Patent Publication Number: US-2016224543-A1

Title: System and method for extracting ontological information from a body of text

Description:
BACKGROUND 
     The embodiments described herein relate generally to language processing systems and, more particularly, to techniques for extracting ontological information from a body of text. 
     Information Extraction (IE) is the science of automating he extraction of information from unstructured or semi-structured documents. Known information extraction systems rely on natural language processing (NLP), and are traditionally implemented as a pipeline of special-purpose processing modules targeting the extraction of a particular kind of information. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be reapplied from scratch to the entire body of text even though only a small part of the text might be affected. 
     Other known information extraction systems rely on keyword search, which involves a set of keywords and a search mechanism as a way of locating information in text documents. However, the search mechanism relies on identifying specific words that appear in the documents without taking into account the meaning of the words. Traditional word-based approaches ignore syntactic and grammatical information present in the sentence as a whole. 
     Information extraction systems also rely on named-entity recognition. In analyzing documents, information extraction systems need to recognize and classify individual elements. Some known approaches to named-entity recognition involve use of a dictionary, a list of known individual elements and their pseudonyms. However, dictionaries are not always available for specific subject matter domains, such as for specific engine components or engine failure symptoms. Creating dictionaries that encounter all possible syntactic variations of technical concepts in a given subject matter domain can be a labor-intensive task. Another known approach is to devise a supervised approach to generate models based on manually annotated data. However, this approach is also a labor-intensive task. 
     BRIEF DESCRIPTION 
     In one aspect, a system for extracting ontological information from a body of text is provided. The system includes an input module configured to receive a verb phrase. The system also includes a parsing module configured to parse one or more sentences from the body of text into parse tree format to generate a set of parsed sentences. The system further includes a named-entity-recognition module configured to identify a subset of parsed sentences from the set of parsed sentences based at least partially on the occurrence of the verb phrase within at least one parsed sentence of the set of parsed sentences. The named-entity-recognition module is also configured to identify a subset of noun phrases from the subset of parsed sentences based at least partially on grammatical relationship of each noun phrase of the subset of parsed sentences to the verb phrase. The named-entity-recognition module is further configured to classify a first noun phrase in the subset of noun phrases as an entity thereby defining a first entity. The named-entity-recognition module is also configured to classify a second noun phrase in the subset of noun phrases as a property thereby defining a first property. The system also includes a concept-extraction module configured to identify and output a conceptual relationship between the first entity and the first property based at least partially on grammatical relationship of the first entity and the first property within a first sentence. 
     In a further aspect, a method for extracting ontological information from a body of text is provided. The method is implemented by at least one computer device including at least one processor and at least one memory device coupled to the at least one processor. The method includes converting one or more sentences in the body of text into parse tree format to generate a set of parsed sentences. The method also includes identifying a verb phrase. The method further includes identifying a subset of parsed sentences from the set of parsed sentences based at least partially on the occurrence of the verb phrase within at least one parsed sentence of the set of parsed sentences. The method also includes identifying a subset of noun phrases from the subset of parsed sentences based at least partially on grammatical relationship of each noun phrase of the subset of parsed sentences to the verb phrase. The method further includes classifying a first noun phrase and a second noun phrase in the subset of noun phrases as one of an entity and a property, thereby defining one of a first entity and a first property. The method also includes identifying a conceptual relationship between the first entity and the first property based at least in part on grammatical relationship of the first entity and the first property within a first sentence. The method further includes outputting the conceptual relationship as an identified relation between the first entity and the first property. 
