Abstract:
A method, system, and computer program product for identifying related information in dissimilar data are provided in the illustrative embodiments. Using a first part of a first entry in a dictionary, a first portion is identified in a first data, the first part matching the first portion within a tolerance. A second part of the first entry referencing a section of a second data is determined, the second data being organized in a repository according to a schema. A third part of the first entry sufficient to locate a record in the section of the second data is determined. A query is constructed using the second part and the third part, and performed on the second data. A result set is obtained, wherein a record in the result set is related to the first portion in the first data and the record does not include the first portion.

Description:
TECHNICAL FIELD 
       [0001]    The present invention relates generally to a method, system, and computer program product for correlating and finding information. More particularly, the present invention relates to a method, system, and computer program product for identifying related information in dissimilar data. 
       BACKGROUND 
       [0002]    Enormous amount of data is created everyday and the total volume of data that is available at any given time is ever increasing. For example, presently, approximately 2.5 Quintillion bytes of data is created every day. 
         [0003]    A person or entity (collectively hereinafter, “user”) may be interested in a fraction of this volume of data according to the user&#39;s interest, field of study, importance to a business, and other reasons. Even this fraction of the total volume is unmanageable. The data is this fraction is still too voluminous to process or consume adequately, relate it with other data available to the user, and use it beneficially. 
       SUMMARY 
       [0004]    The illustrative embodiments provide a method, system, and computer program product for identifying related information in dissimilar data. An embodiment includes a method for identifying related information in dissimilar data. The embodiment identifies, using a first part of a first entry in a dictionary, a first portion in a first data, the first part matching the first portion within a tolerance. The embodiment determines a second part of the first entry in the dictionary, the second part referencing a section of a second data, the second data being organized in a repository according to a schema. The embodiment determines a third part of the first entry in the dictionary, the third part being sufficient to locate a record in the section of the second data. The embodiment constructs a query using the second part and the third part. The embodiment obtains, responsive to performing the query on the second data, a result set, wherein a record in the result set is related to the first portion in the first data and the record does not include the first portion. 
         [0005]    Another embodiment includes a computer program product for identifying related information in dissimilar data. The embodiment further includes one or more computer-readable tangible storage devices. The embodiment further includes program instructions, stored on at least one of the one or more storage devices, to identify, using a first part of a first entry in a dictionary, a first portion in a first data, the first part matching the first portion within a tolerance. The embodiment further includes program instructions, stored on at least one of the one or more storage devices, to determine a second part of the first entry in the dictionary, the second part referencing a section of a second data, the second data being organized in a repository according to a schema. The embodiment further includes program instructions, stored on at least one of the one or more storage devices, to determine a third part of the first entry in the dictionary, the third part being sufficient to locate a record in the section of the second data. The embodiment further includes program instructions, stored on at least one of the one or more storage devices, to construct a query using the second part and the third part. The embodiment further includes program instructions, stored on at least one of the one or more storage devices, to obtain, responsive to performing the query on the second data, a result set, wherein a record in the result set is related to the first portion in the first data and the record does not include the first portion. 
         [0006]    Another embodiment includes a computer system for identifying related information in dissimilar data. The embodiment further includes one or more processors, one or more computer-readable memories and one or more computer-readable storage devices. The embodiment further includes program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to identify, using a first part of a first entry in a dictionary, a first portion in a first data, the first part matching the first portion within a tolerance. The embodiment further includes program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to determine a second part of the first entry in the dictionary, the second part referencing a section of a second data, the second data being organized in a repository according to a schema. The embodiment further includes program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to determine a third part of the first entry in the dictionary, the third part being sufficient to locate a record in the section of the second data. The embodiment further includes program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to construct a query using the second part and the third part. The embodiment further includes program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to obtain, responsive to performing the query on the second data, a result set, wherein a record in the result set is related to the first portion in the first data and the record does not include the first portion. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0007]    The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as a preferred mode of use, further objectives and advantages thereof, will best be understood by reference to the following detailed description of the illustrative embodiments when read in conjunction with the accompanying drawings, wherein: 
           [0008]      FIG. 1  depicts a block diagram of a network of data processing systems in which illustrative embodiments may be implemented; 
           [0009]      FIG. 2  depicts a block diagram of a data processing system in which illustrative embodiments may be implemented; 
           [0010]      FIG. 3  depicts a block diagram of an example configuration for identifying related information in dissimilar data in accordance with an illustrative embodiment; 
           [0011]      FIG. 4  depicts an example of a volume of data and annotation therein in accordance with an illustrative embodiment; 
           [0012]      FIG. 5  depicts example keys, query, and result set for identifying related information in dissimilar data in accordance with an illustrative embodiment; and 
           [0013]      FIG. 6  depicts a flowchart of an example process for identifying related information in dissimilar data in accordance with an illustrative embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0014]    Users maintain information about their business or interest in databases. For example, an entity engaged in the business of oil and gas exploration maintains the information about their projects in a database, organized by project numbers and other such proprietary or business-specific information or concepts. 
