WO2020162884A1 - Système de suggestion de paramètre - Google Patents

Système de suggestion de paramètre Download PDF

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Publication number
WO2020162884A1
WO2020162884A1 PCT/US2019/016640 US2019016640W WO2020162884A1 WO 2020162884 A1 WO2020162884 A1 WO 2020162884A1 US 2019016640 W US2019016640 W US 2019016640W WO 2020162884 A1 WO2020162884 A1 WO 2020162884A1
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WO
WIPO (PCT)
Prior art keywords
knowledge graph
computer
design
knowledge
pattern
Prior art date
Application number
PCT/US2019/016640
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English (en)
Inventor
Thomas Gruenewald
Suraj Ravi MUSUVATHY
Sanjeev SRIVASTAVA
Lucia MIRABELLA
Livio Dalloro
Original Assignee
Siemens Aktiengesellschaft
Siemens Corporation
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Publication date
Application filed by Siemens Aktiengesellschaft, Siemens Corporation filed Critical Siemens Aktiengesellschaft
Priority to PCT/US2019/016640 priority Critical patent/WO2020162884A1/fr
Publication of WO2020162884A1 publication Critical patent/WO2020162884A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/101—Collaborative creation, e.g. joint development of products or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/451—Execution arrangements for user interfaces
    • G06F9/453—Help systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present disclosure is directed, in general, to a system and method for automatically selecting design parameters during an engineering design process, and more specifically to such a system that monitors inputs provided by a designer to predict the needed design parameters.
  • a design may encompass a part design, a system design, a manufacturing process design or other designs typically prepared by engineers. Usually it takes many iterations until the correct parameters are set. In the case of a system design, it takes even more time since higher complexity systems often include many different specialties (e.g., mechanical, electrical, materials, structural, etc.).
  • a system for aiding in the design of an engineered product includes a computer including a processor and a readable storage media having computer-executable instructions including at least one engineering program.
  • An observer application is associated with the engineering program and is operable to monitor inputs to the computer made by a user to identify an observed pattern
  • a knowledge graph includes data related to the engineered product
  • an explorer application searches the knowledge graph to find a stored pattern that corresponds to the observed pattern
  • An insighter application is operable to identify and present a suggested parameter to the user based on the stored pattern.
  • a method of aiding in the design of an engineered product includes monitoring with a computer, inputs to the computer made by a user, identifying an observed pattern to the inputs, applying the inputs to a knowledge graph to expand the content of the knowledge graph, and searching the knowledge graph to find a stored pattern that corresponds to the observed pattern.
  • the method further includes applying the stored pattern to present a predicted parameter to the user, the predicted parameter corresponding to a subsequent step in the design process.
  • a non-transitory computer readable storage media having computer-executable instructions, when executed by a processor in a computer, performs a method for the design of an engineered product, the instructions include monitoring inputs to the computer made by a user, identifying an observed pattern to the inputs, applying the inputs to a knowledge graph to expand or refine the content of the knowledge graph, searching the knowledge graph to find a stored pattern that corresponds to the observed pattern, and applying the stored pattern to present a predicted parameter to the user, the predicted parameter corresponding to a subsequent step in the design process.
  • FIG. 1 is a schematic illustration of a design process for an engineered product.
  • Fig. 2 is a schematic illustration of potential inputs to be considered during the design process of Fig. 1.
  • FIG. 3 is a schematic illustration of a design process for an engineered product including a computer-implemented enhanced design system.
  • FIG. 4 is another more detailed schematic illustration of the computer-implemented enhanced design system of Fig. 3.
  • FIG. 5 is another more detailed schematic illustration of the computer-implemented enhanced design system of Fig. 3.
  • Fig. 6 is an enlarged and more detailed portion of schematic illustration of Fig. 5.
  • Fig. 7 is a detailed schematic of a distiller for use in the system illustrated in Fig. 4.
  • Fig. 8 is a detailed schematic of a discover phase of the design process of Fig. 3.
  • Fig. 9 is a schematic illustration of a portion of a knowledge graph.
  • Fig. 10 is an enlarged schematic illustration of a portion of the knowledge graph of Fig.
  • Fig. 11 is a schematic illustration of another arrangement of the knowledge graph.
  • Fig. 12 is a schematic illustration of a prediction model for the enhanced design system of Fig. 3.
  • Fig. 13 is a circuit diagram for an example operational amplifier and filter.
  • Fig. 14 is a schematic illustration of the design process for the circuit of Fig. 13.
  • Fig. 15 illustrates a recommendation from an insighter made during the design of the circuit of Fig. 13.
  • first, second, third and so forth may be used herein to refer to various elements, information, functions, or acts, these elements, information, functions, or acts should not be limited by these terms. Rather these numeral adjectives are used to distinguish different elements, information, functions or acts from each other. For example, a first element, information, function, or act could be termed a second element, information, function, or act, and, similarly, a second element, information, function, or act could be termed a first element, information, function, or act, without departing from the scope of the present disclosure.
