Multiple engine information retrieval and visualization system6701318Abstract An information retrieval and visualization system utilizes multiple search engines for retrieving documents from a document database based upon user input queries. Search engines include an n-gram search engine and a vector space model search engine using a neural network training algorithm. Each search engine produces a common mathematical representation of each retrieved document. The retrieved documents are then combined and ranked. Mathematical representations for each respective document is mapped onto a display. Information displayed includes a three-dimensional display of keywords from the user input query. The three-dimensional visualization capability based upon the mathematical representation of information within the information retrieval and visualization system provides users with an intuitive understanding, with relevance feedback/query refinement techniques that can be better utilized, resulting in higher retrieval accuracy (precision). Claims That which is claimed is: Description FIELD OF THE INVENTION
TABLE 1
System Features
FEATURES BENEFITS
Fusion of multiple retrieval Improved retrieval performance
engines over independent search engines
An architecture that supports Flexible search strategies
the addition of new search
engines
Search by keyword or document Tailored queries
example
Multiple query capability for a Emphasize best search engine
topic and queries for a topic
Document reduction Improved performance and
smaller document footprint
Partitioning of document corpus Reduces search space
based on similarity
Search screening Remove topical but irrelevant
documents
Manual Relevance Feedback Query refinement
Web-based user interface Familiar style facilitates
quick review of retrieved
documents and selection of
example documents for use as
additional queries
3-D Visualization of retrieved Facilitates intuitive
document sets identification of additional
relevant documents
The retrieval system 10 includes an input interface 12 for accepting at least one user interface query. The input interface 12 also allows users to build queries for a topic of interest, execute the queries, examine retrieved documents, and build additional or refine existing queries. A plurality of search engines are used for retrieving documents from the document database based upon the user search query, wherein each search query produces a common mathematical representation of each retrieved document. The plurality of search engines include n-gram search engine 14, a Vector Space Model (VSM) search engine 16 which, in turn, includes a neural network training portion 18 to query a document corpus 20 to retrieve relevant documents. Results of the retrieval engines 14, 16 are fused together and ranked. In one embodiment, the fusing together and ranking of the retrieved documents is performed by a ranking portion 22 of the computer system. The retrieval system 10 further comprises visualization display means 24 for mapping respective mathematical representations of the retrieved documents onto a display. The visualization display means 24 allow users to explore various aspects of the retrieved documents, and look for additional relevant documents by redefining the topic query corpus 26 via the input interface 12. As previously discussed in the background section, information retrieval is the dissemination of information in response to user input queries. The information to be retrieved is typically stored in documents of various forms. Query formats range from a single set of words or phrases, to a Boolean logical expression that combines sets of words and phrases, to a complete natural language sentence or paragraph. A full document may also be used as an input query. A user begins by defining a topic of interest, then proceeds to define one or more queries for that topic. User queries for the retrieval system 10 can thus take the form of keywords or phrases, an example document, and even document clusters. The retrieval system 10 utilizes an interactive multi-pass approach. It is not assumed that the information will be found immediately, and thus the user needs to interactively refine the search query. The retrieval system 10 allows the user to review the documents and select the documents most relevant to the topic. Relevant documents can be used as queries to further refine the topic. The user can then quickly query over the data with the additional queries. By combining multiple separate and independent retrieval technologies, the various strengths of each approach can be leveraged to develop a more robust information retrieval system. The retrieval system 10 illustrated in FIG. 1 uses an n-gram search engine 14 and a vector space model (VSM) 16 search engine. These two search engines represent an embodiment of the present invention, and one skilled in the art will readily realize that other search engines can be used in place of, or in addition to, the n-gram 14 and the VSM 16 search engines. Other search engines that can be incorporated into the retrieval system 10 include, but are not limited to those using the following retrieval strategies: probabilistic retrieval, inference networks, boolean indexing, latent semantic indexing, genetic algorithms and fuzzy set retrieval. All of these retrieval strategies, including n-gram 14 and VSM 16, are well known to one skilled in the art. For illustrative purposes, a comparison of the strengths and weaknesses of the n-gram 14 and the VSM 16 search engines are provided in Table 2.