         [0015]    Besides being too voluminous to adequately process and user, the data available to the user often presented in significantly different form. For example, a news article in a publication or a research paper often makes references to similar information about an oil reservoir in vastly different ways. As a simple example, an article may reference a reservoir in the Arabian basin as a reservoir in the Middle East, a research paper may refer to the same reservoir as a reservoir in the North African region, and a database of an oil and gas exploration company may reference the reservoir in another different manner. 
         [0016]    The illustrative embodiments recognize that presently, the task of consuming the available data, relating the data to other information, and finding a usefulness of the data is largely performed by humans in a manual manner. The illustrative embodiments recognize that presently, a technological solution is not available to automatically consume available data, relate the data to other information, and extract useful or meaningful relationships between the two to enable a decision making process. 
         [0017]    The illustrative embodiments used to describe the invention generally address and solve the above-described problems and other problems related to consuming diversely presented volumes of data. The illustrative embodiments provide a method, system, and computer program product for identifying related information in dissimilar data. 
         [0018]    In the description of the illustrative embodiments, a database is used for storing a user&#39;s information and a query of a certain type is used to extract information from that database only as an example. Other forms of data storage and their corresponding manners of querying are contemplated within the scope of the illustrative embodiments. 
         [0019]    Furthermore, the illustrative embodiments are described using an oil and gas exploration entity as an example user only for the clarity of the embodiments and not as a limitation on the illustrative embodiments. Accordingly, the example data used by the example user from the generally available volume of data, the example information in the example user&#39;s database are also non-limiting examples. From this disclosure, those of ordinary skill in the art will be able to adapt an embodiment for use with other data, other information, other organization of the information, other manners of querying or extracting the information from that organization, and the same are contemplated within the scope of the illustrative embodiments. 
         [0020]    An embodiment maintains or manages a repository of keywords and their corresponding related concepts in the user&#39;s information. The keywords are hereinafter also referred to interchangeably as keys. The repository of the key-concept entries is called a dictionary. The dictionary can be a general dictionary or a subject-matter-specific dictionary. For example, the example oil and gas exploration entity may maintain a domain-specific dictionary that includes words expected to be found in oil and gas industry related volume of data. In such a dictionary, and embodiment relates the words expected to be found in oil and gas industry related volume of data with how similar concepts are represented in the entity&#39;s database schema. The representation of a concept in a database schema is called a normalized form. 
         [0021]    For example, assume that the data is an article describing some information about oil and gas reservoirs in the Arabian basin. The entity may want to use the research paper to make a decision with respect to their operations in that region. 
         [0022]    The word “Arabian” can be a key in the dictionary that is related to an entry in a row of a table in a database schema. For example, the key “Arabian” may be related to a row entry “North Africa” or a row entry containing a project code for a project that the entity has in that region. The row entry is an example of a normalized form related to the key. Any number of row entries from the entity database can similarly be associated with the key “Arabian”, such as when the entity calls that region by different names, or has several different projects in the region. 
