Abstract:
A query interpretation system exploits semantic annotations in keyword queries over a collection of text documents, casting semantic annotations produced by text analysis engines into a formal annotation type system. The system uses the annotation type system to enumerate various interpretations of a keyword query and automatically translate a keyword query into a set of interpretations expressed in some intermediate query language. The system returns a result list of documents by combining the results of executing one or more of these interpretations. Even though the system generates and uses a complex type system, a user is able to use simple keyword queries to locate documents.

Description:
FIELD OF THE INVENTION 
   The present invention generally relates to query systems and in particular, the present system relates to querying documents that are associated with semantic annotations. More specifically, the present system pertains to modeling such annotations as a type system and translating keyword queries into one or more complex queries against the this type system. 
   BACKGROUND OF THE INVENTION 
   Conventional information retrieval systems (also known as text retrieval systems or text search engines) view document collections as standalone text corpora with little or no structured information associated with them. However, there are two primary reasons why such a view is no longer tenable. First, modern enterprise applications for customer relationship management, collaboration, technical support, etc., regularly create, manipulate, and process data that contains a mix of structured and unstructured information. In such applications, there is inherently a fair amount of structured information associated with every document. Second, advances in natural language processing techniques has led to the increased availability of powerful and accurate text analysis engines. These text analysis engines are capable of extracting structured semantic information from text. Such semantic information, usually extracted in the form of semantic annotations, has the potential to significantly improve the quality of free text search and retrieval. 
   However, the architectures of conventional information retrieval systems are not explicitly designed to take advantage of semantic annotations. In particular, semantic annotations provide the capability for describing content in terms of types and relationships, that is concepts that are not intrinsic to conventional information retrieval systems. For example, a particular document in a corpus may contain a person name “John” and a telephone number for John: “555-1234”, but not the actual word “telephone”. A person may search on that corpus using the keyword phrase “John telephone”. However, a conventional retrieval system does not find the document since the keyword “telephone” is not present. In essence, conventional information retrieval systems merely recognize keywords but not the types into which a word or phrase may be categorized or the relationships between such types. 
   A conventional information retrieval system is typically designed to return a ranked list of matching documents in response to a keyword search query comprising search words or tokens. In a standard implementation of such a system, an entire corpus of documents is processed in advance to build an inverted index. This inverted index maps each token to a list of occurrences of that token. A token is usually a word or a phrase; however, a token can also be a more complex entity. 
   Upon receiving a keyword query, the inverted index is used to compute a list of candidate documents that are potentially relevant to the query. Each of these candidate documents is assigned a rank, using a pre-designed ranking formula. The rank ordered list of candidate documents is then presented to the user. Although this technology has proven to be useful, it would be desirable to present additional improvements. The tightly integrated architecture of conventional information retrieval systems directly maps a query to storage and index structures. Consequently, it becomes difficult to exploit available semantic annotations. In conventional information retrieval systems, the available semantic annotations can only be exploited in an ad-hoc fashion by hand crafting specialized ranking formulae. Such ad-hoc ranking formulae are difficult to construct and are very often not portable across document collections. As a result, every time an information retrieval system is deployed over a new document collection, a significant amount of time and effort is required to craft a ranking formulae appropriate to that collection. 
   What is therefore needed is a system, a computer program product, and an associated method for exploiting semantic annotations in executing keyword queries over a collection of text documents, allowing a user to search on a corpus and locate information based on types and relationships found in the corpus by, for example, a text analysis engine. The need for such a solution has heretofore remained unsatisfied. 
   SUMMARY OF THE INVENTION 