TABLE 2
Retrieval Engine Strengths and Weaknesses
n-gram VSM
Strength unique terms (e.g. example documents used as
proper nouns) input
mis-spelled words document meaning
short documents (e.g.
e-mail)
Weakness long documents unique terms (e.g. proper
nouns - terms that did not
appear in the training corpus
and hence do not appear in
the VSM's dictionary)
With an n-gram search engine 14, an input query is partitioned into n-grams to form an n-gram query 30. An n-gram is a consecutive sequence of n characters, with n being a positive integer. The premise of an n-gram search engine 14 is to separate terms into word fragments of size n, then design algorithms that use these fragments to determine whether or not a match exists. For example, the first 15 tri-grams (3-grams) in the phrase "information retrieval" are listed below:
inf orm ati on_ ret
nfo rma tio n_r etr
for mat ion _re tri
The frequency of occurrence of n-grams can be used to distinguish/characterize the language of a document (3-grams), and as a means of gauging the topical similarity of documents (5-grams). The retrieval system 10 employs an n-gram filter based on work with Least Frequent Tri-grams (LFT), as represented in FIG. 1 by the n-grams least frequency training block 32. The retrieval system 10 moves an n-character sliding window over a document while recording the frequency of occurrence of different combinations of n-characters. A least frequency table 34 is built from a corpus of training documents 36, representative of the document collection. Relevant documents are rapidly identified by looking for the occurrence in the document of the least-frequent n-gram of a search string, such as a keyword. If the least frequently occurring n-gram of a search term is not present in a document, then the term itself is not in the document. If the least frequently occurring n-gram is present, the search continues for the entire string. In one embodiment of the retrieval system 10, a 3-character sliding window (3-grams) is used. For illustrative purposes, a tri-gram frequency analysis is performed on a representative sample of documents. From this analysis, a table of tri-gram probabilities is developed for assigning an occurrence probability p(t) to each of the unique tri-grams. The occurrence probability is expressed as: ##EQU1## The developed table is used to determine the LFT for any given query, and the frequencies were not evenly distributed throughout the tri-grams. When the documents used to train against are written in English, certain tri-grams (TER, ING, THE) occur very frequently, while others (JXF, DBP, GGG) occur only under highly unusual circumstances. The n-gram retrieval search engine 14 counts the number of occurrences of the string. Since typical early searches are keywords, the n-gram search engine 14 acts as a filter to quickly identify candidate documents to be reviewed early in the retrieval process. It is especially useful for phrases not in the dictionary/vocabulary 44 of the VSM search engine 16. The identified relevant documents are then used on the next pass through the retrieval system 10. Additionally, if the keyword phrase of interest is unique enough, the documents containing the keyword phrase are used to create a document subset. Further queries are performed on just the document subset, which increases the search speed. The retrieval system 10 also comprises a Vector Space Model (VSM) search engine 16 to represent documents in an n-dimensional vector space. The strengths and weaknesses of the VSM 16 are listed above in Table 2. In particular, a context vector model is implemented for the retrieval system 10. Words appearing in the document training corpus 40 are represented as vectors in the n-dimensional vector space, .omega..epsilon.{character pullout}.sup.n. The word vectors, .omega..epsilon.{character pullout}.sup.n, are normalized to unit length so that they all lie on the unit hyper-sphere and ##EQU2## The similarity of two words is measured by computing the cosine similarity measure of the associated vectors, .omega., v.epsilon.{character pullout}.sup.n. ##EQU3## This similarity measure is the cosine of the angle between the two vectors. The higher the value of the cosine angle(i.e., closer to +1), the smaller the angle between the two vectors. Since all of the word vectors are on the unit hyper-sphere, .parallel..omega..parallel..sub.2 =.parallel..omega..parallel.=1 for all .omega., the cosine similarity measure reduces to ##EQU4## A vector for each document, .omega..epsilon.