a search query according to claim 25 wherein the statistical weight for each topical text is determined by comparing each term of the query to each term of the topical text, determining the probability that the query term is a correct descriptor of the topical text in accordance with the relationship

P(c.sub.i .vertline.d.sub.j)=0.4+0.6.multidot.idf.sub.i .multidot.tf.sub.ij,

where idf.sub.i is based on the frequency of texts in the database containing the query term and tf.sub.ij is based on the frequency with which the query term appears in the respective topical text, and for each topical text adding the probabilities for all terms of the query and normalizing the sum of the probabilities by the number of terms in the query.

28. A computer system for forming a search query for searching a document database by a computer-implemented search process, the search process identifying documents likely to match the search query by matching individual terms of the search query to individual terms and sequences of terms in the document database, said system comprising:

a) a read only memory containing a database consisting of a plurality of topics each having a descriptive topical text and an associated unique numerical key, each topical text being composed of a plurality of terms;

b) register means for storing an input query composed in natural language, the input query comprising a plurality of terms arranged in a user-selected order;

c) first comparing means for comparing the terms of the input query or the search query to each of the terms of the topical texts in the database;

e) first processing means for assigning a statistical weight to each topical text reflecting the probability that the topical text matches the query; and

f) ranking means for ranking the topical texts based on the statistical weight, said register means being responsive to the ranking means to store the numerical keys associated with up to n highest ranked topical texts, where n is a predetermined integer.

29. A computer system for forming a search query according to claim 28 further including second comparing means for comparing the statistical weight of the topical texts to a predetermined threshold, said register means being responsive to the second comparing means to store numerical keys which are associated with topical texts having statistical weights which exceed the predetermined threshold.

30. A computer system for forming a search query according to claim 28 further including third comparing means for comparing each term of the query to each term of the topical text, second processing means for determining the probability that the query term is a correct descriptor of the topical text in accordance with the relationship

P(c.sub.i .vertline.d.sub.j)=0.4+0.6.multidot.idf.sub.i .multidot.tf.sub.ij,

where idf.sub.i is based on the frequency of texts in the database containing the query term and tf.sub.ij is based on the frequency with which the query term appears in the respective topical text, adding means