   The present invention satisfies this need, and presents a system, a service, a computer program product, and an associated method (collectively referred to herein as “the system” or “the present system”) for exploiting semantic annotations in executing keyword queries over a collection of text documents. The present system comprises an architecture and an associated query expansion algorithm for systematically and meaningfully exploiting semantic annotations. 
   The present system casts semantic annotations produced by text analysis engines into a formal annotation type system. Using the annotation type system, the present system translates a keyword query into a set of queries in an intermediate query language. Each of these intermediate queries is a specific interpretation of the original keyword query. In turn, each interpretation returns a list of documents when executed over the underlying annotated document collection. 
   Thus, the present system replaces the conventional one-stage retrieval model (keywords directly produce result documents) with a two-stage retrieval model (keywords producing interpretations which in turn produce documents). A key advantage of the present system is that even though a complex and powerful type system is used to model semantic annotations, a user is able to continue to use simple keyword queries to locate documents. All of the complex queries against the type system are automatically generated. 
   The present system provides a formal algorithm for exploiting semantic annotations, as opposed to conventional ad-hoc implementations based on ranking functions, thresholds, weights, etc. Since the present system employs a keyword search interface, there is no additional burden on the user to learn complex query languages over annotated text. The present system is easily generalized to take advantage of more complex annotations such as relationship annotations and co-reference resolution annotations. 
   The query execution architecture of the present system separates the semantic interpretation of document content (achieved through text analysis) from the semantic interpretation of keyword queries (achieved using the query expansion algorithm of the present system). This results in a more flexible architecture. Using the present system, a set of text analytic engines can be executed over an existing document collection and the results can be seamlessly used without changing any index structure, ranking algorithm, or query evaluation system. This is difficult to achieve using a conventional monolithic information retrieval architecture. 
   The present system comprises a modeler for generating a type system from the outputs generated by one or more text analysis engines, an annotation type system representing concepts that can be identified in a corpus of data, and an indexer for generating an interpretation index from the type system and the output of the text analysis engines. The present system further comprises an interpreter for translating a keyword search query into one or more precise interpretations, based on matching the keywords with the values in the interpretation index. 
   The present system may be embodied as an annotation-enhanced text retrieval system. The present invention provides means for the user to identify a set of documents to be queried. The present invention further provides means for the user to generate a type system, either automatically through analysis of the set of documents, or as specified by the user. The present invention provides means for the user to build an interpretation index over this type system. In addition, the present system provides means for the user to invoke the interpreter to interpret keyword queries and to retrieve documents matching the generated interpretations. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The various features of the present invention and the manner of attaining them will be described in greater detail with reference to the following description, claims, and drawings, wherein reference numerals are reused, where appropriate, to indicate a correspondence between the referenced items, and wherein: 
       FIG. 1  is a schematic illustration of an exemplary operating environment in which a query interpretation system of the present invention can be used; 
       FIG. 2  is a block diagram of the high-level architecture of the query interpretation system of  FIG. 1 ; 
       FIG. 3  is a diagram of an exemplary type system of the query interpretation system of  FIGS. 1 and 2 ; 
       FIG. 4  is a process flow chart illustrating a method of operation of the query interpretation system of  FIGS. 1 and 2 ; 
       FIG. 5  is a process flow chart illustrating a method of operation of the query interpretation system of  FIGS. 1 and 2  in generating a type system; 
       FIG. 6  is a process flow chart illustrating a method of operation of the query interpretation system of  FIGS. 1 and 2  in building an interpretation index  205 ; 
       FIG. 7  is a process flow chart illustrating a method of operation of the query interpretation system of  FIGS. 1 and 2  in generating a set of candidate interpretations for a keyword query; and 
       FIG. 8  is a process flow chart illustrating a method of operation of the query interpretation system of  FIGS. 1 and 2  in generating a specific candidate interpretation in the set of candidate interpretations for a keyword query. 
   

   DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
   The following definitions and explanations provide background information pertaining to the technical field of the present invention, and are intended to facilitate the understanding of the present invention without limiting its scope: 
   Entity Concept: Any semantic concept, instances of which are mentioned one or times in a document corpus, recognized, and extracted by a text analysis engine, is called an entity concept. For instance, given a collection of customer service reports mentioning names and contact information for various customers, examples of entity concepts include Persons, Organizations, Phone Numbers, Locations, etc. 
   Relationship Concept: A relationship between two or more entity concepts that is explicitly represented at least once in a given document corpus is called a relationship concept. For instance, given the entity concepts listed above, the relationship “Contact Number” that associates a given Person entity with a Phone Number entity that represents that person&#39;s phone number is an example of a Relationship Concept. 
   Type: A type is any formal representation of an entity or relationship concept using the modeling concepts of some standard data model. For instance, using the relational data model, a type can be viewed as a relation whereas using an object oriented data model, a type can be viewed as a “class”. The exact representation for a type is specific to a particular embodiment of this invention. 
   Attribute: Every type is associated with a set of one or more attributes that define the values associated with objects of that type. For instance, a Person type could be associated with attributes First name and Last name so that every object of that Person type has First name and Last name values. 
     FIG. 1  portrays an exemplary overall environment in which a system, a computer program product, and associated method (the “system  10 ”) for exploiting semantic notations in executing keyword queries over a collection of documents according to the present invention may be used. System  10  comprises a software programming code or a computer program product that is typically embedded within, or installed on a host server  15 . Alternatively, system  10  can be saved on a suitable storage medium such as a diskette, a CD, a hard drive, or like devices. 
   Users, such as remote Internet users, are represented by a variety of computers such as computers  20 ,  25 ,  30 , and can access the host server  15  through a network  35  by means of, for example, a keyword search user interface (UI)  40 . By utilizing a keyword search application such as the keyword search UI  40 , a user can search data stored in store/index  45 . 
   The store/index  45  can support standard keyword queries over documents as well as more complex precise queries (e.g., using XPath) over annotations. In one embodiment, the store/index  45  comprises multiple individual data management engines. One data management engine comprises an XPath-capable XML data store for the annotations and another data management engine comprises a standard information retrieval engine for keyword queries over the documents. 
   Computers  20 ,  25 ,  30  each comprise software that allows the user to interface securely with the host server  15 . The host server  15  is connected to network  35  via a communications link  55  such as a telephone, cable, or satellite link. Computers  20 ,  25 ,  30 , can be connected to network  35  via communications links  60 ,  65 ,  70 , respectively. While system  10  is described in terms of network  35 , computers  20 ,  25 ,  30  may also access system  10  locally rather than remotely. Computers  20 ,  25 ,  30  may access system  10  either manually, or automatically through the use of an application such as the keyword search UI  40 . While system  10  is described in terms of the keyword search UI  40 , it should be clear that computers  20 ,  25 ,  30  can access a keyword search interface implemented on the host server  15  via network  35 . 
     FIG. 2  illustrates a high-level hierarchy of system  10 . System  10  comprises an interpretation index  205 , an indexer  210 , a type system  215 , a modeler  220 , and an interpreter  225 . During off-line processing, the text analysis engines  50  execute a suite of pre-selected text analysis engines over a document collection in store/index  45 , producing annotations for the analyzed documents. The annotations produced by the text analysis engines  50  are serialized into a meaningful format that allows for efficient indexing and query-based retrieval. 
   The serialized annotations and the original documents are stored in the store/index  45 . For instance, annotations can be serialized into XML documents and stored in store/index  45 ; in this case store/index  45  is capable of efficiently supporting XPath queries. In addition to serialization, annotations are cast into a formal type system  215  and the interpretation index  205  is built over the type system  215 . 
   Upon receiving a keyword query from the keyword search UI  40 , the interpreter  225  matches the keyword query with the interpretation index  205  to generate a set of intermediate queries. For instance, user  230  formulates a search with two keyword queries, k1 and k2. System  10  interprets the query as five intermediate queries q1, q2, q3, q4, and q5. System  10  executes each of these intermediate queries over the store/index  45  to produce a list of documents. The interpreter  225  merges these individual lists and produces a single output list that is presented to user  230 . While system  10  is described for illustration purpose only with respect to intermediate queries expressed in SQL-like syntax, it should be clear that system  10  is applicable as well to, for example, any other query language of similar or higher expressive power. 
   An exemplary scenario comprising service reports from a customer relationship management database of an auto manufacturer illustrates operation of system  10 . Table 1 lists exemplary text analysis engines  50  that are executed over the exemplary database. Each annotation produced by these exemplary text analysis engines  50  is a structured object. Modeler  220  represents the output of the text analysis engines  50  as types, with each type having one or more named attributes. 
   