         [0023]    Furthermore, the word “Arabian” may be a key in the dictionary that is related to a column name of a column in a database schema. For example, the “Arabian” key may be related to column name “Region” or another column name describing the manner in which the entity classifies projects according to geography, such as “zones” or “country”. The column name is an example of a type of information related to the key. The column name can be another example of a normalized form related to the key. Any number of column names from the entity database can similarly be associated with the key “Arabian”. 
         [0024]    An embodiment enables the management of such a dictionary. For example, the dictionary may initially have been created manually with a few entries, and the embodiment adds, removes, augments, enhances, or otherwise modifies the entries therein over time according to the volume of data used and consumed by one or more illustrative embodiments. 
         [0025]    When certain data is available from a volume of data, such as a news article, an embodiment uses such a dictionary to locate the keys in the data. Using a combination of one or more keys located in the data, the embodiment determines their normalized forms. 
         [0026]    An embodiment further constructs a query for the entity&#39;s database using the normalized forms. For example, if the database is a relational database, the embodiment constructs a Structured Query Language (SQL) query that uses one or more normalized forms in one or more clauses of the query. 
         [0027]    An embodiment executes the query, or causes the query to be executed on the entity&#39;s database, whose schema is used for managing the dictionary. The database returns a result set, which includes one or more database records. 
         [0028]    A database record is selected in the result set because the query used a normalized form that matched the record regardless of how the published article. Thus, an embodiment is able to relate the data of the published article to the information in the user&#39;s database, even though the data of the article contains descriptions and items not found in the user&#39;s database of information. 
         [0029]    A method of an embodiment described herein, when implemented to execute on a device or data processing system, comprises substantial advancement of the functionality of that device or data processing system in relating diverse volumes of data with information in a database. For example, where prior-art fails to recognize the relationship between descriptions and items in one volume of data with descriptions and items in another repository of information, an embodiment uses a dictionary to determine how certain descriptions in the volume of data are related to certain contents of the repository. Furthermore, an embodiment also produces results from the repository, which appear to be related to the descriptions in the volume. Operating in a manner described herein, an embodiment allows a user to make decisions relative to the information in the repository based on the data available in the volume. Such manner of automatic analysis and correlation of diverse and dissimilar data is unavailable in presently available devices or data processing systems. Thus, a substantial advancement of such devices or data processing systems by executing a method of an embodiment allows a user or a decision making process to act upon the related records in the user&#39;s repository based on the descriptions in the volume of data, which a user would otherwise have to relate manually, thereby limiting the amount of data that the user can meaningfully consume or use from the volume. 
         [0030]    The illustrative embodiments are described with respect to certain data or information, sources and repositories, databases and query types, keys and normalized forms, result sets, dictionaries, devices, data processing systems, environments, components, and applications only as examples. Any specific manifestations of these and other similar artifacts are not intended to be limiting to the invention. Any suitable manifestation of these and other similar artifacts can be selected within the scope of the illustrative embodiments. 
         [0031]    Furthermore, the illustrative embodiments may be implemented with respect to any type of data, data source, or access to a data source over a data network. Any type of data storage device may provide the data to an embodiment of the invention, either locally at a data processing system or over a data network, within the scope of the invention. Where an embodiment is described using a mobile device, any type of data storage device suitable for use with the mobile device may provide the data to such embodiment, either locally at the mobile device or over a data network, within the scope of the illustrative embodiments. 
         [0032]    The illustrative embodiments are described using specific code, designs, architectures, protocols, layouts, schematics, and tools only as examples and are not limiting to the illustrative embodiments. Furthermore, the illustrative embodiments are described in some instances using particular software, tools, and data processing environments only as an example for the clarity of the description. The illustrative embodiments may be used in conjunction with other comparable or similarly purposed structures, systems, applications, or architectures. For example, other comparable mobile devices, structures, systems, applications, or architectures therefor, may be used in conjunction with such embodiment of the invention within the scope of the invention. An illustrative embodiment may be implemented in hardware, software, or a combination thereof. 
         [0033]    The examples in this disclosure are used only for the clarity of the description and are not limiting to the illustrative embodiments. Additional data, operations, actions, tasks, activities, and manipulations will be conceivable from this disclosure and the same are contemplated within the scope of the illustrative embodiments. 
