Transforming a lexicon that describes an information asset

Systems, computer-implemented methods, and computer program products to transform a lexicon that describes an information asset are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a term validation component that can determine from a subject matter expert, a validated term that can indicate validation of a candidate term that describes an information asset. The computer executable components can further comprise a lexicon transforming component that, based on the validated term, can transform a lexicon that describes the information asset, by incorporating the validated term into the lexicon.

TECHNICAL FIELD

The embodiments described herein relate to information processing, and more specifically, to management of information assets.

SUMMARY

The following presents a summary to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements, nor delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, systems, devices, computer-implemented methods, and computer program products that transform a lexicon that describes an information asset are described.

As discussed further below, in accordance with one or more embodiments described herein, an expert can be utilized by embodiments in different circumstances, e.g., a subject matter expert. It is important to note that, when utilized by some embodiments described herein, a subject matter expert can refer to an electrical or mechanical component that can be implemented in hardware, software, a repository, artificial intelligence or machine learning device, repository, or other type of entity for providing the functions described. For example, according to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer-executable components can comprise a term validation component that can determine from a subject matter expert, a validated term that can indicate validation of a candidate term that describes an information asset. The computer executable components can further comprise a lexicon transforming component that, based on the validated term, can transform a lexicon that describes the information asset, by incorporating the validated term into the lexicon.

In some implementations, the above-noted system can further comprise a query augmenting component that can augment a query of a knowledge base of information assets, by employing the validated term of the lexicon. In additional or alternative implementations, the computer-executable components can further comprise an expert selecting component that, based on the information asset, can select the subject matter expert. An approach to selecting the subject matter expert based on the information asset comprises selecting the subject matter expert based on a relationship between the subject matter expert and the information asset, e.g., the subject matter expert can be an owner of the information asset. In some implementations, selecting the subject matter expert based on the information asset can include determining that the subject matter expert has expertise regarding the information asset that exceeds a threshold and selecting the subject matter expert based on the expertise.

In additional or alternative implementations, the computer-executable components can further comprise a term submitting component that can submit the candidate term to the subject matter expert that can validate the candidate term. In additional or alternative implementations, the computer-executable components can further comprise a configuration component that can configure a neural network that can analyze information assets by employing the validated term of the lexicon.

In additional or alternative implementations, the computer executable components can further include a keyword identifying component that can identify, by employing distributional semantics, a salient keyword describing the information asset based on an analysis of textual content describing the information asset. Additionally, in a variation, the computer executable components can further include a candidate term selecting component that can select the salient keyword as the candidate term.

According to another embodiment, a computer-implemented method can comprise determining from a subject matter expert a validated term that indicates validation of a candidate term that describes an information asset. The computer-implemented method can further include operations to, based on the validated term, transform a lexicon that describes the information asset by incorporating the validated term into the lexicon. The computer-implemented method can further include operations to augment, by the device, a query of a knowledge base of information assets by employing the validated term of the lexicon.

The computer-implemented method can further include operations to, based on the information asset, select, by the device, the subject matter expert. The computer-implemented method can further include operations to submit, by the device, the candidate term to the subject matter expert that validates the candidate term. In some implementations, the selecting the subject matter expert can be based on factors including, but not limited to: characteristics of the subject matter expert, a relationship between the subject matter expert and the information asset, e.g., the subject matter expert can be an entity that owns, manages, controls, describes, monitors, or is otherwise related to, the information asset.

In additional or alternative embodiments, the computer-implemented method can further comprise selecting the subject matter expert based on the information asset. For example, a subject matter expert can be selected because of access to information about the asset that exceeds a threshold. In alternative or additional embodiments, the computer-implemented method can further include configuring, by the device, a neural network that can analyze information assets by employing the validated term of the lexicon. In additional or alternative embodiments, the computer-implemented method can further comprise identifying, by the device employing distributional semantics, a salient keyword describing the information asset based on an analysis of textual content describing the information asset, and selecting, by the device, the salient keyword as the candidate term.

According to another embodiment, a computer program product that can transform a lexicon that describes an information asset is provided. The computer program product can comprise a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to determine from a subject matter expert, a validated term that indicates validation of a candidate term that describes the information asset, and based on the validated term, transform the lexicon.