     
       
             
           
             
             
           
         
             
               TABLE 1 
             
           
           
             
                 
             
             
               Exemplary text analysis engines and their associated purpose in 
             
             
               extracting annotations from the exemplary data collection. 
             
           
        
         
             
               Text Analysis Engine 
               Purpose 
             
             
                 
             
             
               Named-Entity Person 
               Identifies names of persons occurring in text 
             
             
               Named-Entity 
               Identifies names of organizations occurring in text 
             
             
               Organization 
             
             
               Named-Entity 
               Identifies names of cities or other locations 
             
             
               City/Location 
               occurring in the text 
             
             
               Contacted 
               Links a Person p with an Organization o if the 
             
             
                 
               text indicates that p contacted o 
             
             
               Partner 
               Identifies those organizations that are partners 
             
             
                 
               of the auto manufacturer and lists an 
             
             
                 
               associated department for that partner 
             
             
               Topic 
               Identifies service reports that refer to engine 
             
             
                 
               problems or brake problems 
             
             
                 
             
           
        
       
     
   
     FIG. 3  illustrates an exemplary type system  300  that describes the annotation objects produced by the exemplary text analysis engines  50  when executed on the customer relationship management database. Each of the text analysis engines  50  analyzes the customer relationship management database for different concepts; i.e., department, city, organization name, person name, city name, engine problem, and brake problem. Table 2 illustrates exemplary instances of this type system including the strings that are generated by the text analysis engines  50 . 
   
     
       
             
           
             
             
             
             
             
           
         
             
               TABLE 2 
             
           
           
             
                 
             
             
               Exemplary strings generated by the text analysis engines for an 
             
             
               exemplary customer relationship management database. 
             
           
        
         
             
                 
               Type 
               Attribute 
               String 
               Values 
             
             
                 
                 
             
             
                 
               Partner 
               Department 
               String 1 
               Engine 
             
             
                 
                 
                 
                 
               Axle 
             
             
                 
                 
                 
                 
               Brake 
             
             
                 
                 
                 
                 
               Tires 
             
             
                 
               City 
               Name 
               String 2 
               LA 
             
             
                 
                 
                 
                 
               Boston 
             
             
                 
                 
                 
                 
               NY 
             
             
                 
               Organization 
               Name 
               String 3 
               Firestone 
             
             
                 
                 
                 
                 
               GM 
             
             
                 
                 
                 
                 
               National Ins. 
             
             
                 
               Person 
               Name 
               String 4 
               Jane 
             
             
                 
                 
                 
                 
               Sue 
             
             
                 
                 
                 
                 
               John Smith 
             
             
                 
               City 
               Name 
               String 5 
               San Jose, CA 
             
             
                 
                 
                 
                 
               New York 
             
             
                 
                 
                 
                 
               Boston, MA 
             
             
                 
               Engine Problem 
               Topic 
               String 6 
               fuel injection 
             
             
                 
                 
               words 
                 
               cylinder 
             
             
                 
               Brake Problem 
               Topic 
               String 7 
               brake shoes 
             
             
                 
                 
               words 
                 
               brake pads 
             
             
                 
                 
                 
                 
               master cylinder 
             
             
                 
                 
             
           
        
       
     
   
   The text analysis engines  50  generate string  1 ,  305 , string  2 ,  310 , string  3 ,  315 , string  4 ,  320 , string  5 ,  325 , string  6 ,  330 , and string  7 ,  335  (collectively referenced as strings  340 ). Modeler  220  analyzes the outputs of the text analysis engines  50  and generates the type system  300  shown in  FIG. 3 . Modeler  220  assigns attributes to each of the strings  340  as shown in Table 2. Each of the strings  340  is assigned to an entity concept or a relationship concept. A relationship concept relates strings or entity concepts. In  FIG. 3 , entity concepts comprise organization  345 , person  350 , city  355 , EngineProblem  360 , and BrakeProblem  365 . Relationship concepts comprise partner  370  and contacted  375 . Values of the strings are referenced as attribute values. 
   Indexer  210  builds an interpretation index  205  (I) over the exemplary set of all names of types (e.g., Person, Organization, EngineProblem, . . . ), attribute names (e.g., city, department, . . . ), and attribute values (e.g., GM, Jane, National Ins., . . . ), such that given a keyword w, I(w) returns one or more of the following:
         [type T]: w matches the name of a type T in the object model, and   [val T.y]: w matches the value of an attribute y of type T.       