{character pullout}.sup.n, is constructed based on the terms in a document. A query is considered to be like a document, so a document and a query can be compared by comparing their respective vectors in the vector space. Documents whose content, as measured by the terms in the document, correspond most closely to the content of the query are judged to be the most relevant. The documents are retrieved through keyword, word clusters (series of words), and example document queries mapped into the n-dimensional vector space. The documents whose respective vectors are a minimal distance from the query's vector are retrieved. Many Vector Space Models count the frequency of occurrence of words and phrases to build the document queries 42. Frequency counts are done on the individual document files and for the document corpus 20. As new data are entered, the frequency counts must be updated or recomputed. Queries 42 are built upon the highest frequency counts for documents, necessitating more computation time. The retrieval system 10 creates an entirely mathematical representation of a document, and builds queries 42 from that representation. The mathematical representation allows consistent grouping of the words so that they can be compared. Using only a mathematical representation offers several advantages. One advantage is that additional documents can be added to the document corpus 20 without having to recalculate word occurrence frequencies. Other advantages include reduced documents, small vectors, a small index, and minimal calculations are required to build positive and negative queries, i.e., no document recalculation is required. Also, document size independence and similarity equations are simplified. Keywords, keyword phrases, single documents, and document clusters are provided as input to the retrieval system's 10 VSM component as queries. Queries 42 constructed by the VSM 16 can be broadly or narrowly focused, depending on the keywords, phrases, and example documents used in the queries. The document's score is obtained by computing the distance between the vectors representing the query and the document. Scores for relevant documents typically range from approximately 0.45 to 1. The closer to 1, the better the document matches the search query. Experiments have shown that the VSM's 16 strongest performance results from the use of example documents and document clusters. As passes are completed, top query results are reviewed and identified as relevant or irrelevant. Relevant documents from the query are input to the next pass of the VSM search engine 16. A neural network (NN) training portion 18 is used within the retrieval system 10 to train the word vectors, .omega..epsilon.{character pullout}.sup.n, in the VSM search engine 16. The NN training algorithm 18 is based on the training rule for Kohonen's Self-Organizing Map. This unsupervised learning algorithm organizes a high-dimensional vector space based on features within the training data so that items with similar usage are clustered together. Heavier weights are placed on words in closer proximity. The neural network 18 also accounts for training that has already taken place by adjusting the lesser trained words. The training algorithm is described as follows: 1. Initialize context vectors for each word in the dictionary. The values in the vector are chosen at random, and 2. For N training epochs 3. For each training document a) Set word_index to 0 b) Set neighbor_word_index to word_index +1 c) Adjust context vector for word (word_index) and word (neighbor_word_index). This accounts for proximity of words in the document, weighting more heavily words that are closer in proximity. This also accounts for training that has already taken place by adjusting highly trained words less. The algorithm is given as i) d=w.sub.1 -w.sub.2, where w.sub.1 =context vector for word (word_index), and w.sub.2 =context vector for word(neighbor_word_index) ii) w.sub.1 (k+1)=w.sub.1 (k)-.mu..sub.w k.sub.1 d, where .mu..sub.w =learning rate for word neighbor adjustments, and k.sub.1 =(w1_num_updates* (neighbor_word_index--word_index)) .sup.-1 iii) w.sub.2 (K+1)=w.sub.2 (k)+.mu..sub.w k.sub.2 d where k.sub.2 =(w2_num_updates* neighbor_word_index--word_index)) .sup.-1 iv) Renormalize w.sub.1 and w.sub.2 d) if (neighbor_word_index--word_index)<max_neighbor_words i) Increment neighbor_word_index ii) Go to 3c, else if not done with all words in document iii) Increment word_index iv) Go to 3b e) Calculate context vector for document f) For every word in the document, adjust the word's context vector so that it is closer to the document's context vector. This steers words and the document that contains them towards a cluster of similar meaning in the vector space. i) d=w-v, where w is the context vector for the word, and v is the context vector for the entire document ii) w(k+1)=w(k)-.mu..sub.d d where .mu..sub.d is the learning rate for word-to-document adjustment (.mu..sub.d <<.mu..sub.w) iii) Renormalize w iv) Note that early in the training, .mu..sub.w k.sub.i should be much larger than .mu..sub.d, since the document's context vector is very random until some word training has been done. Eventually, .mu..sub.d may dominate .mu..sub.w k.sub.i since k.sub.i shrinks rapidly as word training continues. 