In some embodiments, the program instructions are further executable by the processor to cause the processor to augment a query of a knowledge base of information assets by employing the validated term of the lexicon. Further, the program instructions can be further executable to cause the processor to, based on the information asset, select the subject matter expert, and submit the candidate term to the subject matter expert that validates the candidate term. In additional or alternative embodiments, the program instructions are further executable by the processor to cause the processor to select the subject matter expert based on the information asset by selecting the subject matter expert based on a relationship between the subject matter expert and the information asset.

In additional embodiments, the program instructions are further executable by the processor to cause the processor to select the subject matter expert based on the information asset can include determining that the subject matter expert has expertise regarding the information asset that exceeds a threshold, and the subject matter expert can be selected based on the expertise. In additional embodiments, the program instructions are further executable by the processor to cause the processor to configure, by the device, a neural network that can analyze information assets by employing the validated term of the lexicon. In additional embodiments, the program instructions are further executable by the processor to cause the processor to identify, by the device employing distributional semantics, a salient keyword describing the information asset based on an analysis of textual content describing the information asset, and selecting, by the device, the salient keyword as the candidate term.

Other embodiments may become apparent from the following detailed description when taken in conjunction with the drawings.

DETAILED DESCRIPTION

As referenced herein, an entity can comprise a human, a client, a user, a computing device, a software application, an agent, a machine learning model, an artificial intelligence, and another entity. It should be appreciated that such an entity can implement one or more of the embodiments described herein. An example entity described by one or more embodiments described here is a subject-matter expert validator, also termed a subject-matter expert, an expert, or an expert validator.

FIG.1illustrates a block diagram of an example, non-limiting system100that can transform a lexicon that describes an information asset, in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity.

In one or more embodiments, information assets165can broadly include a variety of internal assets of an organization, e.g., and asset that the organization owns, produces, offers to clients, etc. An organization information asset can also be any artifact or intellectual property owned by the organization. As described in one or more embodiments, an under-defined target entity refers to an information asset where a knowledge base that describes the information asset is determined to require additional information for some use of the information asset.

As would be appreciated by one having skill in the relevant art(s), given the description herein, one or more embodiments can identify information about an under-defined target entity in available, potentially informal text. In some examples, when an information asset is in a rapidly evolving technical area, the vocabulary used to describe the asset can be in flux, e.g., terms to describe the information asset can be adopted and discarded relatively quickly compared to more established technical areas.

As described further herein, one or more embodiments can supplement and update terms that describe an information asset by maintaining lexicon108with terms that describe information asset165. Some approaches described herein can manage lexicon108by selecting candidate terms for use in transforming lexicon108to better describe current aspects of information asset165. Once selected, candidate terms can be validated for use in a variety of ways, including the use of selected outside knowledge from subject matter experts, e.g., received via input device180.

In one or more embodiments, memory104can store one or more computer and machine readable, writable, and executable components and instructions that, when executed by processor106(e.g., a classical processor, a quantum processor, etc.), can perform operations defined by the executable components and instructions. For example, memory104can store computer and machine readable, writable, and executable components and instructions that, when executed by processor106, can execute the various functions described herein relating to lexicon transforming system102, lexicon transforming component120, term validation component110, and another components associated with lexicon transforming systems as described herein with or without reference to the various figures of the one or more embodiments described herein.

Memory104can comprise volatile memory (e.g., random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), etc.) and non-volatile memory (e.g., read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), etc.) that can employ one or more memory architectures. Further examples of memory104are described below with reference to system memory816andFIG.8. Such examples of memory104can be employed to implement any of the embodiments described herein.

Processor106can comprise one or more types of processors and electronic circuitry (e.g., a classical processor, a quantum processor, etc.) that can implement one or more computer and machine readable, writable, and executable components and instructions that can be stored on memory104. For example, processor106can perform various operations that can be specified by such computer and machine readable, writable, and executable components and instructions including, but not limited to, logic, control, input/output (I/O), arithmetic, and the like. In some embodiments, processor106can comprise one or more central processing unit, multi-core processor, microprocessor, dual microprocessors, microcontroller, System on a Chip (SOC), array processor, vector processor, quantum processor, and another type of processor. Further examples of processor106are described below with reference to processing unit814andFIG.8. Such examples of processor106can be employed to implement any embodiments described herein.