   In the preceding definition, string matches can be fuzzy. In particular, interpreter  225  can employ standard information retrieval techniques for approximate matches such as, for example, stemming, stop-word elimination, relaxed capitalization, substring matches, synonym expansion, etc. 
   Depending on the precise implementation, interpreter  225  determines exemplary matches of keywords as shown in Table 3. 
   
     
       
             
           
             
             
             
           
         
             
               TABLE 3 
             
           
           
             
                 
             
             
               Exemplary matches shown are generated by interpreter 225 for 
             
             
               keywords in the exemplary type system of the exemplary 
             
             
               customer relationship management database. 
             
           
        
         
             
                 
               Keyword 
               Matches 
             
             
                 
                 
             
             
                 
               National 
               [val Organization.name] 
             
             
                 
               Jane 
               [val Person.name] 
             
             
                 
               engine 
               [type EngineProblem] 
             
             
                 
               engine 
               [val Partner.dept] 
             
             
                 
               LA 
               [val Partner.city] 
             
             
                 
               LA 
               [val City.name] 
             
             
                 
                 
             
           
        
       
     
   
   Interpreter  225  tokenizes a query provided by user  230  to generate one or more tokens and attempts to match those tokens with the interpretation index  205 . For instance, consider the keyword query “Jane GM”. Interpreter  225  generates two tokens “Jane” and “GM” and probes the interpretation index  205  for matches. Interpreter  225  identifies matches in [val Person.name] and [val Organization.name] respectively. Furthermore, each token can be treated as a keyword without imposing any additional semantics. The interpreter  225  denotes this default match using the notation [kwd Jane] and [kwd GM] respectively. 
   Interpreter  225  generates queries by taking one or more possible combinations of matches for each keyword. For this example, interpreter  225  generates the following queries shown below in SQL-like syntax: 
   Query (1,1): return documents that mention person Jane and organization GM. 
   
       
       
         
           select d 
           from Document d, Person p, Organization o 
           where d=p.doc=o.doc AND
           MATCH(p.name, Jane) AND MATCH(o.name, GM)
 
Query (1,2): return documents that mention person Jane and keyword “GM”.
   
         
           select d 
           from Document d, Person p 
           where d=p.doc AND MATCH(p.name, Jane) AND SEARCH(d, “GM”)
 
Query (2,1): return docs that match keyword Jane and organization GM.
 
           select d 
           from Document d, Organization o 
           where d=o.doc AND MATCH(o.name, GM) AND SEARCH(d, “Jane”)
 
Query (2,2): return docs that match keywords “Jane GM”.
 
           select d 
           from Document d, Person p 
           where SEARCH(d, “Jane GM”) 
         
       
     
  
   Query (2,2) is a conventional keyword search query. In general, since a [kwd] match exists for every query token, the query expansion technique of interpreter  225  generates the standard keyword search query as a special case for all queries. Thus, the interpretations produced by the query expansion technique of the present invention subsume a standard retrieval engine that is based purely on keyword matches. 
   Interpreter  225  makes additional use of the type system  300  represented in  FIG. 3  to derive more sophisticated interpretations of the keyword query. Query (1,1) was generated using the matches [val Person.name] and [val Organization.name]. The type system  300  comprises a type “Contacted”; “Contacted comprises attributes of type “Person” and “Organization”. Interpreter  225  generates a modified version of Query (1,1) using the type “Contacted” and replacing each “Person” with Contacted.initiator and “Organization” with Contacted.recipient. The generated query becomes: 
   Query (1,1)′: return docs that mention person Jane contacted organization GM. 
   