4. Get next document and go to 3 5. Finish training epoch a) Increment epoch_count b) Reduce .mu..sub.w. This ensures that as training nears completion, updates are small even for words that have not been trained much. c) If this is not the last epoch, go to 2, else done. Application of the neural network 18 training rule causes the vectors for words with similar meaning, as defined by similar usage in the document corpus 20, to converge towards each other. Upon completion of training, words are assigned to the closest axis in the n-dimensional vector space. An axis represents the direction of each vector in the n-dimensional space. Words are assigned to the closest axis by taking the highest cosine similarity among the axes. FIG. 2a shows an example vector in a 3-dimensional space, with the words "budget" and "music" being assigned to an axis. FIG. 2b shows a fairly even distribution of words through the first thirty axes. A document is cleaned by removing stop-words, performing stemming, and inserting compound words. A document is further reduced by examining the word axis representation. Document cleaning and reduction is performed in portion 46, as illustrated in FIG. 1. The number of words in each axis are also counted. The axes containing the highest percentage of words are retained until over 70% of the document is represented. In lieu of 70%, other percentage levels are acceptable. FIG. 3 shows the percentage of words in the first 30 document axes before reduction and after reduction. FIG. 4 shows text of a document that has been reduced from 58 axes to 26 axes. Reducing the words in a document increases the speed of the document representation and improves the query matching. Reduction increases the match because it removes terms that lower the values of the higher axes used to match other documents. Tests have been performed to determine what is a sufficient amount of document reduction without removing too many words. Reduction beyond 70% begins to remove some of the unique words of the document. Therefore, documents reduced up to 70% give the best performance. An example of test results performed on over 2000 web news stories is shown in FIG. 5. The topic was "find awards/honors given to people and things, i.e., television shows". The search started with the set of words: honor, award, mvp, noble prize, and hall of fame. The search was performed on documents which contain 100%, 90%, 80%, 70%, and 60% of the original document. FIG. 5 shows the results using the keyword search. The top 10 relevant documents found in the reduction were then used in the second pass to further define the query. FIG. 6 shows the results of the keyword and document example search. Information for a document is stored in two ways: a document context vector, and an axis context vector. The document context vector, X.epsilon.{character pullout}.sup.n, is the sum of all the words that are in the document after clean up and reduction. It is used for building a single document query, and to compare documents and queries. A document's axis context vector is the sum of the words in each axis vector after document clean up and reduction. The axis context vector is used for building a query for a document cluster and the 3-D display. A query, whether it consists of keywords, keyword clusters, example documents, or document clusters, is considered to be like a document. A query is represented by an n-dimensional context vector, y.epsilon.{character pullout}.sup.n. Therefore, a query can be compared within the document corpus 20. Positive queries are combinations of words and documents. Single word and single document queries use the entire word/document as the query. When multiple documents are used to build the query, the document axes with the highest usage are used to build the query. Table 3 shows three documents with an example vector size of 6 to build a positive query. In this example, the query is built using axes 1, 3, and 5 since they contain the highest axis usage among the three relevant documents. The default is to use the axis used by all the documents, and the next highest used axes. The user is allowed to lower or raise the number of axes used to build the query.
TABLE 3
Positive Query Example
Axis Doc 1 Doc 2 Doc 3
1 x x x
2 x
3 x x x
4 x
5 x x
6 x
When building a multiple document query, the documents should be similar. That is, a cosine measure of similarity should be greater then 0.6. Documents lower then 0.6 are not very similar, and it is not beneficial to combine them into one query. FIG. 7 shows examples of multiple query retrieval. The multiple query done on documents with a measure of similarity greater then 0.6 retrieves more relevant documents in the top 25 retrieved documents. The documents retrieved are mainly the documents retrieved by the individual queries, with the multiple query identifying one new relevant document in the top 25. The multiple queries can be used to help further identify relevant documents, through repetition of the document being repeated in several queries. Multiple document queries with dissimilar documents identify more irrelevant documents. These queries correspond to a cosine measure of similarity less then 0.6. Building a negative query is very similar to building a positive query. Instead of looking for the most frequently used axes, the least frequently used axes are examined. That is, the least frequently used axes in the specified relevant documents relative to the axis used by the bad documents are examined. Table 4 shows a negative query being built. In this example, the least frequently used axes 2, 4, and 6 are used with respect to the good documents to build the negative query. As with building the positive query, the user can also raise or lower the number of axes used in building the negative query.