As depicted, example100can include lexicon transforming system102coupled to information assets165and input device180, via network190. It should be noted that, when an element is referred to herein as being “coupled” to another element, it can describe one or more different types of coupling. For example, when an element is referred to herein as being “coupled” to another element, it can be described one or more different types of coupling including, but not limited to, chemical coupling, communicative coupling, capacitive coupling, electrical coupling, electromagnetic coupling, inductive coupling, operative coupling, optical coupling, physical coupling, thermal coupling, and another type of coupling.

In one or more embodiments, lexicon transforming system102can include term validation component110, lexicon transforming component120, memory104, processor106, storage109, and any other components that can be used to enable different functions described herein. It should be appreciated that the embodiments described herein depict in various figures disclosed herein are for illustration only, and as such, the architecture of such embodiments are not limited to the systems, devices, and components depicted therein. For example, in some embodiments, system100and lexicon transforming system102can further comprise various computer and computing-based elements described herein with reference to operating environment800andFIG.8. In several embodiments, such computer and computing-based elements can be used in connection with implementing one or more of the systems, devices, components, and computer-implemented operations shown and described in connection withFIG.1and other figures disclosed herein.

Lexicon transforming system102, memory104, processor106, lexicon transforming component120, term validation component110, and any other component of lexicon transforming system102as described herein, can be communicatively, electrically, operatively, and optically coupled to one another via a bus112to perform functions of system100, lexicon transforming system102, and any components coupled therewith. Bus112can comprise one or more memory bus, memory controller, peripheral bus, external bus, local bus, a quantum bus, and another type of bus that can employ various bus architectures. Further examples of bus112are described below with reference to system bus818andFIG.8. Such examples of bus112can be employed to implement any of the embodiments described herein.

Lexicon transforming system102can comprise any type of component, machine, device, facility, apparatus, and instrument that comprises a processor and can be capable of effective and operative communication with a wired and wireless network. All such embodiments are envisioned. For example, lexicon transforming system102can comprise a server device, a computing device, a general-purpose computer, a special-purpose computer, a quantum computing device (e.g., a quantum computer), a tablet computing device, a handheld device, a server class computing machine and database, a laptop computer, a notebook computer, a desktop computer, a cell phone, a smart phone, a consumer appliance and instrumentation, an industrial and commercial device, a digital assistant, a multimedia Internet enabled phone, a multimedia players, and another type of device.

In some embodiments, lexicon transforming system102can be coupled (e.g., communicatively, electrically, operatively, optically, etc.) to one or more external systems, sources, and devices (e.g., classical and quantum computing devices, communication devices, etc.) via network190. In some embodiments, network190can comprise wired and wireless networks, including, but not limited to, a cellular network, a wide area network (WAN) (e.g., the Internet) or a local area network (LAN). For example, lexicon transforming system102can communicate with one or more external systems, sources, and devices, for instance, computing devices (and vice versa) using virtually any desired wired or wireless technology, including but not limited to: wireless fidelity (Wi-Fi), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra-mobile broadband (UMB), high speed packet access (HSPA), Zigbee and other 802.XX wireless technologies and legacy telecommunication technologies, BLUETOOTH®, Session Initiation Protocol (SIP), ZIGBEE®, RF4CE protocol, WirelessHART protocol, 6LoWPAN (IPv6 over Low power Wireless Area Networks), Z-Wave, an ANT, an ultra-wideband (UWB) standard protocol, and other proprietary and non-proprietary communication protocols. In such an example, lexicon transforming system102can thus include hardware (e.g., a central processing unit (CPU), a transceiver, a decoder, quantum hardware, a quantum processor, etc.), software (e.g., a set of threads, a set of processes, software in execution, quantum pulse schedule, quantum circuit, quantum gates, etc.) or a combination of hardware and software that communicates information between lexicon transforming system102and external systems, sources, and devices (e.g., computing devices, communication devices, etc.).

In one or more embodiments described herein, lexicon transforming system102can perform (e.g., via processor106) operations including, but not limited to, lexical expansion, pattern identification, term extraction, term validation, micro-adjudication, and lexicon transformation, executed by and associated with one or more components, e.g., lexicon transforming component120, and term validation component110. For example, term validation component110can determine (e.g., via processor106), from a subject matter expert, a validated term that can indicate validation of a candidate term that describes an information asset. In some embodiments, term validation component110can facilitate such, performing the following: identifying a candidate term for validation, selecting an expert validator for validation, communicating the candidate term to a selected expert validator for validation. Such will be discussed in greater detail with reference toFIG.2and the accompanying text.