     In yet another aspect, one or more computer-readable storage media having computer-executable instructions embodied thereon are provided. When executed by at least one processor, the computer-executable instructions cause the at least one processor to convert one or more sentences of a body of text into parse tree format, thereby generating a set of parsed sentences. The computer-executable instructions also cause the at least one processor to identify a verb phrase. The computer-executable instructions further cause the at least one processor to identify subset of parsed sentences from the set of parsed sentences based at least partially on the occurrence of the verb phrase within at least one parsed sentence of the set of parsed sentences. The computer-executable instructions also cause the at least one processor to identify a subset of noun phrases from the subset of parsed sentences based at least partially on grammatical relationship of each noun phrase of the subset of parsed sentences to the verb phrase. The computer-executable instructions further cause the at least one processor to classify a. first noun phrase and a second noun phrase in the subset of noun phrases as one of an entity and a property, thereby defining one of a first entity and a first property. The computer-executable instructions also cause the at least one processor to identify a conceptual relationship between the first entity and the first property based at least in part on grammatical relationship of the first entity and the first property within a first sentence. The computer-executable instructions further cause the at least one processor to output the conceptual relationship as a relation between the first entity and the first property. 
    
    
     
       DRAWINGS 
       These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG. 1  is a block diagram of an exemplary computing system that may be used to extract ontological information from a body of text; 
         FIG. 2  is a block diagram of an exemplary text processor system for parsing and storing a body of text using the computing system shown in  FIG. 1 ; 
         FIG. 3  is a block diagram of an exemplary parse tree structure for an exemplary sentence as created by the text processor system shown in  FIG. 2 ; 
         FIG. 4  is a block diagram of an exemplary system for extracting ontological information from a body of text using the computing system shown in  FIG. 1  and the text processor system shown in  FIG. 2 ; and 
         FIG. 5  is a flow chart of a method for extracting ontological information from a body of text shown in  FIGS. 2 and 4 . 
     
    
    
     Unless otherwise indicated, the drawings provided herein are meant to illustrate key inventive features of the invention. These key inventive features are believed to be applicable in a wide variety of systems comprising one or more embodiments of the invention. As such, the drawings are not meant to include all conventional features known by those of ordinary skill in the art to be required for the practice of the invention. 
     DETAILED DESCRIPTION 
     In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. 
     The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. 
     “Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where the event occurs and instances where it does not. 
     Approximating language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that may permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term or terms, such as “about” and “substantially”, are not, to be limited to the precise value specified. In at least some instances, the approximating language may correspond to the precision of an instrument for measuring the value. Here and throughout the specification and claims, range limitations may be combined and/or interchanged, such ranges are identified and include all the sub-ranges contained therein unless context or language indicates otherwise. 
     As used herein, the term “non-transitory computer-readable media” is intended to be representative of any tangible computer-based device implemented in any method or technology for short-term and long-term storage of information, such as, computer-readable instructions, data structures, program modules and sub-modules, or other data in any device. Therefore, the methods described herein may be encoded as executable instructions embodied in a tangible, non-transitory, computer readable medium, including, without limitation, a storage device and/or a memory device. Such instructions, when executed by a processor, cause the processor to perform at least a portion of the methods described herein. Moreover, as used herein, the term “non-transitory computer-readable media” includes all tangible, computer-readable media, including, without limitation, non-transitory computer storage devices, including, without limitation, volatile and nonvolatile media, and removable and non-removable media such as a firmware, physical and virtual storage, CD-ROMs, DVDs, and any other digital source such as a network or the Internet, as well as yet to be developed digital means, with the sole exception being a transitory, propagating signal. 
     As used herein, the term “body of text” is intended to describe, without limitation, any set of text documents and text files, including the text contained within those text documents and text files. Alternatively, “text corpus” or “corpus of text” are terms used in the art as synonyms for “body of text,” as used herein. “Body of text” may refer to, without limitation, something as large as a collection of documents, and to something as small as a single sentence fragment. 
     As used herein, the terms “ontology” and “ontological” are intended to refer, generally, to things that exist and their relationships. More specifically, as used herein, an ontology is a formal representation of knowledge, concepts, and the relationships between things. Also, as used herein, ontologies are structural frameworks for organizing information about the world, or some part of it. 
     As used herein, the terms “syntax” and “syntactic” refer to the way in which linguistic elements, such as words, are put together to form constituents, i.e. phrases or clauses. These terms refer to the formal properties of a language, such as English, and how its elements are arranged. 