TABLE 4
Negative Query Example
Relevant Documents Bad
Axis Doc 1 Doc 2 Doc 3 Doc 4
1 x x x x
2 x x
3 x x x
4 x x
5 x x
6 x x
The retrieval system 10 initiates each retrieval engine to calculate a document's score for each query. The retrieval engines maintain only the high level scores. The retrieval system 10 standardizes the scores from each retrieval engine to range from 0 to 1. The user can adjust the lowest acceptable score and retrieval engine weight to effect score results to favor/disfavor a particular retrieval engine. A ranking processor 22 uses an algorithm to fuse the results of the retrieval engines and ranks the documents based on the number of times the document was selected, highest score, lowest score, average score, location in the query list and number of retrieval engines locating the document. Irrelevant documents and queries can be removed. Each topic is made up of multiple queries from each of the retrieval components. The scores are scaled by query and for the entire topic, i.e., all the queries. Each set of scores are a separate entry into the ranking algorithm. The query 42 for the VSM 16 contains the entire vector and the axis vectors. Scores are obtained by taking the cosine similarity measure of the query vector with the entire vector of each of the documents in the document corpus 20. The closer to one the better the match, where only high positive scores are kept. If none of the document scores equals the value 1, then the documents are scaled based on the highest score for the query. This is a quick method of increasing the scores on potentially relevant VSM documents, thus allowing fusing of the VSM highest results with the n-gram result scores. A cosine similarity measure of the query vector against the corresponding axis of the documents in the document corpus 20 can also be applied. In this case, applying a cosine similarity measure is also useful for word queries. However, the axis for the document typically contain a large number of axes, and a large number of documents unrelated to the topic are retrieved. Using the example search that retrieved 2000 web news stories, retrieved documents describing "awards/honors given to people and things receiving awards" shows the effect of using the document axis queries, as shown in Table 5 and FIG. 8. Using the VSM document axis the precision and recall scores are lower, and 16% more irrelevant documents were retrieved. This causes more work to be performed in locating relevant documents.
TABLE 5
Example of Using VSM Document Axis
n-gram, n-gram, VSM, VSM
VSM, VSM word axis, VSM
word axis document axis
Number relevant documents 12 17
located
Number relevant documents 17 17
in the corpus
Number documents retrieved 42 705
Referring back to the n-gram retrieval engine 14, the number of occurrences of the least frequent term (n-gram) are counted. Since most early searches on the topic are keyword(s), the filter quickly identifies candidate documents to be reviewed early in the retrieval process. The filter is especially useful for keywords or phrases which may not have appeared in the document corpus 40 used to train the VSM 16 component of the retrieval system 10. The identified relevant documents are used on the next pass through the retrieval system 10. A tri-gram was selected due to the speed of processing and small amount of storage for the least frequency table corresponding to block 34 in FIG. 1. The n-gram frequency count can vary widely. The n-grams need to be in the range from 0 to 1 to correspond with the standardized range. The documents having a large number of the specified tri-grams (3-grams) appearing in one document were examined. The documents were divided into three groups: few matches, high matches, and scaled documents. The few matches were removed from the scaling calculation. Few matches consists of a large number of documents (greater than 50) with a couple of matches (approximately ranging from 1-20 matches) per document. High matches have a large number of matches in a single document. These documents need to be examined and are set to a value 1. The remaining documents are scaled between 0 and 1. The scaling helps to identify documents that have the most 3-gram matches and should be reviewed. As a further note, taking the mean or dividing by the largest number does not provide a good representation of the n-gram documents. Furthermore, looking at a document that had only had a few matching occurrences is not desirable. A statistical method is used to locate the clusters of interest to be scaled. The mean and standard deviation are calculated without the largest n-gram frequency value. If the largest value fits within three standard deviations of the mean, then the number is used as the scaling factor. If the largest value does not fit, it is considered to be outside the cluster range. When the process is repeated, it takes out the next largest value. Accordingly, this process is repeated until the largest removed frequency value falls within the range of the third standard deviation. Numbers larger than the selected scaling number are set to one. An example of calculating the scaling value is shown in Table 6.