  • adjacent to may mean: that an element is relatively near to but not in contact with a further element; or that the element is in contact with the further portion, unless the context clearly indicates otherwise.
  • phrase“based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.
  • Terms“about” or“substantially” or like terms are intended to cover variations in a value that are within normal industry manufacturing tolerances for that dimension. If no industry standard as available a variation of twenty percent would fall within the meaning of these terms unless otherwise stated.
  • Fig. 1 schematically illustrates the typical design process 10 as including a Define phase 15 in which the problem to be solved and the initial parameters of the design are defined, a Create phase 20 in which the design is refined, and additional parameters are defined, and an Evaluate phase 25 in which the completed design is evaluated and tested. During or after the Evaluate phase 25 there are often iterations 27 in which initial or subsequent parameters must be changed or adjusted. Often, this reverts the design process back to the Define phase 15 or the Create phase 20 This process continues until the designer arrives at a final design that satisfies the initial requirements.
  • Phase 15 Portion of the overall design process in which the requirements, goals, operational characteristics and the like are defined.
  • Capture Phase 30 - Process that runs in an enhanced design system 35 to capture inputs provided by the designer during the design process.
  • the inputs are made using various software tools including but not limited to 3D modeling software, CAD software, spreadsheets, Internet searches, etc. as well as other inputs.
  • Knowledge Graph sometimes referred to as Digital Twin Graph 55 - Database including accumulated knowledge related to the particular design process to which the graph is associated.
  • Each knowledge graph 55 includes nodes 60 and connections 65 between the nodes 60.
  • Neural Network 70 An arrangement of data based on a collection of connected units or nodes 60 that are analogous to artificial neurons, which loosely model the neurons in a biological brain.
  • Connections 65 Links between different nodes 60.
  • the connections 65 are logical links between related pieces of data.
  • Digital Twin 75 A fully operational model of a component or system that includes the component being designed.
  • Observer Programs 80 Add-ins or plug-ins for pre-existing engineering tools that gather information from the designers as they work. Information such as keystrokes, drawing values, log files, engineering calculations, and the like can be gathered by observer programs 80.
  • Insighter Programs 95 Programs that use patterns gathered from observer 80 and distiller programs 85 and artificial intelligence approaches to predict the next step or other options in the design process and to present those options to the designer.
  • Causal Explorer 105 Records the evolution of the design workflows in the digital twin graph 55 and identifies causal links or connections 65 between nodes 60 to identify recurring design and engineering practices.
  • Past Designs Explorer 110 Compares the design workflow with previous design workflows stored in the digital twin graph 55.
  • Recommendation Generator 115 Program that delivers possible alternative designs or other choices to the designer during the design process.
  • System Vision Server 120 Server of a free cloud-based simulation tool to design and simulate complete analog, digital, mixed signal and electro-mechanical systems.
  • MDN Mixture Density Network 125 - A class of models obtained by combining a conventional neural network with a mixture density model.
  • the mixture density model represents the conditional probability density function of the target variables conditioned on the input vector of the neural network.
  • Fig. 2 illustrates additional details that might be involved in the design process of a component or a part 130.
  • the left side of the diagram illustrates design steps such as defining requirements 135 for the design (i.e., the Define phase) and the development of a system model 140 and the system architecture (i.e., the Create phase).
  • a 3D design 145 is completed and evaluated with iteration steps possible back to any of the prior steps.
  • assembly and component models can be created or completed.
  • the right side of the schematic of Fig. 2 illustrates other design inputs or considerations that can complicate the design process. For example, some designs may require the designer to consider the supply chain 150 for the part or parts, the production schedule 155 for certain parts, and the
  • manufacturability 160 of the final design All of these are considered by the designer. However, these considerations often arise late in the design process and require additional iterations that further slow the design.
  • Figs. 3-11 illustrate a computer-implemented enhanced design system 35 that utilizes advanced artificial intelligence (AI) to enhance the design process just described in an effort to reduce wasted time, increase engineering productivity, and produce superior quality designs.
  • AI advanced artificial intelligence
  • the software aspects of the present invention could be stored on virtually any computer readable medium including a local disk drive system, a remote server, internet, or cloud-based storage location. In addition, aspects could be stored on portable devices or memory devices as may be required.
  • the computer generally includes an input/output device that allows for access to the software regardless of where it is stored, one or more processors, memory devices, user input devices, and output devices such as monitors, printers, and the like.