     As used herein, the terms “parse” and “parsing” are used in both the linguistic sense, i.e., when discussing how phrases are divided, as well as the syntactic analysis sense, to mean the processing, analysis, and syntactic breakdown of a body of text to determine its grammatical structure with respect to formal grammar systems, such as English. 
     As used herein, the term “entity” is intended to refer to, out limitation, an item and an object, either in an individual capacity, or as representing a class of similar items or objects. As used herein, the term “property” is intended to be descriptive of, without limitation, a state of being of an entity, a feature or function of an entity, and a quality or trait of an entity. For example, in the text “the sponge is wet,” the entity is the “sponge”, and the property of the sponge is “wet”. 
       FIG. 1  is a block diagram of an exemplary computing system  120  that may be used to extract ontological information from a body of text (not shown in  FIG. 1 ). Alternatively, any computer architecture that enables operation of the systems and methods as described herein may be used. 
     In the exemplary embodiment, computing system  120  includes a memory device  150  and a processor  152  operatively coupled to memory device  150  for executing instructions. In some embodiments, executable instructions are stored in memory device  150 . Computing system  120  is configurable to perform one or more operations described herein by programming processor  152 . For example, processor  152  may be programmed by encoding an operation as one or more executable instructions and providing the executable instructions in memory device  150 . Processor  152  may include one or more processing units, e.g., without limitation, in a multi-core configuration. 
     In the exemplary embodiment, memory device  150  is one or more devices that enable storage and retrieval of information such as executable instructions and/or other data. Memory device  150  may include one or more tangible, non-transitory computer-readable media, such as, without limitation, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), a solid state disk, a hard disk, read-only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), and/or non-volatile RAM (NVRAM) memory. The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program. 
     Also, in the exemplary embodiment, memory device  150  may be configured to store information associated with the extraction of ontological information from the body of text including, without limitation, content from the body of text, parsed sentence information, a parse tree database (not shown in  FIG. 1 ), one or more verb phrases, and ontological information such as a conceptual relationship between entities and properties. 
     In some embodiments, computing system  120  includes a presentation interface  154  coupled to processor  152 . Presentation interface  154  presents information, such as a user interface and/or an alarm, to a user  156 . For example, presentation interface  154  may include a display adapter (not shown) that may be coupled to a display device (not shown), such as a cathode ray tube (CRT), a liquid crystal display (LCD), an organic LED (OLED) display, and/or a hand-held device with a display. In some embodiments, presentation interface  154  includes one or more display devices. In addition, or alternatively, presentation interface  154  may include an audio output device (not shown) (e.g., an audio adapter and/or a speaker). 
     In some embodiments, computing system  120  includes a user input interface  158 . In the exemplary embodiment, user input interface  158  is coupled to processor  152  and receives input from user  156 . User input interface  158  l may include, for example, a keyboard, a pointing device, a mouse, a stylus, and/or a touch sensitive panel (e.g., a touch pad or a touch screen). A single component, such as a touch screen, may function as both a display device of presentation interface  154  and user input interface  158 . 
     Further, a communication interface  160  is coupled to processor  152  and is configured to be coupled in communication with one or more other devices, such as, without limitation, another computing system  120 , and any device capable of accessing computing system  120  including, without limitation, a portable laptop computer, a personal digital assistant (PDA), and a smart phone. Communication interface  160  may include, without limitation, a wired network adapter, a wireless network adapter, a mobile telecommunications adapter, a serial communication adapter, and/or a parallel communication adapter. Communication interface  160  may receive data from and/or transmit data to one or more remote devices. For example, communication interface  160  of one computing system  120  may transmit transaction information to communication interface  160  of another computing system  120 . Computing system  120  may be web-enabled for remote communications, for example, with a remote desktop computer (not shown). 
     Also, presentation interface  154  and/or communication interface  160  are both capable of providing information suitable for use with the methods described herein (e.g., to user  156  or another device). Accordingly, presentation interface  154  and communication interface  160  may be referred to as output devices. Similarly, user input interface  158  and communication interface  160  are capable of receiving information suitable for use with the methods described herein and may be referred to as input devices. 