TABLE 6
Scaling Example
Original Removed Removed
Data Largest # Next Largest #
n-Gram 1 1 1
Frequency 2 2 2
Occur- 1 1 1
rence 3 3 2
12 2 2
2 2 1
2 1 1
1 1
1
Mean 2.77 1.625 1.42
Standard 3.52 0.744 0.534
Deviation
3 Standard 13.33 3.857 3.03
Deviations
Comment If used Remove the Remove the next largest # -
largest largest # - 12. 3. The # 3 does fall within
value. The # 12 does not three standard deviations.
12/12 = fall within three Use 3 as the scaling factor.
1 standard deviations All numbers larger than 3
3/12 = of the remaining are set to
0.25 values 1.
2/12 = 12 = 1
0.16 3/3 = 1
1/12 = 2/3 = 0.66
0.08 1/3 = 0.33
It is The documents with
doubtful values 1 - 0.66
that would probably
anything be reviewed.
other
then the
document
with the
value of
one
would be
reviewed
Table 7 shows the number of files used to calculate the n-gram scaling factor applied to data provided for a TREC-6 conference. TREC is an acronym for Text REtrieval Conference. This conference is a workshop series that encourages research in information retrieval from large text applications by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results.
TABLE 7
Number of Files Used to Calculate the n-gram Scaling Factor
Query #301 Query #337 Query #350
# Files 79,777 550 3,271
Range of files dropped out 1-18 1-3 1-10
(# matches/file)
# of files dropped out 79,155 434 2,547
Average 60 10 48
# Documents Scaled 522 73 538
Range of files where 60-829 4-10 48-7,789
documents = 1
# Documents = 1 100 43 186
The user specifies the lowest acceptable score for each of the retrieval engines. This helps eliminate the lower scoring documents. This also controls the number of documents shown to the user. The higher the number, the fewer the documents shown to the user. Additionally, the documents should reveal better matches to the query. Each retrieval engine is assigned a specific percentage by the user. The document scores for the retrieval engine are reduced by the specified percentage. Depending upon the query type, different retrieval engines can be emphasized. For example, potentially misspelled words may put more emphasis on the n-gram search engine 14. Document example queries place more emphasis on the VSM search engine 16. An algorithm ranks the document scores from the different retrieval engines. The algorithm rates the following items: number of times document identified per query and per retrieval engine, maximum and minimum score, average score and penalty points. An algorithm performing these functions is well known to one skilled in the art and will not be described in further detail herein. The ranking is thus determined by a score. Each item is ranked except for the penalty points. A higher number results in a lower score. The individual items are totaled and the lowest final score indicates the best document. Penalty points are assigned to a document based on the retrieval engine and the document location in the ranked list. A penalty is added to a document for each retrieval engine not identifying it as relevant. Multiple engines retrieving the document is a strong indication of a relevant document. The score must be above the supplied value after it is calculated according to predetermined scaling and retrieval engine weight. If all the engines retrieve the document, then the penalty score is 0. Referring to FIG. 9, the value 200 was chosen because it supplied enough of a penalty that it moved a document's rank below documents that appeared in more then one method. The algorithm allows each document to receive a penalty point for its location in each query list. This is intended to reward the documents that are located close to the top of the list in the individual queries, by assigning fewer penalty points. FIG. 10 further illustrates the penalty point assignment. Once the penalties are assigned for each query, the document's final list location based on all the queries results is calculated. The final location is based on the scores of the number of times the document is identified, maximum, minimum, average score, number of retrieval engines locating the document, and the list location penalty. In addition, location of the individual queries can also improve the results. High precision in the retrieval system 10 is derived by providing users multiple input interaction modes, fusing results obtained from multiple information retrieval search engines 14 and 16, each supporting a different retrieval strategy, and by supporting relevance feedback mechanisms. The basic premise of relevance feedback is to implement information retrieval in multiple passes. The user refines the query in each pass based on results of previous queries. Typically, the user indicates which of the documents presented in response to an initial query are relevant, and new terms are added to the query based on this selection. Additionally, existing terms in the query can be re-weighted based on user feedback. Primary user interaction with the retrieval system 10 is through a web-browser-based user interface 12. Users can build and tailor queries as the topic of interest is further defined, moving from a generic search to specific topic areas through query inputs. Queries may consist of a single keyword, multiple keywords (or phrases), keyword clusters, an example document, and document clusters. In addition, a user can increase or decrease the system precision, effecting the number of documents that will be rated