The computer executable components of lexicon transforming system102can further comprise a lexicon transforming component120that, based on the validated term, can transform a lexicon108that describes the information asset165, by incorporating the validated term into the lexicon. In some embodiments, lexicon transforming component120can perform (e.g., via processor106) operations including, but not limited to: ontology selection, ontology alignment, and lexicon transformation. Such will be discussed in greater detail with reference toFIG.4and the accompanying text.

Additional details regarding some implementations that can use subject matter experts to validate candidate terms are provided withFIG.2below. Selecting and utilizing subject-matter expert validators by one or more embodiments, is described withFIG.3, below. Selecting candidate terms and utilizing one or more embodiments to augment queries are discussed withFIG.5below, and utilizing different artificial intelligence approaches to transform lexicon108is described withFIG.6below.

FIG.2illustrates a block diagram of an example, non-limiting system200that can validate candidate terms for a lexicon that describes an information asset, in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity.

One approach that can be used by one or more embodiments to identify candidate terms220is lexicon expansion260. In one or more embodiments, candidate terms220for validation by term validation component110can be identified by lexicon expansion260from different sources, including but not limited to, selecting references to be used for term expansion, and analyzing the references for similar terms to be used to add descriptive elements to the word to be expanded. Example references that can be used for expansion include, but are not limited to, pertinent large textual corpora, organization white papers and technical documents (not shown). In the example depicted inFIG.2, five candidate terms have been identified as expansion terms that can potentially be applied to the analyzed terms, and these expansion terms can further be used to validate other terms for use describing information asset165.

As depicted and discussed above, term validation component110can use different approaches to validate candidate terms220and utilize lexicon transforming component120to transform270lexicon108, e.g., by adding validated terms225to lexicon108. One approach that can be used by term validation component110to validate candidate terms220is by utilizing information provided by subject matter experts210. This use of subject matter experts by embodiments can include, but is not limited to, micro-adjudication215. In one or more embodiments, adjudication tasks can be submitted to subject matter experts210to determine whether respective candidate terms220are validated. For example, micro-adjudication215tasks can be estimated or limited to last no longer than a relatively short duration, 5 minutes. In some implementations, multiple subject matter experts210can be assigned adjudication tasks for the same lexicon at the same time, with adjudications being collated for use by lexicon transforming component120. With this approach, lexicon108can be termed a curated lexicon, e.g., as a dynamic resource referencing information asset165.

As described further withFIG.3below, subject matter experts210can be selected using a variety of approaches, including, but not limited to, utilizing organization asset owners of information asset165.

FIG.3illustrates a block diagram of an example300of non-limiting lexicon transforming system302that can transform a lexicon that describes an information asset, in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity. As depicted, example300can include lexicon transforming system302coupled to information assets165and input device180, via network190. In one or more embodiments, lexicon transforming system302can include term validation component110, lexicon transforming component120, expert selecting component310, term submitting component320, and any other components that can be used to enable different functions described herein.

In lexicon transforming system302, the computer-executable components can further comprise expert selecting component310that, based on information asset165, can select subject matter expert210. An approach to selecting subject matter expert210based on the information asset can select subject matter expert210based on a relationship between subject matter expert210and information asset165, e.g., the subject matter expert can be an owner of the information asset. In some implementations, selecting the subject matter expert based on the information asset can include determining that the subject matter expert has expertise regarding the information asset that exceeds a threshold and selecting the subject matter expert based on the expertise. One approach to selecting subject matter experts can include use of a recommender system, where users can be prompted to identify a certain number of top experts at the organization for information asset165. Further, in a process that can use micro-adjudication215, potential experts identified for an information asset can be relayed to the potential subject matter expert, as well as other subject matter experts210for assessment, e.g., similar the validation process described above.