     Further, processor  152  and/or memory device  150  may also be operatively coupled to a storage device  162 . Storage device  162  is any computer-operated hardware suitable for storing and/or retrieving data, such as, but not limited to, data associated with a database  164 . In the exemplary embodiment, storage device  162  is integrated in computing system  120 . For example, computing system  120  may include one or more hard disk drives as storage device  162 . Moreover, for example, storage device  162  may include multiple storage units such as hard disks and/or solid state disks in a redundant array of inexpensive disks (RAID) configuration. Storage device  162  may include a storage area network (SAN), a network attached storage (NAS) system, and/or cloud-based storage. Alternatively, storage device  162  is external to computing system  120  and may be accessed by a storage interface (not shown). 
     Moreover, in the exemplary embodiment, database  164  contains a variety of static and dynamic data associated with, without limitation, a parse tree database (not shown in  FIG. 1 ), the body of text, a table of entities and properties (not shown in  FIG. 1 ), and an ontological database (not shown in  FIG. 1 ). 
     The embodiments illustrated and described herein as well as embodiments not specifically described herein but within the scope of aspects of the disclosure, constitute exemplary means for extracting ontological information from a body of text. For example, computing system  120 , and any other similar computer device added thereto or included within, when integrated together, include sufficient computer-readable storage media that is/are programmed with sufficient computer-executable instructions to execute processes and techniques with a processor as described herein. Specifically, computing system  120  and any other similar computer device added thereto or included within, when integrated together, constitute an exemplary means for recording, storing, retrieving, and displaying operational data associated with a concept extraction system (not shown in  FIG. 1 ) for extracting ontological information from the body of text. 
       FIG. 2  is a block diagram of an exemplary text processor system  200  for extracting ontological information from a body of text  202  using computing system  120  (shown in  FIG. 1 ). In the exemplary embodiment, body of text  202  includes written text. Body of text  202  may include text in any language, natural or artificial. In the exemplary embodiment, body of text  202  includes a set of documents related to a specific subject matter, e.g., a set of documents related to engine parts and equipment. Document sets with specific subject matter are more likely to generate meaningful conceptual relationships between entities and properties. Alternatively, body of text  202  may include any set of documents related to any subject matter. 
     Also, in the exemplary embodiment, text processor system  200  includes a parsing module  210  and a parse tree database  212 . In the exemplary embodiment, parsing module  210  converts body of text  202  into parse tree format. Each sentence contained in body of text  202  is parsed by parsing module  210  into its component pieces, as described below. Parsing module  210  is a syntactic parser based on parse trees, also known as constituent trees. In the exemplary embodiment, Apache OpenNLP parser is used. Alternatively, any other parser that enables operation of text processor system  200  as described herein may be used. 
     Further, in the exemplary embodiment, each document in body of text  202  is represented as a hierarchical representation called the parse tree of the document (not shown in  FIG. 2 ). Once parsed by parsing module  210 , all of the parse trees of all documents in body of text  202  are stored in parse tree database  212 . Parse tree database  212  includes storage for database  164  (shown in  FIG. 1 ), as well as MySQL™ relational database management system (RDBMS) software. Alternatively, any other RDBMS such as, without limitation, Oracle™ or DB2™ that enables operation of text processor system  200  as described herein may be used. As used herein, Parse tree database  212 , and the term “database”, refers to either the storage for database  164 , or the RDBMS (not separately shown), or both. 
       FIG. 3  is a block diagram of an exemplary parse tree  300  for an exemplary original sentence fragment  302  that may be parsed by parsing module  210  (shown in  FIG. 2 ). Parse tree  300  is a syntactic tree of original sentence fragment  302 . Parse tree  300  generally includes two types of elements. A first of the two types of elements are constituents, i.e., the “nodes” of the tree. A second of the two types of elements are linkages  304 , i.e., the lines connecting the nodes together, and represent the syntactic dependencies between pairs of words in sentence root  310 . In  FIG. 3 , constituents are represented with circles and have a letter code corresponding to what type of sentence fragment it represents. The letter code is referred to hereinafter as a “part-of-speech tag.” Original sentence fragment  302  is a sentence fragment in English, “Water found in inlet plenum.” Original sentence fragment  302  is represented in parse tree  300  as sentence root  310 , represented in  FIG. 3  with part-of-speech tag “S”, and logically represents a sentence or sentence fragment. 