         [0034]    Any advantages listed herein are only examples and are not intended to be limiting to the illustrative embodiments. Additional or different advantages may be realized by specific illustrative embodiments. Furthermore, a particular illustrative embodiment may have some, all, or none of the advantages listed above. 
         [0035]    With reference to the figures and in particular with reference to  FIGS. 1 and 2 , these figures are example diagrams of data processing environments in which illustrative embodiments may be implemented.  FIGS. 1 and 2  are only examples and are not intended to assert or imply any limitation with regard to the environments in which different embodiments may be implemented. A particular implementation may make many modifications to the depicted environments based on the following description. 
         [0036]      FIG. 1  depicts a block diagram of a network of data processing systems in which illustrative embodiments may be implemented. Data processing environment  100  is a network of computers in which the illustrative embodiments may be implemented. Data processing environment  100  includes network  102 . Network  102  is the medium used to provide communications links between various devices and computers connected together within data processing environment  100 . Network  102  may include connections, such as wire, wireless communication links, or fiber optic cables. 
         [0037]    Clients or servers are only example roles of certain data processing systems connected to network  102  and are not intended to exclude other configurations or roles for these data processing systems. Server  104  and server  106  couple to network  102  along with storage unit  108 . Software applications may execute on any computer in data processing environment  100 . Clients  110 ,  112 , and  114  are also coupled to network  102 . A data processing system, such as server  104  or  106 , or client  110 ,  112 , or  114  may contain data and may have software applications or software tools executing thereon. 
         [0038]    Only as an example, and without implying any limitation to such architecture,  FIG. 1  depicts certain components that are usable in an example implementation of an embodiment. For example, servers  104  and  106 , and clients  110 ,  112 ,  114 , are depicted as servers and clients only as example and not to imply a limitation to a client-server architecture. As another example, an embodiment can be distributed across several data processing systems and a data network as shown, whereas another embodiment can be implemented on a single data processing system within the scope of the illustrative embodiments. Data processing systems  104 ,  106 ,  110 ,  112 , and  114  also represent example nodes in a cluster, partitions, and other configurations suitable for implementing an embodiment. 
         [0039]    Device  132  is an example of a mobile device and is usable for executing an implementation of an embodiment, such as a version of application  115  that is configured in a manner suitable for execution on device  132 . For example, device  132  can take the form of a smartphone, a tablet computer, a laptop computer, client  110  in a stationary or a portable form, a wearable computing device, or any other similarly purposed device. Application  115  in client  112  implements an embodiment described herein. Application  115  operates in conjunction with database client  113 . Client  113  is any application that is configured to submit a query, such as a query constructed by application  115 , to a database server (not shown), to execute on database  109 . Database  109  comprises a repository of information according to schema  111  as described elsewhere in this disclosure. Data source  107  supplied data  117 . Data  117  is an example of a volume of data as described elsewhere. Application  115  manages dictionary  105 . Application  105  uses dictionary  105  to analyze data  117 , forms a query, submit the query to database client  113 , for extracting a result set (not shown) from database  109  where the records in the result set are related to the descriptions contained in data  117  even when database  109  does not include all or portions of those descriptions. 
         [0040]    Servers  104  and  106 , storage unit  108 , and clients  110 ,  112 , and  114  may couple to network  102  using wired connections, wireless communication protocols, or other suitable data connectivity. Clients  110 ,  112 , and  114  may be, for example, personal computers or network computers. 
         [0041]    In the depicted example, server  104  may provide data, such as boot files, operating system images, and applications to clients  110 ,  112 , and  114 . Clients  110 ,  112 , and  114  may be clients to server  104  in this example. Clients  110 ,  112 ,  114 , or some combination thereof, may include their own data, boot files, operating system images, and applications. Data processing environment  100  may include additional servers, clients, and other devices that are not shown. 