as relevant. The weights on the retrieval engines can be modified to favor different engines based on the type of query. A set of documents retrieved for a particular topic many exhibit a variety of aspects. This can be seen, for example, in a search retrieving information about the Oklahoma City bombing of 1996. There are relevant articles about the bomb, damage from the bomb blast, rescue work, the victims, suspects, the Timothy McVeigh trial, and the Terry Nichols trial, just to name a few. In particular, the retrieval system 10 is used to search for documents relevant to the trial of Timothy McVeigh for the Oklahoma City bombing. The document corpus consists of over 2000 news stories from the CNN web site on a variety of topics. In this case, a user begins by creating a new topic of interest: McVeigh Trial. Since this is a new topic, there are no queries associated with the topic. So the user creates a query by entering a few keywords: "McVeigh", "trial", "bombing", and "Oklahoma City", as shown in FIG. 11a. This initial query is labeled "words-1" and added to the list of queries for a McVeigh trial topic, as shown in FIG. 11b. The user has the retrieval system 10 execute this query. As illustrated in FIG. 11c, a ranked list of documents, complete with score, is returned to the user. Clicking on the document retrieves the text through HTML hyperlinks. This enables a user to determine the relevance, from his point of view, of a document to the topic. Top documents can be reviewed, and both relevant and irrelevant documents are identified and marked as such. Irrelevant documents are filtered from subsequent queries. Removal of the higher-scoring irrelevant documents allows lower scoring documents to be accepted on the final result list. Documents can also be marked for use as examples in additional queries for the topic. Such stories are then added to the list of queries, as shown in FIG. 11d. In the retrieval system 10, visualization display means comprises an n-dimensional document visualization display for enhancing user understanding of the retrieved document set. This tool supports multiple levels of data abstraction, clustered document presentation, data thresholding, and a variety of user interaction paradigms. The n-dimensional document visualization display enables the user to view different aspects of the document's topic. The visualization display means displays a similarity measure of the documents. The information retrieval system 10 is thus able to reduce the display down to the most important aspects of the document. The Oklahoma City bombing stories have a number of different aspects: the bomb, building damage, the victims, victim's families, the Timothy McVeigh trial, etc. Displaying documents in a 3-dimensional space enables a user to see document clusters, the relationships of documents to each other, and also aids in the location of additional documents that may be relevant to a query. Documents near identified relevant documents (through queries) can be easily reviewed for topic relevance. The user is able to manipulate the dimensional view to gain new views of document relationships. Changing the document's dimensionality allows the information to be viewed for different topic aspects to aid in further identification of relevant documents. FIG. 12a shows an example of the 3-dimensional viewer containing documents retrieved for the McVeigh trial topic using the query keywords: McVeigh, trial, Oklahoma City, and bomb. Document locations are represented in space by a box. Additionally in this view, documents determined as relevant by the information retrieval system 10 displays the document name next to the box. Clustering of documents can be observed in several areas. A first cluster is with respect to the McVeigh trial stories, plus additional stories related to the topic, but not identified by retrieval system 10. A second cluster is with respect to the Bosnia stories deals with bombing, and are near the keyword "bomb". A third cluster is with respect to the O. J. Simpson trial stories, and appears near the word "trial". Referring to FIG. 12a, each document in the retrieved document corpus may be represented mathematically in the 3-dimensional space by a colored cube. For example, a red cube could represent a query request--in this case the words "trial", "bombing", "McVeigh" and "Oklahoma City". Yellow text could be used to indicate the relevant documents found through text queries submitted to the retrieval system 10. Additional colors may be used to indicated document clusters, i.e., documents related in some aspect. The colors selected to represent a query request, relevant documents and document clusters are not limited to red and yellow. These colors are for illustrative purposes, wherein other colors are acceptable for indicating such information to a user. Therefore, using different colors to represent different aspects of the retrieved documents on the display would allow the user to more quickly identify the relevant information to be retrieved. The information retrieval system's 10 3-dimensional view enables a user to quickly identify document clusters around queries by plotting spheres around each query. FIG. 12b shows spheres drawn around the keywords in the "words-1" query. A user can "fly" into the sphere to explore the documents clustered around a query. FIG. 13 shows the clustering of documents in several areas. As