In one or more implementations of computer-implemented methods described above, the computer-implemented methods can further include operations to, based on the information asset, select, by the device, the subject matter expert. In some implementations, the selecting the subject matter expert can be based on characteristics of the subject matter expert, based on a relationship between the subject matter expert and the information asset, e.g., the subject matter expert can be an owner of the information asset. For example, one computer-implemented method to select the subject matter expect can include different operations, including, but not limited to, identifying the subject-matter associated with the information asset, generating a query for the expert validator, and communicating the query to the validator. In one or more implementations of the computer program product described above, the program instructions are further executable by the processor to cause the processor to select the subject matter expert based on the information asset by selecting the subject matter expert based on a relationship between the subject matter expert and the information asset.

FIG.4illustrates a block diagram of an example400of non-limiting lexicon transforming system402that can transform a lexicon that describes an information asset, in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity. As depicted, example400can include lexicon transforming system402coupled to information assets165and input device180, via network190. In one or more embodiments, lexicon transforming system402can include term validation component110, lexicon transforming component120, expert selecting component310, term submitting component320, keyword identifying component410, candidate term selecting component420, and any other components that can be used to enable different functions described herein.

As noted above, one approach to selecting candidate terms220for validation is to utilize lexicon expansion260. An additional approach can utilize keyword identifying component410that can identify, by employing distributional semantics, a salient keyword describing information asset165based on factors including, but not limited to, an analysis of textual content describing the information asset. In different embodiments, utilizing distributional semantics can include processes that include, but are not limited to, selecting a source related to the informational asset, identifying patterns of terms related to the information asset and selecting terms based on the semantic relationships identified. In a simple example, for an asset that includes an omelet, recipes for omelets could be identified as a source, with phrases having particular verb and noun patterns, e.g., “heat the oil,” “slice the onions,” “add the eggs,” and “fry the mushrooms.” From this analysis, identified keywords can include oil, onions, eggs, and mushrooms. Once identified by keyword identifying component410, candidate term selecting component420can select the salient keyword as one of candidate terms220. For example, different approaches to evaluating the descriptiveness of keywords can be applied by term selecting component420, including assessing the frequency of the term in sources related to the informational asset

Returning to lexicon transforming component120, in another function of one or more embodiments, in some circumstances, when lexicon108is transformed, an ontology alignment process can be performed. In some implementations, ontology alignment can assess the similarity of lexicon terms for information asset165, and when similarity between terms deviates more than a threshold level, in some circumstances, terms can be removed from the lexicon.

FIG.5illustrates a block diagram of an example500of non-limiting lexicon transforming system502that can transform a lexicon that describes an information asset, in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity. As depicted, example500can include lexicon transforming system502coupled to information assets165and input device180, via network190. In one or more embodiments, lexicon transforming system402can include term validation component110, lexicon transforming component120, expert selecting component310, term submitting component320, keyword identifying component410, candidate term selecting component420, query augmenting component510, and any other components that can be used to enable different functions described herein.

As described above, one or more approaches can use lexicon expansion260and micro-adjudication215by subject matter experts210to identify valid terms for describing information asset165. In one or more additional embodiments, these components and processes can be used to augment queries for information assets, e.g., stored in a knowledge base. By generating keywords (e.g., with keyword identifying component410), utilizing feedback from subject matter experts210, and identifying similar terms in available lexicons, searches can be more likely to identify useful information assets.

In one or more embodiments, the query augmentation process can be performed by query augmenting component510that can augment a query of a knowledge base of information assets by employing validated term of a lexicon.

In one or more additional embodiments, in a function related to queries, for some queries of information assets, health scores lexicons can be updated to reflect different attributes of the lexicon and the information assets. Different factors that can be used to generate and periodically update health scores include, but are not limited to, freshness of the lexicon, popularity of the asset, the variety of the terms of the lexicon as compared to other lexicons, numbers and qualifications of contributors to the lexicon, and the size of the lexicon.

Generally speaking, in one or more embodiments, the health-score is a linear combination of the single indicators, each weighted by a scalar factor. In variations to this approach, weights and thresholds can be applied to different factors, e.g., to reflect the relevance of the factor.

FIG.6illustrates a block diagram of an example600of non-limiting lexicon transforming system602that can employ artificial intelligence and machine learning to transform a lexicon that describes an information asset, in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity. As depicted, example600can include lexicon transforming system502coupled to information assets165and input device180, via network190. In one or more embodiments, lexicon transforming system402can include term validation component110, lexicon transforming component120, expert selecting component310, term submitting component320, keyword identifying component410, candidate term selecting component420, query augmenting component510, artificial intelligence component610, configuration component620, and any other components that can be used to enable different functions described herein.