  • the processor could include a standard micro-processor or could include artificial intelligence accelerators or processors that are specifically designed to perform artificial intelligence applications such as artificial neural networks, machine vision, and machine learning. Typical applications include algorithms for robotics, internet of things, and other data- intensive or sensor-driven tasks.
  • AI accelerators are multi-core designs and generally focus on low-precision arithmetic, novel dataflow architectures, or in-memory computing capability.
  • the processor may include a graphics processing unit (GPU) designed for the manipulation of images and the calculation of local image properties.
  • GPU graphics processing unit
  • FPGA field- programmable gate arrays
  • ASIC application-specific integrated circuits
  • the computer also includes communication devices that may allow for communication between other computers or computer networks, as well as for communication with other devices such as machine tools, work stations, actuators, controllers, sensors, and the like.
  • Fig. 3 is a simplified schematic of a portion of the enhanced engineering design process 35 that is enhanced by the design system.
  • the designer is providing inputs to the design using various software tools including but not limited to 3D modeling software, CAD software, spreadsheets, Internet searches, etc. This is illustrated as the capture phase 30.
  • the design system 35 analyzes the various inputs to determine if suggestions such as design parameters 45 could be provided to the designer to enhance the process.
  • the explore phase 40 can use simulation tools to validate any parameters before they are suggested.
  • Recommendations, provided in the discover phase 50 could include design parameters 45 or could include recommendations based on manufacturability, material selection, etc.
  • Figs. 4 and 5 illustrates the capture phase 30, the explore phase 40, and the discover phase 50 in additional detail and each interacting with the knowledge graph 55, sometimes referred to as a digital twin graph 55 and potentially one or more digital re-creations or digital twins 75 of devices or systems similar to that being designed by the designer.
  • the capture phase 30 is used to gather data from the designer as well as from other available sources.
  • an engineer or designer at work uses multiple different software tools 165 such as NX, StarCCM, etc.
  • An observer program 80, or multiple observer programs 80 monitor the inputs of the designer in these tools 165 and transmit that information to a distiller program 85.
  • observer programs 80 are add-ins or plug ins for pre-existing engineering tools 165.
  • engineering data 170 from other programs such as EXCEL, MATHCAD, and the like is gathered and transmitted to the distiller program 85.
  • Requirements of the design 175 may be stored in another location or program.
  • These requirements 175 may include size limitations, cost limitations, performance requirements and the like and are also sent to the distiller program 85.
  • components or systems similar to those being designed are in use or operation and actual operating data 180 is available. If available, this data 180 can be provided to the distiller program 85 as well.
  • the distiller program 85 reviews all the available data and reduces that data to useful pieces of information that can be provided to the knowledge graph 55. In addition, relationships between various pieces of data can be provided to the knowledge graph 55 to assure that the knowledge graph 55 contains useful and helpful information.
  • the knowledge graph 55 also contains links that might lead a designer to the next important piece of information needed in the design process.
  • the computer interacts with the knowledge graph 55, the new design, and the designer to provide alternative designs 185 and validate and rank them through analysis and simulation.
  • the computer runs an alternative generator program 90 that delivers possible alternative designs 185 or other choices to the designer during the design process.
  • the alternative generator 90 runs algorithms that generate new designs from seeds of previously generated design points and knowledge from the digital twin graph 55.
  • the computer runs quantitative analysis 190 (quants) to evaluate the design as it develops.
  • the quants 190 predict the performance of a given design, either through simulation or through learning from past experience stored in the digital twin graph 55.
  • the results of these quants 190 are delivered to the designer during the design process to further advance the design.
  • the system 35 includes forward prediction 195 in which the system 35 leams from past experience and provides suggestions for a given design parameter 45.
  • Inverse prediction 200 is also employed. Inverse prediction 200 provides alternative suggestions based on the suggestions arrived at using forward prediction 195. Finally, the system 35 performs optimization analysis to arrive at the optimum suggestion for a design parameter 45.
  • the system 35 includes an observer program 80 that observes the actions of the designer to discover the intention of the designer, design requirements, patterns, or features of the design and compares those discoveries to the knowledge graph 55.
  • An insighter program 95 uses these patterns to predict the next step or other options and presents them to the designer.
  • Figs. 4-6 also illustrate digital twin graph algorithms 100 that operate to explore past designs, build relations between the nodes 60, and improve knowledge over time.
  • the digital twin graph algorithms 100 include a causal explorer 105 and a past designs explorer 110.
  • the causal explorer 105 records the evolution of the design workflows in the digital twin graph 55 and identifies causal links to other digital twin graph nodes 60 to identify recurring design and engineering practices.
  • the past designs explorer 110 compares the design workflow with previous design workflows stored in the digital twin graph 55. Insight on analyses results performed in the past on those cases is used to inform the designer and pre- validate the designs.