     Also, in the exemplary embodiment, parse tree  300  further includes a first noun phrase  312  (NP), a first verb phrase  314  (VP), a proper noun  316  (NNP), a past tense verb  318  (VBD), a prepositional phrase  320  (PP), a preposition  322  (IN), a second noun phrase  324  (NP), a first noun  326  (NN), and a second noun  328  (NN). Each of these nodes is represented by a corresponding part-of-speech tag, as listed above. A “leaf node” is a node that has no children, i.e. no nodes below it to which it is linked. Each leaf node represents a single word in original sentence fragment  302 . 
     Also, in the exemplary embodiment, parse tree  300  includes linkages  304 , the inter-connecting lines between the nodes. In link grammar theory, linkages  304  represent the syntactic dependencies between pairs of words in sentence root  310 . 
     Further, in the exemplary embodiment, original sentence fragment  302 , “Water found in inlet plenum,” is parsed by parsing module  210  (shown in  FIG. 2 ). Original sentence fragment  302  is represented in parse tree  300  as the root node of the tree, sentence root  310 . Upon initial evaluation by parsing module  210 , sentence root  310  is determined to include first noun phrase  312 , representing “Water”, and first verb phrase  314 , representing “found in inlet plenum.” Upon further evaluation, first noun phrase  312  is determined to be proper noun  316 , “Water”. Upon further evaluation, first verb phrase  314  is found to include past tense verb  318 , “found”, as well as prepositional phrase  320 , “in inlet plenum”. Prepositional phrase  320  is further broken down into preposition  322 , “in”, along with second noun phrase  324 , “inlet plenum”. Second noun phrase  324  is further broken down into first noun  326 , “inlet”, and second noun  328 , “plenum”. The full parse tree  300  is the output of parsing module  210  (shown in  FIG. 2 ) after parsing original sentence fragment  302 , and represents the dependencies between pairs of words in original sentence fragment  302 . This information is stored in parse tree database  212  (shown in  FIG. 2 ). Alternatively, parse tree  300  may be stored in memory device  150  (shown in  FIG. 1 ). 
     In the exemplary embodiment, parse tree  300  shows only the parse tree structure of original sentence fragment  302 , and not any higher-order parse tree information above sentence root  310 . In other embodiments, all of the text in entire collections of documents will be parsed into parse tree format similar to parse tree  300 . Alternatively, there may be parse tree information for structures above the sentence level, such as, without limitation, paragraph-level information and document-level information. 
     Referring to  FIG. 2 , in the exemplary embodiment, text processor system  200  also includes a query subsystem  222  and a PTQL query  220 . Parse Tree Query Language (PTQL) is a query language for use with text processor system  200  to submit database queries to parse tree database  212 . PTQL is an extension of the linguistic query language “LPath” which facilitates queries to be performed not only on the constituent trees but also the syntactic links between words on linkages. PTQL, without limitation, facilitates expressing linguistic patterns based on parse trees, such as parse tree  300 , facilitates querying the parse trees, facilitates creating expressions using immediate-following siblings and immediate-preceding siblings within parse trees, and facilitates expressing linguistic patterns based on dependency grammar, PTQL query  220  is made up of four components, including tree patterns, link conditions, proximity conditions, and a return expression. A tree pattern describes the hierarchical structure and the horizontal order between the nodes of the parse tree. A link condition describes the linking requirements between nodes. A proximity condition is to find words that are within a specified number of words. A return expression defines what to return. 