         [0042]    In the depicted example, data processing environment  100  may be the Internet. Network  102  may represent a collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) and other protocols to communicate with one another. At the heart of the Internet is a backbone of data communication links between major nodes or host computers, including thousands of commercial, governmental, educational, and other computer systems that route data and messages. Of course, data processing environment  100  also may be implemented as a number of different types of networks, such as for example, an intranet, a local area network (LAN), or a wide area network (WAN).  FIG. 1  is intended as an example, and not as an architectural limitation for the different illustrative embodiments. 
         [0043]    Among other uses, data processing environment  100  may be used for implementing a client-server environment in which the illustrative embodiments may be implemented. A client-server environment enables software applications and data to be distributed across a network such that an application functions by using the interactivity between a client data processing system and a server data processing system. Data processing environment  100  may also employ a service oriented architecture where interoperable software components distributed across a network may be packaged together as coherent business applications. 
         [0044]    With reference to  FIG. 2 , this figure depicts a block diagram of a data processing system in which illustrative embodiments may be implemented. Data processing system  200  is an example of a computer, such as servers  104  and  106 , or clients  110 ,  112 , and  114  in  FIG. 1 , or another type of device in which computer usable program code or instructions implementing the processes may be located for the illustrative embodiments. 
         [0045]    Data processing system  200  is also representative of a data processing system or a configuration therein, such as device  132  in  FIG. 1  in which computer usable program code or instructions implementing the processes of the illustrative embodiments may be located. Data processing system  200  is described as a computer only as an example, without being limited thereto. Implementations in the form of other devices, such as device  132  in  FIG. 1 , may modify data processing system  200 , modify data processing system  200 , such as by adding a touch interface, and even eliminate certain depicted components from data processing system  200  without departing from the general description of the operations and functions of data processing system  200  described herein. 
         [0046]    In the depicted example, data processing system  200  employs a hub architecture including North Bridge and memory controller hub (NB/MCH)  202  and South Bridge and input/output (I/O) controller hub (SB/ICH)  204 . Processing unit  206 , main memory  208 , and graphics processor  210  are coupled to North Bridge and memory controller hub (NB/MCH)  202 . Processing unit  206  may contain one or more processors and may be implemented using one or more heterogeneous processor systems. Processing unit  206  may be a multi-core processor. Graphics processor  210  may be coupled to NB/MCH  202  through an accelerated graphics port (AGP) in certain implementations. 
         [0047]    In the depicted example, local area network (LAN) adapter  212  is coupled to South Bridge and I/O controller hub (SB/ICH)  204 . Audio adapter  216 , keyboard and mouse adapter  220 , modem  222 , read only memory (ROM)  224 , universal serial bus (USB) and other ports  232 , and PCI/PCIe devices  234  are coupled to South Bridge and I/O controller hub  204  through bus  238 . Hard disk drive (HDD) or solid-state drive (SSD)  226  and CD-ROM  230  are coupled to South Bridge and I/O controller hub  204  through bus  240 . PCI/PCIe devices  234  may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. ROM  224  may be, for example, a flash binary input/output system (BIOS). Hard disk drive  226  and CD-ROM  230  may use, for example, an integrated drive electronics (IDE), serial advanced technology attachment (SATA) interface, or variants such as external-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device  236  may be coupled to South Bridge and I/O controller hub (SB/ICH)  204  through bus  238 . 
         [0048]    Memories, such as main memory  208 , ROM  224 , or flash memory (not shown), are some examples of computer usable storage devices. Hard disk drive or solid state drive  226 , CD-ROM  230 , and other similarly usable devices are some examples of computer usable storage devices including a computer usable storage medium. 
         [0049]    An operating system runs on processing unit  206 . The operating system coordinates and provides control of various components within data processing system  200  in  FIG. 2 . The operating system may be a commercially available operating system such as AIX® (AIX is a trademark of International Business Machines Corporation in the United States and other countries), Microsoft® Windows® (Microsoft and Windows are trademarks of Microsoft Corporation in the United States and other countries), Linux® (Linux is a trademark of Linus Torvalds in the United States and other countries), iOS™ (iOS is a trademark of Cisco Systems, Inc. licensed to Apple Inc. in the United States and in other countries), or Android™ (Android is a trademark of Google Inc., in the United States and in other countries). An object oriented programming system, such as the Java™ programming system, may run in conjunction with the operating system and provide calls to the operating system from Java™ programs or applications executing on data processing system  200  (Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle Corporation and/or its affiliates). 