  • Fig. 6 illustrates a portion of Fig. 5 and includes additional data sources for the knowledge graph.
  • the additional data includes databases or data sources 205 that may provide valuable information to the knowledge graph 55.
  • team center data 210 (data in a team-based database) is available to the knowledge graph 55.
  • Team center data 210 may include specifications and limitations that effect the design.
  • Other databases or sources of data could include online engineering resources, materials databases, engineering tables and the like.
  • Additional databases may include operating data from prior similar designs or similar devices.
  • Fig. 7 better illustrates the operation of one of the distillers 85, specifically the engineer at work distiller 85.
  • Engineers at work or designers often interact with engineering tools such as 3D modelers, CAD systems, CAM systems, and the like.
  • the distiller 85 collects this information in the form of mouse events, keyboard events, screen captures, and the like and distills that collected data into useful knowledge. Additional useful knowledge can be transferred to the knowledge graph via engineer feedback 215.
  • the useful knowledge is stored in the knowledge graph 55 along with links between related data.
  • the distiller 85 determines a design intent from the processes knowledge that can be used to generate recommendations for the designer.
  • a recommendation generator 115 may use knowledge from the knowledge graph and engineer feedback 215 to develop the recommendation.
  • the recommendation generator 115 provides recommendations to the designer using a user interface, auto completion, a script player, or other communication means.
  • the system 35 observes the actions of the designer to discover patterns or features of the design and compare those patterns or features to the knowledge graph 55.
  • Fig. 8 provides additional details of this process.
  • the observer program 80 generally provides data to the knowledge graph 55 while the insighter program 95 takes knowledge from the knowledge graph 55 and provides suggestions to the user.
  • the engineering tool is SIEMENS NX, a common CAD software provided by SIEMENS PLM and the insighter program 95 has provided a suggested design parameter 45 and asked if the designer would like to use that parameter 45.
  • the patterns and components used by the designer are searched in the knowledge graph 55 to find knowledge relating to similar patterns or components.
  • the knowledge graph 55 includes links 65 that lead to additional knowledge 60 that may be helpful to the designer and which can be passed on to the designer via the insighter program 95.
  • Fig. 9 a schematic illustration of a portion of a knowledge graph 55 is provided.
  • the knowledge graph 55 is essentially a neural network 70 including nodes 60 and connections 65.
  • Each node 60 represents a piece of knowledge with each connection 65 representing a link between different pieces of knowledge.
  • the designer may be designing a turbine blade 220 as illustrated in Fig. 10.
  • the insighter program 95 based in part on past similar products might suggest a particular material for the manufacture of the blade 220.
  • the central node 60 of Fig. 10 is identified.
  • the connections 65 between the central node 60 and a first layer of secondary nodes 60a lead the insighter program 95 to possible suggestions for the designer.
  • the secondary nodes 60a could provide information regarding the cost of the selected material, manufacturing processes that are required for the selected material, material properties of the material, and other information that might be helpful.
  • each secondary node 60a can lead to tertiary nodes 60b that include knowledge specific to the secondary nodes 60a and so on.
  • the secondary node 60a that includes the cost of the material may be connected to tertiary nodes 60b that contain information about possible material suppliers.
  • the nodes 60 can include virtually any type of knowledge including parts, available machines, manufacturing processes, machine parameters, process parameters, component parameters, relationships, sequence of events, etc.
  • the connections 65 can define a sequence or order. For example, the selection of the material may provide a connection 65 that leads to a machining process, a connection 65 from the machining process might then lead to a node 60 including a polishing process, which includes a connection 65 that leads to a node 60 that includes a quality control process.
  • Fig. 11 illustrates a different arrangement of the data within the knowledge graph 55.
  • the nodes 60 are arranged or grouped by the type of data they contain.
  • nodes 60 that relate to operational issues may be categorized and/or stored in an operation region 225.
  • Other categories could include design features 230, manufacturing features 235, process features 240, or process segment features 241, materials, suppliers, etc.
  • Connections 65 extend between nodes 60 and between groups as described before.
  • Figs. 13-15 illustrate an example of how the system 35 aids an engineer during the design of a printed circuit board 245 including a low pass filter and amplifier in which a desired gain 250 and frequency 255 is known.
  • Fig. 13 illustrates the basic circuit with the frequency 255 and gain 250 identified.
  • Components including an amplifier 257, an input resistor 260, a ground resistor 265, an amplifier bypass resistor 270, and a capacitor 275. Each of these five components must be identified and optimized to arrive at the desired frequency 255 and gain 250.
  • a machine learning approach (Mixture Density Networks - MDN - in this example) 125 is used to choose the design parameters by sampling multiple alternative designs, predicting their gain 250 and frequency 255, and then choosing the design whose gain 250 and frequency 255 are closest to the desired values.