Some embodiments of artificial intelligence component610can generate classifications, correlations, inferences and/or expressions associated with principles of artificial intelligence. For instance, system components can employ an automatic classification system and/or an automatic classification process to determine candidate terms220. In one example, candidate term selection component420can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to learn and/or generate inferences with respect to candidate terms220.

In one or more embodiments, configuration component620can configure neural network650for different system functions, e.g., updating lexicon108based on candidate terms220and subject matter experts210. In another function that can be performed by neural network650, configuration component610can be used to assess semantic distances between aspects of subject matter experts and information assets.

One or more embodiments can employ any suitable machine-learning based techniques, statistical-based techniques and/or probabilistic-based techniques. For example, one or more embodiments can employ expert systems, fuzzy logic, SVMs, Hidden Markov Models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analytical systems, systems employing Bayesian models, etc. For example, one or more embodiments can perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least square machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and/or a set of different machine learning computations.

It is to be appreciated that one or more embodiments described herein (e.g., validation component110, lexicon transforming component120, expert selecting component310, term submitting component320, keyword identifying component410, candidate term selecting component420, query augmenting component510, as well as other system components) perform functions that cannot be performed by a human (e.g., is greater than the capability of a single human mind). For example, an amount of data processed, a speed of data processed and/or data types of data processed by the system100over a certain period of time can be greater, faster, and different than an amount, speed, and data type that can be processed by a single human mind over the same period of time. One or more embodiments can also be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, etc.) while also performing the above-referenced functions and processes, e.g., including information that can be impossible to obtain manually by a user.

FIG.7illustrates a flow diagram of an example, non-limiting computer-implemented method700that can transform a lexicon that describes an information asset (e.g., via lexicon transforming system102), in accordance with one or more embodiments described herein. Repetitive description of like elements and processes employed in respective embodiments is omitted for sake of brevity.

At702, computer-implemented method700can comprise determining, by a device operatively coupled to a processor, from a subject matter expert a validated term that indicates validation of a candidate term that describes an information asset. For example, in one or more embodiments, computer-implemented method700can include determining lexicon transforming system320via processor106, from subject matter expert210(e.g., selected via expert selecting component310) validated term225that indicates the validation (e.g., via term validation component110) of candidate term220that describes information asset165.

At704, computer-implemented method700can comprise, based on the validated term, transforming, by the device, a lexicon that describes the information asset by incorporating the validated term into the lexicon. For example, in one or more embodiments, computer-implemented method700can include, based on validated term225, transforming by lexicon transforming system320, lexicon108(e.g., via lexicon transforming component120) that describes the information asset165by incorporating validated term225into lexicon108.

In an additional embodiment, at706, computer-implemented method700can comprise augmenting, by the device, a query of a knowledge base of information assets by employing the validated term of the lexicon. For example, in one or more embodiments, computer-implemented method700can include augmenting (e.g., via term submitting component320), a query (e.g., submitted via input device180) of a knowledge base of information assets165by employing validated term225of the lexicon108.

At least in practical implementations, at scale, lexicon transforming system102can employ combinations of hardware and software to solve problems that are highly technical in nature, that are not abstract and that cannot be performed as a set of mental acts by a human. In some embodiments, one or more of the processes described herein can be performed by one or more specialized computers (e.g., a specialized processing unit, a specialized classical computer, a specialized quantum computer, etc.) to execute defined tasks related to the various technologies identified above. Lexicon transforming system102and/or components thereof, can be employed to solve new problems that arise through advancements in technologies mentioned above, employment of quantum computing systems, cloud computing systems, computer architecture, and/or another technology.

It is to be appreciated that, in one or more embodiments, lexicon transforming system102can utilize various combinations of electrical components, mechanical components, and circuitry that cannot be replicated in the mind of a human or performed by a human. As would be understood by one having skill in the relevant art(s), given the description herein, for practical implementations at scale the various operations that can be executed by lexicon transforming system102and components thereof, are greater than the capability of a human mind. For instance, the amount of data processed, the speed of processing such data, or the types of data processed by lexicon transforming system102over a certain period of time can be greater, faster, or different than the amount, speed, or data type that can be processed by a human mind over the same period of time.