         [0050]    Instructions for the operating system, the object-oriented programming system, and applications or programs, such as application  115  in  FIG. 1 , are located on storage devices, such as hard disk drive  226 , and may be loaded into at least one of one or more memories, such as main memory  208 , for execution by processing unit  206 . The processes of the illustrative embodiments may be performed by processing unit  206  using computer implemented instructions, which may be located in a memory, such as, for example, main memory  208 , read only memory  224 , or in one or more peripheral devices. 
         [0051]    The hardware in  FIGS. 1-2  may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in  FIGS. 1-2 . In addition, the processes of the illustrative embodiments may be applied to a multiprocessor data processing system. 
         [0052]    In some illustrative examples, data processing system  200  may be a personal digital assistant (PDA), which is generally configured with flash memory to provide non-volatile memory for storing operating system files and/or user-generated data. A bus system may comprise one or more buses, such as a system bus, an I/O bus, and a PCI bus. Of course, the bus system may be implemented using any type of communications fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. 
         [0053]    A communications unit may include one or more devices used to transmit and receive data, such as a modem or a network adapter. A memory may be, for example, main memory  208  or a cache, such as the cache found in North Bridge and memory controller hub  202 . A processing unit may include one or more processors or CPUs. 
         [0054]    The depicted examples in  FIGS. 1-2  and above-described examples are not meant to imply architectural limitations. For example, data processing system  200  also may be a tablet computer, laptop computer, or telephone device in addition to taking the form of a mobile or wearable device. 
         [0055]    With reference to  FIG. 3 , this figure depicts a block diagram of an example configuration for identifying related information in dissimilar data in accordance with an illustrative embodiment. Application  302  is an example of application  115  in  FIG. 1 . Data  304  is an example of data  117  in  FIG. 1 . Schema  306  is an example of schema  111  in  FIG. 1 . Dictionary  308  is an example of dictionary  105  in  FIG. 1 . Database  310  is an example of database  109  in  FIG. 1 . 
         [0056]    Component  312  manages dictionary  308 . In one embodiment component  312  creates dictionary  308 , such as by creating an initial set of key entries with their corresponding normalized forms, types, or a combination thereof. In another embodiment component  312  manipulates dictionary  308 , such as by adding, deleting, changing, augmenting, or otherwise modifying key entries in dictionary  308 . 
         [0057]    Data annotation component  314  identifies in data  304  descriptions, words, phrases, content, or portions thereof, which match within a tolerance one or more keys in one or more entries of dictionary  308 . A tolerance for matching a description, word, phrase, content, or a portion thereof with a key can be defined in many ways, including but not limited to accepting different grammatical forms or synonyms as matching, accepting phrases conveying similar meanings as matching, accepting words in different languages as matching, accepting entire strings as matching when only some portions of the strings match, and so on. For example, if a key includes “fracking”, an embodiment regards a sentence in a volume that pertains to “hydraulic fracturing” or “shale extraction” as matching the key. Accordingly, component  314  identifies such sentences as matching a key within a tolerance. 
         [0058]    Component  316  constructs a query using one or more normalized forms, one or more types, or some combination thereof, which correspond to the one or more keys that component  314  has matched with some description, word, phrase, content, or a portion thereof in data  304 . The query from component  316  follows a query specification of database  310 . In other words, component  316  constructs the query according to schema  306  of database  310  and by using a query language supported by database  310 . 
         [0059]    Component  318  presents the query constructed by component  316  for execution on database  310 . Component  318  receives the result set from the execution of the query on database  310 . Component  318  presents the result set as report  320 . 
         [0060]    With reference to  FIG. 4 , this figure depicts an example of a volume of data and annotation therein in accordance with an illustrative embodiment. Data  400  is an example of data  304  in  FIG. 3 . 