previously stated, Cluster 1 corresponds to the McVeigh trial stories, plus additional stories related to the topic not identified by the information retrieval system 10. Cluster 2 corresponds to Bosnia stories dealing with bombing, and are near the keyword "bomb". Cluster 3 corresponds to the O. J. Simpson trial stories, and appears near the word "trial". FIG. 13 clusters found in the 3-dimensional keyword view of higher ranking documents in the corpus include McVeigh trial stories, Bosnia bombings, and O. J. Simpson trial stories FIG. 14a shows a close-up focused on the word "trial", and the text has been turned on so that the file names of documents represented by the boxes are also displayed. It is noted that the 0. J. Simpson trial articles have clustered near the word trial. A user can double click on any of the boxes to bring up the associated document. FIG. 14b shows another perspective on the whole corpus of retrieved documents using different dimensions. Again, it is noted that the relevant stories appear to separate from the other stories using the retrieval system 10. To look at some of the documents which have not been identified, the user can double click on any of the boxes to bring up the associated document, and make an inspection to determine whether or not it is relevant to the Timothy McVeigh trial. The information retrieval system 10 has been evaluated using the benchmark data and query sets provided by the National Institute of Standards and Technology as part of the 6.sup.th Text REtrieval Conference (TREC-6). The results obtained demonstrated high precision for limited-sized retrieval sets. As previously stated, the Text REtrieval Conference (TREC) workshop series encourages research in information retrieval from large text applications by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TREC has become the major experimental effort in the field of information retrieval. The information retrieval system 10 of the present invention competed in the Manual Ad Hoc Retrieval task for the large data set. During TREC, the n-gram search engine 14 was used as a high speed filter to provide document examples. The VSM search engine 16 was used for final retrieval and document scoring. There are two primary measures employed in the TREC competition; precision and recall. Precision is the percentage of documents from the retrieved set that are relevant to the query. Recall is the percentage of relevant documents retrieved from the total number of relevant documents available within the collection. Time constraints and interest-level limit the typical user of an information retrieval system to reviewing the top documents before determining if the results of a query are accurate and satisfactory. The user needs precise, representative documents at the top of the list to make this determination. Accordingly, the information retrieval system 10 emphasizes precision (accuracy) and speed while retrieving relevant documents high on the list. The information retrieval system 10 permits the user to build and tailor the query as he or she further defines the topic. The system also permits movement from a generic search to a specific topic area through query inputs. In comparing precision results using the present invention with other TREC teams that retrieved a similar number of documents, the information retrieval system 10 maintains a high level of precision for the top 5, 10, 20 and 30 documents retrieved. As FIG. 15 illustrates, precision for the Applicants' invention (which is assigned to Harris corporation) is higher than the other TREC teams that retrieved more relevant documents. This fits well with the fact that time constraints and interest-level on the part of an information retrieval system user often limit the user to reviewing the top documents before the user determines if the results of a particular query were accurate and satisfactory. During TREC, the documents were scored for the entire topic, i.e., all the queries were combined into the ranking. A modified algorithm included individual query list locations into the overall scoring, which improved the results, as shown in FIG. 16. Conclusion The information retrieval system 10 is an efficient, high-level precision information retrieval and visualization system. The information retrieval system 10 allows interactive formation of query refinement, and fuses results form multiple retrieval engines to leverage the strengths of the each one. In addition, the information retrieval system 10 allows for efficient maintenance, i.e., making it easy to add new documents. The information retrieval system 10 also allows for multiple dictionaries and vocabularies, thus allowing a user to develop role-based dictionaries and/or vocabularies for searching specific databases. The information retrieval system 10 provides a user interface for user interaction as well as a 3-dimensional presentation of the retrieved documents for more efficiently exploring the documents retrieved in response to a user's search query. Many modifications and other embodiments of the invention will come to the mind of one skilled in the art having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the invention is not to be limited to the specific embodiments disclosed, and that the modifications and embodiments are intended to be included within the scope of the dependent claims.
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