According to several embodiments, lexicon transforming system102can also be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, etc.) while also performing the various operations described herein. It should be appreciated that such simultaneous multi-operational execution is beyond the capability of a human mind. It should also be appreciated that lexicon transforming system102can include information that is impossible to obtain manually by an entity, such as a human user. For example, the type, amount, and/or variety of information included in lexicon transforming system102, lexicon transforming component120, and term validation component110can be more complex than information obtained manually by a human user.

Lexicon transforming system102can comprise one or more computer and machine readable, writable, and executable components and instructions that, when executed by processor106(e.g., a classical processor, a quantum processor, etc.), can perform operations defined by such components and instructions. Further, in numerous embodiments, any component associated with lexicon transforming system102, as described herein with or without reference to the various figures of the one or more embodiments described herein, can comprise one or more computer and machine readable, writable, and executable components and instructions that, when executed by processor106, can perform operations defined by such components and instructions. For example, lexicon transforming component120, term validation component110, and any other components associated with lexicon transforming system102as disclosed herein (e.g., communicatively, electronically, operatively, and optically coupled with and employed by lexicon transforming system102), can comprise such computer and machine readable, writable, and executable components and instructions. Consequently, according to numerous embodiments, lexicon transforming system102and any components associated therewith as disclosed herein, can employ processor106to execute such computer and machine readable, writable, and executable components and instructions to perform one or more operations described herein with reference to lexicon transforming system102and any such components associated therewith.

It should be noted that lexicon transforming system102can be associated with a cloud computing environment. For example, lexicon transforming system102can be associated with cloud computing environment950described below with reference toFIG.9and one or more functional abstraction layers described below with reference toFIG.10(e.g., hardware and software layer1060, virtualization layer1070, management layer1080, and workloads layer1090).

Lexicon transforming system102and components thereof (e.g., lexicon transforming component120, term validation component110, etc.) can employ one or more computing resources of cloud computing environment950described below with reference toFIG.9and one or more functional abstraction layers (e.g., quantum software, etc.) described below with reference toFIG.10to execute one or more operations in accordance with one or more of the embodiments described herein. For example, cloud computing environment950and such one or more functional abstraction layers can comprise one or more classical computing devices (e.g., classical computer, classical processor, virtual machine, server, etc.), quantum hardware, and quantum software (e.g., quantum computing device, quantum computer, quantum processor, quantum circuit simulation software, superconducting circuit, etc.) that can be employed by lexicon transforming system102and components thereof to execute one or more operations in accordance with one or more of the embodiments described herein.

In order to provide a context for the various aspects of the disclosed subject matter,FIG.8as well as the following discussion are intended to provide a general description of a suitable environment in which the various aspects of the disclosed subject matter can be implemented.FIG.8illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity.

With reference toFIG.8, a suitable operating environment800for implementing various aspects of this disclosure can also include a computer812. The computer812can also include a processing unit814, a system memory816, and a system bus818. The system bus818couples system components including, but not limited to, the system memory816to the processing unit814. The processing unit814can be any of various available processors. Dual microprocessors and other multiprocessor architectures also can be employed as the processing unit814. The system bus818can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and a local bus using any variety of available bus architectures including, but not limited to, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus (USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and Small Computer Systems Interface (SCSI).

The system memory816can also include volatile memory820and nonvolatile memory822. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer812, such as during start-up, is stored in nonvolatile memory822. Computer812can also include removable/non-removable, volatile/non-volatile computer storage media.FIG.8illustrates, for example, a disk storage824. Disk storage824can also include, but is not limited to, devices like a magnetic disk drive, floppy disk drive, tape drive, Jaz drive, Zip drive, LS-80 drive, flash memory card, or memory stick. The disk storage824also can include storage media separately or in combination with other storage media. To facilitate connection of the disk storage824to the system bus818, a removable or non-removable interface is typically used, such as interface826.FIG.8also depicts software that acts as an intermediary between users and the basic computer resources described in the suitable operating environment800. Such software can also include, for example, an operating system828. Operating system828, which can be stored on disk storage824, acts to control and allocate resources of the computer812.