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Abstract

L'invention concerne un système d'aide à la conception d'un produit modifié, comprenant un ordinateur comprenant un processeur et un support de stockage lisible ayant des instructions exécutables par ordinateur comprenant au moins un programme d'ingénierie. Une application d'observateur est associée au programme d'ingénierie et peut être utilisée pour surveiller des entrées dans l'ordinateur effectuées par un utilisateur pour identifier un motif observé, un graphe de connaissances comprend des données relatives au produit modifié, et une application d'exploration réalise une recherche dans le graphe de connaissances pour trouver un motif stocké qui correspond au motif observé. Une application d'indicateur est utilisable pour présenter un paramètre suggéré à l'utilisateur sur la base du motif stocké.
PCT/US2019/016640 2019-02-05 2019-02-05 Système de suggestion de paramètre WO2020162884A1 (fr)

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Cited By (4)

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Publication number Priority date Publication date Assignee Title
DE102020120027A1 (de) 2020-07-29 2022-02-03 Liebherr-Verzahntechnik Gmbh Verfahren zur automatischen Bestimmung von Konstruktionsparametern eines Greifers
US11580127B1 (en) 2018-12-21 2023-02-14 Wells Fargo Bank, N.A. User interfaces for database visualizations
US11768837B1 (en) 2021-12-28 2023-09-26 Wells Fargo Bank, N.A. Semantic entity search using vector space
US11880379B1 (en) 2022-04-28 2024-01-23 Wells Fargo Bank, N.A. Identity resolution in knowledge graph databases

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WO2018140365A1 (fr) * 2017-01-24 2018-08-02 Siemens Aktiengesellschaft Système et procédé de technologie d'ingénierie cognitive pour automatisation et commande de systèmes
WO2018183275A1 (fr) * 2017-03-27 2018-10-04 Siemens Aktiengesellschaft Système de synthèse de conception générative automatisée utilisant des données provenant d'outils de conception et des connaissances provenant d'un graphe à jumeaux numériques

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11580127B1 (en) 2018-12-21 2023-02-14 Wells Fargo Bank, N.A. User interfaces for database visualizations
US11989198B1 (en) 2018-12-21 2024-05-21 Wells Fargo Bank, N.A. User interfaces for database visualizations
DE102020120027A1 (de) 2020-07-29 2022-02-03 Liebherr-Verzahntechnik Gmbh Verfahren zur automatischen Bestimmung von Konstruktionsparametern eines Greifers
US11768837B1 (en) 2021-12-28 2023-09-26 Wells Fargo Bank, N.A. Semantic entity search using vector space
US11880379B1 (en) 2022-04-28 2024-01-23 Wells Fargo Bank, N.A. Identity resolution in knowledge graph databases

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