         [0061]    An embodiment, such as an embodiment implemented in application  302  in  FIG. 3 , annotates description, word, phrase, content, or a portion thereof in data  400  depending on finding a match within a tolerance with one or more keys in a dictionary, such as in dictionary  308  in  FIG. 3 . For example, based on some example keys depicted in  FIG. 5  and other keys, the application annotates certain words, strings, phrases, or portions or combinations thereof as annotations  402 ,  404 ,  406 ,  408 ,  410 ,  412 , and  414 . For example, annotation  402  contains the word “Arabian”, which matches within a tolerance with a key, and the word “Basin” which matches within the tolerance with another key. Similarly, annotation  404  matches another key within the tolerance. 
         [0062]    The application further determines that the numeric data of annotation  406  is followed by a unit of measure, to wit, “ft”. According to a regular expression analysis, the application determines that annotation  406  contains a measurement. 
         [0063]    Earlier in that sentence, the application also found the word “depth” ( 406 A), which provides the context for the measurement according to the grammar of the language of the sentence. Accordingly, the application concludes that annotation  406  is a measurement of depth. Such grammatical analysis for the context can be performed in any known technique, including but not limited to natural language processing (NLP). 
         [0064]    Similarly, annotation  408  contains the words “Continental rift”, all or parts of which match one or more keys within the tolerance. Annotations  410  and  412  similarly contain the words “Passive margin” and “foreland basins”, respectively, all or parts of which match one or more keys within the tolerance. Similarly, annotation  414  contains the words “rift basins”, all or parts of which match one or more keys within the tolerance. 
         [0065]    Once the annotations are matched with certain keys, the application constructs a query using the normalized forms, types, or a combination thereof from those keys as described elsewhere in this disclosure. The application then obtains a result set responsive to the query as described elsewhere in this disclosure.  FIG. 5  depicts examples of the keys, the query, and the result set related to example annotated data  400 . 
         [0066]    With reference to  FIG. 5 , this figure depicts example keys, query, and result set for identifying related information in dissimilar data in accordance with an illustrative embodiment. Keys  502  can be used with data  400  in  FIG. 4 . Query  504  is constructed using certain annotations in data  400  in  FIG. 4 . The records in result set  506  are returned by executing query  504  on database  310  in  FIG. 3 . 
         [0067]    Consider example key entry  502 A in keys  502 . Portion  1  of key entry  502 A contains the word “Arabian”. Portion  1  of key entry  502 A is regarded as a token and is used to identify matching description, word, phrase, content, or a portion thereof for annotating, such as to identify annotation  402  in  FIG. 4 . 
         [0068]    Portions  2 ,  3 , and  4  together form the information associated with the token in portion  1  for key entry  502 A. For example, portion  2  identifies a type associated with the token “Arabian”. In this example, the type of the token is a “region”. In other words, information of a type similar to the type of the token in portion  1  will be found in a column that has a column name “region” in the schema of the database that serves as the repository of information that is to be related with data  400 . 
         [0069]    Portions  3  and  4  identify more than one alternative normalized forms associated with the token “Arabian”. In this example, the normalized forms of the token are “North Africa” according to portion  3  or “Middle East” according to portion  4 . In other words, a related record will include data from a row containing the data “North Africa” or data “Middle East” in a column that has a column name “region” in the schema of the database. 
         [0070]    As can be seen in other key entries in key entries  502 , some key entries can have one or more normalized forms corresponding to a token. When more than one normalized forms are provided in a key entry, those normalized forms are the possible alternative normalized forms for the token of the key entry. Similarly, some tokens can have one or more types (not shown) corresponding to a token. When such a situation arises, separate key entries can be created for each alternative type, each separate key entry having one type, and one or more normalized forms. 
         [0071]    Query  504  is a simple example query constructed by application  302  by applying key entries  502  and other key entries not shown, and regular expression analysis of data  400  of  FIG. 4 . For example, the query seeks to identify (select) the information of those reservoirs (from a reservoir table in the entity&#39;s database or repository of information), which are in the region of annotations  402  (Arabian) or  404  (Gulf of Mexico) and basin of annotation  402  (Basin) or  412  (foreland basins) or  414  (rift basin), whose minimum depth is less than or equal to the depth measurement of annotation  406  (32,808 ft, context “depth”  406 A) and whose maximum depth is greater than or equal to the depth measurement of annotation  406  (32808 ft, context “depth”  406 A). 