     In operation, a user  156  (shown in  FIG. 1 ) inputs PTQL query  220  using computing system  120  (shown in  FIG. 1 ). The PTQL query  220  is submitted to query subsystem  222 . PTQL query  220  goes through a query translator  224 , which facilitates translating PTQL query  220  from PTQL to the query language understood by parse tree database  212 . Query translator  224  translates PTQL query  220  into SQL, the query language understood by MySQL™ RDBMS and database  164  (shown in  FIG. 1 ). Alternatively, other query languages that support the ability to query based on constituent trees may be used in place of PTQL. Also, alternatively, query translator  224  may translate PTQL query into any other query language, as required by the underlying RDBMS used with database  164 . 
     Moreover, in the exemplary embodiment, text processor system  200  includes an indexing subsystem  213  and a filter  226 , part of query subsystem  222 . Indexing subsystem includes an index builder  214 , an inverted index  216 , and an IR engine  218 . Index builder  214  facilitates building and maintaining inverted index  216  for the indexing of sentences according to words and the corresponding entity types. Inverted index  216  is a database index related to parse tree database  212  facilitating faster response times in the processing of database queries such as PTQL query  220 . For example, and without limitation, as text processor system  200  identifies entity names within sentences in parse tree database  212 , index builder  214  updates inverted index  216  to include new entities. When PTQL query  220  is submitted, query subsystem  222  sends PTQL query  220  through filter  226 . If PTQL query  220  involves an entity that is in the inverted index  216 , filter  226  utilizes IR engine  218  and inverted index  216  to more quickly process PTQL query  220  using inverted index  216 . IR engine  218  enables efficient processing of PTQL queries  220  by selecting sentences based on the lexical features defined in PTQL queries  220 , and only the subset of sentences retrieved by the IR engine are considered for the evaluation of the conditions specified in PTQL queries  220 . 
       FIG. 4  is a block diagram of an exemplary concept extraction system  400  for extracting ontological information from body of text  202  (shown in  FIG. 2 ). Concept extraction system  400  includes a named-entity-recognition module  402  and an input module  404 . Input module  404  provides a verb phrase  406 , such as past tense verb  316 , “found”, to named-entity-recognition module  402 . In some embodiments, input module  404  may receive verb phrase  406  from user  156  (shown in  FIG. 1 ) using computing system  120  (shown in  FIG. 1 ). Alternatively, without limitation, input module  404  may identify verb phrase  406  using any process for identifying verb phrases relevant to body of text  202 , In another embodiment, verb phrase  406  is identified through “bootstrapping” verb phrases, a method of identifying verb phrase  406  from body of text  202  based on a grammatical relationship between verb phrase  406  and another already-known entity or property. 
     Moreover, concept extraction system  400  queries parse tree database  212  using verb phrase  406  to identify a subset of sentences  410  that include verb phrase  406 . Concept extraction system  400  then identifies a subset of noun phrases  412  related to verb phrase  406  within subset of sentences  410 . Concept extraction system  400  identifies subset of noun phrases  412 , without limitation, based at least partially on grammatical relationship of a noun phrase (not shown in  FIG. 4 ) to verb phrase  406  within a sentence (not shown in  FIG. 4 ). For example, and without limitation, the grammatical relationship between proper noun  316  “water” and past tense verb  318  “found” in original sentence fragment  302  (shown in  FIG. 3 ). More specifically, for example, and without limitation, consider the following PTQL query: 
     //S{/NP(kw2)-&gt;/VP{/?[Value=‘found’]-&gt;/PP{/?[Value=‘in’}]&gt;//NP(kw1)}}}::: distinct kw1.value, kw2.value 
     The above PTQL query defines the pattern in finding a sentence that contains noun phrases  412  kw1 and kw2 related to verb phrase  406  “found in”. The return expression, which is stated in the expression to the right of the “:::” symbols, defines the output values of kw1 and kw2. When the above PTQL query is applied to sentence  302  (shown in  FIG. 3 ), “water found in inlet plenum”, the phrases “inlet plenum” and “water” are returned as the values of kw1 and kw2. 