System applications830take advantage of the management of resources by operating system828through program modules832and program data834, e.g., stored either in system memory816or on disk storage824. It is to be appreciated that this disclosure can be implemented with various operating systems or combinations of operating systems. A user enters commands or information into the computer812through input device(s)836. Input devices836include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, and the like. These and other input devices connect to the processing unit814through the system bus818via interface port(s)838. Interface port(s)838include, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). Output device(s)840use some of the same type of ports as input device(s)836. Thus, for example, a USB port can be used to provide input to computer812, and to output information from computer812to an output device840. Output adapter842is provided to illustrate that there are some output devices840like monitors, speakers, and printers, among other output devices840, which require special adapters. The output adapters842include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device840and the system bus818. It should be noted that other devices and systems of devices provide both input and output capabilities such as remote computer(s)844.

Computer812can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s)844. The remote computer(s)844can be a computer, a server, a router, a network PC, a workstation, a microprocessor based appliance, a peer device or other common network node and the like, and typically can also include many or all of the elements described relative to computer812. For purposes of brevity, only a memory storage device846is illustrated with remote computer(s)844. Remote computer(s)844is logically connected to computer812through a network interface848and then physically connected via communication connection850. Network interface848encompasses wire and wireless communication networks such as local-area networks (LAN), wide-area networks (WAN), cellular networks, etc. LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL). Communication connection(s)850refers to the hardware/software employed to connect the network interface848to the system bus818. While communication connection850is shown for illustrative clarity inside computer812, it can also be external to computer812. The hardware/software for connection to the network interface848can also include, for exemplary purposes only, internal and external technologies such as, modems including regular telephone grade modems, cable modems and DSL modems, ISDN adapters, and Ethernet cards.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

As shown, cloud computing environment950includes one or more cloud computing nodes910with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone954A, desktop computer954B, laptop computer954C, and automobile computer system954N may communicate. Although not illustrated inFIG.9, cloud computing nodes910can further comprise a quantum platform (e.g., quantum computer, quantum hardware, quantum software, etc.) with which local computing devices used by cloud consumers can communicate. Nodes910may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment950to offer infrastructure, platforms and software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices954A-N shown inFIG.9are intended to be illustrative only and that computing nodes910and cloud computing environment950can communicate with any type of computerized device over any type of network and network addressable connection (e.g., using a web browser).

Hardware and software layer1060includes hardware and software components. Examples of hardware components include: mainframes1061; RISC (Reduced Instruction Set Computer) architecture based servers1062; servers1063; blade servers1064; storage devices1065; and networks and networking components1066. In some embodiments, software components include network application server software1067, database software1068, quantum platform routing software (not illustrated inFIG.10), and quantum software (not illustrated inFIG.10).

Virtualization layer1070provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers1071; virtual storage1072; virtual networks1073, including virtual private networks; virtual applications and operating systems1074; and virtual clients1075.

Workloads layer1090provides examples of functionality for which the cloud computing environment may be utilized. Non-limiting examples of workloads and functions which may be provided from this layer include: mapping and navigation1091; software development and lifecycle management1092; virtual classroom education delivery1093; data analytics processing1094; transaction processing1095; and quantum state measurement logic software1096.

While the subject matter has been described above in the general context of computer-executable instructions of a computer program product that runs on a computer and computers, those skilled in the art will recognize that this disclosure also can or can be implemented in combination with other program modules. Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks and implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive computer-implemented methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, mini-computing devices, mainframe computers, as well as computers, hand-held computing devices (e.g., PDA, phone), microprocessor-based or programmable consumer or industrial electronics, and the like. The illustrated aspects can also be practiced in distributed computing environments in which tasks are performed by remote processing devices that are linked through a communications network. However, some, if not all aspects of this disclosure can be practiced on stand-alone computers. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. For example, in one or more embodiments, computer executable components can be executed from memory that can include or be comprised of one or more distributed memory units. As used herein, the term “memory” and “memory unit” are interchangeable. Further, one or more embodiments described herein can execute code of the computer executable components in a distributed manner, e.g., multiple processors combining or working cooperatively to execute code from one or more distributed memory units. As used herein, the term “memory” can encompass a single memory or memory unit at one location or multiple memories or memory units at one or more locations.