         [0072]    Result set  506  is an example presentation of example records returned by the entity&#39;s database in response to query  504 . For example, record  506 A identifies a reservoir known in the entity&#39;s repository of information as reservoir  1011500 . Reservoir  1011500  matches the description in data  400  because that reservoir lies in the region of Gulf of Mexico that is a normalized form of annotation  404  in data  400 , has a minimum depth of 32000 ft that is less than depth 32808 ft provided in data  400 , and has a maximum depth of 34000 ft that is greater than depth 32808 ft provided in data  400 . Additionally, record  506 A provides that reservoir  1011500  is situated at a basin depth classified as “continental rift” according to annotation  408 . 
         [0073]    Similarly, record  506 B identifies a reservoir known in the entity&#39;s repository of information as reservoir  1011870 . Reservoir  1011870  matches the description in data  400  because that reservoir lies in the region of Middle East that is a normalized form of annotation  402  in data  400 , has a minimum depth of 31500 ft that is less than depth 32808 ft provided in data  400 , and has a maximum depth of 33500 ft that is greater than depth 32808 ft provided in data  400 . Additionally, record  506 B provides that reservoir  1011870  is situated at a basin depth classified as “foreland” according to annotation  412 . 
         [0074]    Similarly, record  506 C identifies a reservoir known in the entity&#39;s repository of information as reservoir  1011220 . Reservoir  1011220  matches the description in data  400  because that reservoir lies in the region of Gulf of Mexico that is a normalized form of annotation  404  in data  400 , has a minimum depth of 30000 ft that is less than depth 32808 ft provided in data  400 , and has a maximum depth of 35000 ft that is greater than depth 32808 ft provided in data  400 . Additionally, record  506 C provides that reservoir  1011220  is situated at a basin depth classified as “rift” according to annotations  408 ,  412 , and  414 . 
         [0075]    For the clarity of the depictions and the descriptions, not all annotations are depicted in  FIG. 4 , not all key entries applicable to data  400  are shown in  FIG. 5 , and not all parts of query  504  are shown in  FIG. 5 . Furthermore, result set  506  can include more of less records than depicted in  FIG. 5  depending upon the information available in the entity&#39;s repository. As can be seen from the example depictions and descriptions though, records  506 A-C could not have been automatically identified from the repository without the novel steps of an embodiment. Not only would such identification require great human effort even with such limited example data, but such effort would also be error prone. Such human effort would be prohibitive or impractical with comparatively more data in the volume and more information in the repository. 
         [0076]    With reference to  FIG. 6 , this figure depicts a flowchart of an example process for identifying related information in dissimilar data in accordance with an illustrative embodiment. Process  600  can be implemented in application  302  in  FIG. 3 . 
         [0077]    The application receives data, such as data  400  in  FIG. 4 , that is to be related to information in a user&#39;s repository, such as database  310  in  FIG. 3  (block  602 ). The application, using a dictionary such as dictionary  308  in  FIG. 3 , identifies relatable portions for annotation in the data (block  604 ). 
         [0078]    The application, using the dictionary and the schema, identifies the types and normalized forms of the annotations in the data (block  606 ). The application constructs a query using the identified types and normalized forms (block  608 ). 
         [0079]    The application executes the query or causes the query to be executed on the repository (block  610 ). The application presents the result set obtained from executing the query (block  612 ). The application ends process  600  thereafter. 
         [0080]    Thus, a computer implemented method, system or apparatus, and computer program product are provided in the illustrative embodiments for identifying related information in dissimilar data. Where an embodiment or a portion thereof is described with respect to a type of device, the computer implemented method, system or apparatus, the computer program product, or a portion thereof, are adapted or configured for use with a suitable and comparable manifestation of that type of device. 
         [0081]    The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
         [0082]    The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
         [0083]    Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
         [0084]    Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
         [0085]    Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
         [0086]    These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0087]    The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0088]    The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.