     Further, from subset of noun phrases  412 , concept extraction system  400  classifies noun phrases as either an entity  430 , such as “inlet plenum” (shown in  FIG. 3  as first noun  326  and second noun  328 ), or a property  432 , such as proper noun  316  “water”. Alternatively, concept extraction system  400  may classify a noun phrase as an entity based on grammatical relationship to a preposition and an already-classified property. For example, without limitation, if “water” has previously been classified as a “property”, then “water found in &lt;X&gt;” grammatically suggests that &lt;X&gt; is likely an “entity”. Alternatively, concept extraction system  400  may classify a noun phrase as a “property” based on grammatical relationship to a preposition and an already-classified “entity”. For example, without limitation, if “inlet plenum” has previously been classified as an “entity”, then “&lt;Y&gt; found in inlet plenum” grammatically suggests that &lt;Y&gt; is a “property”. Alternatively, concept extraction system  400  may classify a noun phrase as a “property” based on grammatical relationship to a preposition. For example, without limitation, “&lt;Y&gt; in &lt;X&gt;” may grammatically suggest that &lt;Y&gt; is a “property” given that &lt;X&gt; is recognized as an “entity”, because the English language normally uses the preposition “in” with the property preceding. All noun phrases in subset of noun phrases  412  may be classified, but minimally at least one entity  430  and one property  432  may be classified. Named-entity-recognition module  402  stores classification information for entity  430  and property  432  in parse tree database  212 . Alternatively, classification information may be stored in any way, such as, without limitation, memory device  150  (shown in  FIG. 1 ), that enables operation of system  200  as described herein. 
     Moreover, in the exemplary embodiment, concept extraction system  400  includes a normalizing module  450 . Normalizing module  450  normalizes like noun phrases by the methods of, without limitation, stemming, dropping extraneous articles, and dropping adjectives. “Normalization,” in the linguistic context here, refers to renaming like words or phrases to a single, standard form. Stemming is a process for reducing inflected words to their stem, or root word, for example, and without limitation, identifying “fishing”, “fished”, “fish”, and “fisher” to the root word “fish”. Dropping extraneous articles refers to a process for dropping “a” from “a fisher” to identify just “fisher”. Dropping adjectives is a process for eliminating a descriptive adjective from a noun phrase, such as removing “smelly” from “smelly fisher” to identify just “fisher”. Alternatively, normalizing module  450  may use any methods of normalizing like nouns and noun phrases. Methods of normalizing help to reduce what may be duplicate entities. 
     Further, in the exemplary embodiment, concept extraction system  400  includes an aliasing module  460 . Aliasing module  460  helps standardize names of entities and properties by replacing related entities or properties with a single alias, a standard name by which all of the related entities or properties are going to be called. For example, and without limitation, aliasing module  460  may create an alias “Part_compressor_blade” for any entities called either “compressor blades” or “compressor blade.” Aliasing module  460  replaces all occurrences of “compressor blades” and “compressor blade” in parse tree database  212  with the alias “Part_compressor_blade”. Alternatively, aliasing module  460  may use any method of aliasing. 
     Moreover, concept extraction system  400  includes a concept-extraction module  420 . Concept-extraction module  420  identifies and outputs a conceptual relationship  434  between entity  430  and property  432  based at least partially on grammatical relationship between entity  430  and property  432  within sentence root  310  (shown in  FIG. 3 ) as stored in parse tree database  212 . Alternatively, concept-extraction module  420  can identify conceptual relationship  434  between entity  430  and property  432  in any parsed sentence not necessarily stored in parse tree database  212 . For example, and without limitation, consider the following PTQL query: 
                                        //NP{/?[Tag= ‘Property’](kw2)−&gt;           /PP{/?[Value IN {‘in’,‘on’,‘from’}]−&gt;/?[Tag=‘Entity’](kw1)}}           ::: distinct kw1.value, kw2.value                    
The above PTQL query defines the pattern for entity-property relation extraction. The constructs “Tag=‘Entity’” and “Tag=‘Property’” correspond to noun phrases  412  that have been identified as entities  430  and properties  432 . This PTQL query defines a syntactic constraint that, within a noun phrase  412 , an identified property is followed by a prepositional phrase that includes a preposition and an identified entity. The returning entities and properties, i.e., the values of kw1 and kw2, are deemed to have entity-property relations, denoted as &lt;entity, property&gt;.