       
       
         
           select d 
           from Document d, Contacted c 
           where d=c.initiator.doc=c.recipient.doc AND 
           MATCH(c.initiator.name, Jane) AND MATCH(c.recipient.name, GM) 
         
       
     
  
   In the exemplary type system  300  shown in  FIG. 3 , there is only one type “Contacted” with the requisite characteristics to replace “Person” and “Organization”. In general, there can be more than one such type. Furthermore, interpreter  225  can extend this technique to look for higher-order relationships (as opposed to an immediate ancestor) in the type graph of a type system  215 . Interpreter  225  uses a “shared ancestor rule” to discover higher-order relationships. 
   The shared ancestor rule for query generation is as follows: Given a query “k 1  k 2 ” such that keyword k 1  has a match of the form [val T1.x], keyword k 2  has a match of the form [val T2.y], and there exists a type T with attributes “a” and “b” such that T.a is of type T1 and T.b is of type T2, interpreter  225  can generate the following query:
         select d   from Document d, T t   where t.a.doc=t.b.doc=d AND
           MATCH(t.a.x, k 1 ) AND MATCH(t.b.y, k 2 )   
               

   As another example, consider a keyword “Engine LA”. Interpreter  225  generates tokens for the keyword and identifies matches as follows:
         Engine: type EngineProblem
           val Partner.dept   kwd Engine   
           LA: val City.name
           val Partner.city   kwd LA   
               

   Interpreter  225  generates an exemplary query for keyword “Engine LA” as follows: 
   Query (2,2): return docs that mention an engine partner and a partner located in LA. 
   
       
       
         
           select d 
           from Document d, Partner p1, Partner p2 
           where d=p1.org.doc=p2.org.doc AND
           MATCH(p1.dept, Engine) AND MATCH(p2.city, LA)   
         
         
       
     
  
   Interpreter  225  employs a type-merging rule to merge the instances of “Partner” (p1 and p2) into a single “Partner” instance, generating the following query: 
   Query (2,2)′: return docs that mention an engine partner located in LA. 
   
       
       
         
           select d 
           from Document d, Partner p 
           where d=p.org AND MATCH(p.dept, Engine) AND MATCH(p.city, LA) 
         
       
     
  
   More generally, the type-merging rule can be stated as follows: Given a query “k 1  k 2 ” such that keyword k 1  has a match of the form [val T.x] and keyword k 2  has a match of the form [val T.y], interpreter  225  can generate a query:
         select d   from Document d, T t   where t.doc=d AND MATCH(t.x, k 1 ) AND MATCH (t.y, k 2 )       

     FIG. 4  illustrates a method  400  of system  10  in generating a type system for a collection of documents comprising annotations and executing a query against that collection of documents. Each of the text analysis engines  50  generates one or more character strings per document analyzed by the text analysis engine  50 . The modeler  220  models output of the text analysis engines  50  to generate the type system  215  (step  500 , further described in  FIG. 5 ). Indexer  210  builds an interpretation index  205  from the type system  215  (step  600 , further described in  FIG. 6 ). Interpreter  225  considers a keyword query provided by user  230  and generates a set of candidate interpretations for the keyword query (step  700 , further described in  FIG. 7 ). Interpreter  225  executes one or more candidate interpretations against the type system  215  represented in the interpretation index  205  (step  800 , further described in  FIG. 8 ). The interpreter  225  retrieves matching documents from the store/index  45  (step  405 ). Step  500  and step  600  comprise an “offline” document analysis process that is performed prior to query execution by system  10 . Step  700 , step  800 , and step  405  are performed “online” during query execution by system  10 . 
   The type system  215  is a structure representing annotations generated by the text analysis engines  50  from data in the store/index  45 . Annotations are, for example, strings representing data found in a document. The type system  215  comprises concepts. Concepts are entity concepts or relationship concepts. The relationship concept describes a relationship between selected entity concepts. For example, a “contact” relationship concept may describe a relationship between a person entity concept and a phone number entity concept. The type system  215  provides a structure for the annotations that enables complex queries against data in the store/index  45 . The type system  215  is a specific instance of a model for annotations of the data in store/index  45 . 
     FIG. 5  illustrates a method  500  by which modeler  220  generates the type system  215  from annotations provided by the text analysis engines  50 . Method  500  can be performed manually by a human, automatically by an application, or manually with assistance from an application. 
   Modeler  220  selects output of one of the text analysis engines  50  (step  505 ). The output of each of the text analysis engines  50  comprises one or more concepts. Modeler  220  selects an initial concept for the selected output (step  510 ). 
   Modeler  220  generates a type to represent the selected concept (step  515 ), with one type per concept. Generating a type comprises naming the type as an entity or a relationship and identifying one or more attributes for the type. The modeler  220  determines whether the selected concept is a relationship concept (decision step  520 ). For example, a selected concept may be an entity concept Person; another selected concept may be an entity concept Phone number”. Person and Phone number can be related by a relationship concept Contact. If the selected concept is a relationship (decision step  520 ), modeler  220  identifies the relationship concept and associated entity concepts (step  525 ). For example, modeler  220  may identify “contact” as a relationship concept, with associated entity concepts as person and phone number. 
   After identifying the relationship concept and associated entity concepts (step  525 ) or if the selected concept is not a relationship (decision step  520 ), modeler  220  determines whether additional concepts remain for processing from the selected output of the selected text analysis engine  50  (decision step  530 ). If additional concepts remain for processing, modeler  220  selects a next concept (step  535 ) and repeats step  515  through step  530  until no concepts from the selected output of the text analysis engine  50  remain for processing. 
   Modeler  220  determines whether additional outputs of the selected text analysis engines  50  remain for processing (decision step  540 ). If additional outputs remain, modeler  220  selects a next output for a text analysis engine  50  (step  545 ). The selected output can be from the same text analysis engine  50  as just processed or another text analysis engine  50 . Modeler  220  repeats step  510  through step  545  until no additional outputs remain for processing (decision step  540 ). Modeler  220  outputs the generated type system  215  (step  550 ). 
     FIG. 6  illustrates a method  600  by which indexer  210  generates an interpretation index  205 . Input to indexer  210  is the output of the text analysis engines  50  and the type system  215 . Indexer  210  selects an output of the text analysis engine  50  (step  605 ). Indexer  210  tokenizes strings in the selected output (step  610 ). Indexer  210  selects a token from the tokenized strings in the selected output (step  615 ). The indexer places an entry in the interpretation index that maps the selected token to the type in the type system  215  that represents the text analysis engine output selected in step  605 . 
   Indexer  210  determines whether additional tokens remain for analysis (decision step  625 ). If yes, indexer  210  selects a next token (step  630 ) and repeats step  620  through step  630  until no additional tokens remain for processing. 
   Indexer  210  determines whether additional outputs from text analysis engines  50  remain for processing (decision step  635 ). If yes, indexer  210  selects a next output of a text analysis engine  50  (step  640 ) and repeats step  610  through step  640  until no additional outputs remain for processing. Indexer  210  outputs the generated interpretation index  205  (step  645 ). 
     FIG. 7  illustrates a method  700  by which interpreter  225  generates one or more candidate query interpretations from a keyword query. Interpreter  225  tokenizes a keyword query (step  705 ). Interpreter  225  selects a keyword token (step  710 ) and consults the interpretation index  205  to obtain a match for the keyword token (step  715 ). Interpreter  225  determines whether additional tokens remain for processing (decision step  720 ). If yes, interpreter  225  selects a next keyword token and repeats step  715  through step  725  until no additional keyword tokens remain for processing. 
   Interpreter  225  calculates the Cartesian product (in other words, all possible combinations) of the generated matches (step  730 ). Interpreter  225  selects a combination from the Cartesian product (step  735 ). Interpreter  225  generates a specific candidate interpretation for the selected combination (step  740 ). Interpreter  225  determines whether additional combinations remain for processing (decision step  745 ). If yes, interpreter  225  selects a next combination (step  750 ) and repeats step  740  through step  750  until no additional combinations remain for processing. Interpreter  225  outputs candidate query interpretations (step  755 ). 
     FIG. 8  illustrates a method  800  by which interpreter  225  generates a specific candidate interpretation for a keyword query. In the embodiment described in this invention, interpretations are represented using a SQL-like syntax. Therefore, an interpretation comprises a “select” clause, a “from” clause, and a “where” clause. Interpreter  225  generates a candidate interpretation with a select clause and a from clause (step  805 ):
         select d   from Document d       
   For each type name occurring in any of the type or value matches for a selected keyword token, interpreter  225  adds an entry to the from clause (step  810 ). For example, referring to query (1,1) previously discussed, the candidate query becomes:
         select d   from Document D, Person p, Organization o       