 
     In the exemplary embodiment, named-entity-recognition module  402  and concept-extraction module  420  submit PTQL queries  220  (shown in  FIG. 2 ) to parse tree database  212  when performing execution tasks such as, without limitation, identifying subset of sentences  410  with verb phrase  406 , identifying subset of noun phrases  412  related to verb phrase  406  within the subset of sentences  410 , classifying noun phrases as either an entity or a property, and identifying conceptual relationship  434  between entity  430  and property  432 . 
       FIG. 5  is a flow chart of an exemplary method  500  for extracting ontological information from body of text  202  (shown in  FIGS. 2 and 4 ). More specifically, method  500  extracts conceptual relationship  434  (shown in  FIG. 4 ) from body of text  202  (shown in  FIG. 2 ). Body of text  202  is converted  502  into parse tree format. Verb phrase  406  (shown in  FIG. 4 ) is identified  504 . Subset of sentences  410  (shown in  FIG. 4 ) is identified  506  from body of text  202  using verb phrase  406 . From subset of sentences  410 , subset of noun phrases  412  (shown in  FIG. 4 ) is identified  508 . From subset of noun phrases  412 , entity  430  (shown in  FIG. 4 ) and property  432  (shown in  FIG. 4 ) are classified  510 . Conceptual relationship  434  (shown in  FIG. 4 ) between entity  430  and property  432  is identified  512 , and is then output  514 . In some embodiments, conceptual relationship  434  may be output  514  to user  156  (shown in  FIG. 1 ) using computing system  120  (shown in  FIG. 1 ). In other embodiments, conceptual relationship  434  may be stored in an ontological database (not separately shown) in database  164  (shown in  FIG. 1 ). 
     The above-described systems and methods provide a way to extract conceptual relationships from a web of unstructured text documents. Information Extraction (IE) systems are traditionally implemented in file-based ways that require repeated processing of text documents as new kinds of information are desired. The embodiments described herein allow for sentence parsing and database storage, encapsulating not only the word content but also the grammatical relationships between words within a sentence. The use of a database, along with supporting query language and infrastructure, minimizes reprocessing requirements, enables faster information retrieval as new extraction goals are identified, and provides a process for generic extraction. Moreover, the above-described systems and methods provide a way to extract conceptual relationships from a web of unstructured text documents in a way that requires no use of dictionaries of technical concepts or the manual annotation of training data, and limited human involvement in the process. The systems and methods described above do not rely on labor-intensive efforts, and can be applied to various business domains. Storing unstructured text documents in a structured format allows for unsupervised data mining, and the discovery of conceptual relationships based on grammatical relationships within the text. 
     An exemplary technical effect of the methods, systems, and apparatus described herein includes at least one of: (a) reducing reprocessing requirements by retaining both content and grammatical relationship information for the body of text in a parse tree database, with both current queries, as well as when new extraction goals are identified; (b) facilitating a process for generic extraction, as the systems and methods are not subject-specific, and can be applied across business and technical domains; and (c) facilitating unsupervised information extraction, as the systems and methods require less human involvement for training. 
     Exemplary embodiments of systems and methods for extracting ontological information from a body of text are described above in detail. The systems and methods described herein are not limited to the specific embodiments described herein, but rather, components of systems and/or steps of the methods may be utilized independently and separately from other components and/or steps described herein. For example, the methods may also be used in combination with other systems requiring concept extraction systems and methods, and are not limited to practice with only the text processing system and concept extraction system and methods as described herein. Rather, the exemplary embodiments can be implemented and utilized in connection with many other concept extraction applications. 
     Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the systems and methods described herein, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing. 
     This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.