   For each value match, interpreter  225  adds a match predicate in the where clause (step  815 ). The match predicate comprises one or more operands. One operand is a variable for a corresponding type, followed by a “.” and the name of the attribute in the value match. Another operand is a query token that corresponds to the value match. Referring again to exemplary query (1,1), the candidate query becomes:
         select d   from Document D, Person p, Organization o   where   MATCH(p.name, Jane) AND MATCH(o.name, GM)       

   For each keyword match, interpreter  225  adds a search predicate to the where clause (step  820 ). Referring to exemplary query (2,1), the candidate query becomes:
         select d   from Document d, Organization o   where   MATCH(o.name, GM) AND SEARCH(d, “Jane”)       

   For each type in the from clause, interpreter  225  equates doc attributes to d to ensure that all the concepts involved in the interpretation occur in the same document. Referring to exemplary query (1,1), the candidate query becomes:
         select d   from Document d, Person p, Organization o
           where d=p.doc=o.doc AND   MATCH(p.name, Jane) AND MATCH(o.name, GM)   
               

   Referring to exemplary query (2,1), the candidate query becomes:
         select d   from Document d, Organization o   where d=o.doc AND MATCH(o.name, GM) AND SEARCH(d, “Jane”)       

   An exemplary pseudocode for the query expansion process performed by the interpreter  225  is as follows: 
   
     
       
             
             
           
         
             
                 
                 
             
           
           
             
                 
               INPUT (keyword query q = { k 1 , ..., k n  }, interpretation index I) 
             
             
                 
               ForEach i = 1.. n 
             
             
                 
                compute m i  = I(k i ) U { [kwd k i ] } 
             
             
                 
               END ForEach 
             
             
                 
               generated = { } 
             
             
                 
               ForEach c = (c 1 , c 2 , ..., c n ) ∈ (m 1  × m 2  × .... × m n)   
             
             
                 
                fromEntries = {“Document d”} 
             
             
                 
                predicates = { } 
             
             
                 
                keywords = { } 
             
             
                 
                ForEach i = 1..n 
             
             
                 
                 c i  = [type T] =&gt; fromEntries = fromEntries U {T x i } AND 
             
             
                 
                 predicates = predicates U {x i .doc = d} 
             
             
                 
                 c i  = [val T.y] =&gt; fromEntries = fromEntries U {T x i } AND 
             
             
                 
                 predicates = predicates U {x i .doc = d, MATCH(x i .y, k i )} 
             
             
                 
                 c i  = [kwd k i ] =&gt; keywords = keywords U {k i } 
             
             
                 
                END ForEach 
             
             
                 
                selectClause = “select d” 
             
             
                 
                /* construct a valid from clause using the 
             
             
                 
                 * elements of the fromEntries set 
             
             
                 
                */ 
             
             
                 
                fromClause = generateFromClause(fromEntries) 
             
             
                 
                /* construct a valid where clause by AND&#39;ing 
             
             
                 
                 * together all the predicates 
             
             
                 
                */ 
             
             
                 
                whereClause = generateWhereClause(predicates, keywords) 
             
             
                 
                /* construct a valid query using the select, 
             
             
                 
                 * from, and where clauses 
             
             
                 
                */ 
             
             
                 
                currentQuery = generateQuery(selectClause, 
             
             
                 
                      fromClause, whereClause) 
             
             
                 
               generated = generated U {currentQuery} 
             
             
                 
               END ForEach 
             
             
                 
               OUTPUT generatedQueries 
             
             
                 
                 
             
           
        
       
     
   
   It is to be understood that the specific embodiments of the invention that have been described are merely illustrative of certain applications of the principle of the present invention. Numerous modifications may be made to the system and method for exploiting semantic annotations in executing keyword queries over a collection of text documents described herein without departing from the spirit and scope of the present invention. Moreover, while the present invention is described for illustration purpose only, in relation to documents or texts, it should be clear that the invention is similarly applicable to, for example, any type of objects. Moreover, while the present invention is described for illustration purpose only, in relation to a network such as, for example, the Internet, it should be clear that the invention is applicable as well to, for example, local access by users.