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1. WO2020205047 - QUERYING KNOWLEDGE GRAPH WITH NATURAL LANGUAGE INPUT

Publication Number WO/2020/205047
Publication Date 08.10.2020
International Application No. PCT/US2020/016306
International Filing Date 03.02.2020
IPC
G06F 16/9032 2019.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
903Querying
9032Query formulation
G06F 16/901 2019.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
901Indexing; Data structures therefor; Storage structures
CPC
G06F 16/24522
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
20of structured data, e.g. relational data
24Querying
245Query processing
2452Query translation
24522Translation of natural language queries to structured queries
G06F 16/9024
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
901Indexing; Data structures therefor; Storage structures
9024Graphs; Linked lists
G06F 16/90332
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
903Querying
9032Query formulation
90332Natural language query formulation or dialogue systems
G06F 40/284
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
40Handling natural language data
20Natural language analysis
279Recognition of textual entities
284Lexical analysis, e.g. tokenisation or collocates
G06F 40/295
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
40Handling natural language data
20Natural language analysis
279Recognition of textual entities
289Phrasal analysis, e.g. finite state techniques or chunking
295Named entity recognition
G06F 40/30
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
FELECTRIC DIGITAL DATA PROCESSING
40Handling natural language data
30Semantic analysis
Applicants
  • MICROSOFT TECHNOLOGY LICENSING, LLC [US]/[US]
Inventors
  • YAN, Rui
  • DENG, Yonggang
  • CHAI, Junyi
  • GUAN, Maochen
  • HE, Yujie
  • LI, Bing
Agents
  • MINHAS, Sandip S.
  • ADJEMIAN, Monica
  • BARKER, Doug
  • CHATTERJEE, Aaron C.
  • CHEN, Wei-Chen Nicholas
  • CHOI, Daniel
  • CHURNA, Timothy
  • DINH, Phong
  • EVANS, Patrick
  • GABRYJELSKI, Henry
  • GUPTA, Anand
  • HINOJOSA-SMITH, Brianna L.
  • HWANG, William C.
  • JARDINE, John S.
  • LEE, Sunah
  • LEMMON, Marcus
  • MARQUIS, Thomas
  • MEYERS, Jessica
  • ROPER, Brandon
  • SPELLMAN, Steven
  • SULLIVAN, Kevin
  • SWAIN, Cassandra T.
  • WALKER, Matt
  • WIGHT, Stephen A.
  • WISDOM, Gregg
  • WONG, Ellen
  • WONG, Thomas S.
  • ZHANG, Hannah
  • TRAN, Kimberly
Priority Data
16/370,72129.03.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) QUERYING KNOWLEDGE GRAPH WITH NATURAL LANGUAGE INPUT
(FR) INTERROGATION D'UN GRAPHE DE CONNAISSANCES PAR UNE ENTRÉE EN LANGAGE NATUREL
Abstract
(EN)
A server computing device, including memory storing a knowledge graph. The server computing device may further include a processor configured to receive a natural language input and generate a tokenized utterance based on the natural language input. Based on the tokenized utterance, the processor may select a predefined intention indicating a target ontology entity type of the natural language input. The processor may identify at least one input ontology entity token included in the tokenized utterance and may identify at least one relation between the predefined intention and the input ontology entity token. Based on the predefined intention, the at least one input ontology entity token, and the relation, the processor may generate a structured query. Based on the structured query and the knowledge graph, the processor may output an output ontology entity token having the target ontology entity type.
(FR)
L'invention concerne un dispositif informatique de serveur, comprenant une mémoire stockant un graphe de connaissances. Le dispositif informatique serveur peut en outre comprendre un processeur, configuré pour recevoir une entrée en langage naturel et pour générer un énoncé en unités lexicales, en fonction de l'entrée en langage naturel. En fonction de l'énoncé en unités lexicales, le processeur peut sélectionner une intention prédéfinie indiquant un type d'entité ontologique cible de l'entrée en langage naturel. Le processeur peut identifier au moins une unité lexicale d'entité ontologique d'entrée incluse dans l'énoncé en unités lexicales et peut identifier au moins une relation entre l'intention prédéfinie et l'unité lexicale d'entité ontologique d'entrée. En fonction de l'intention prédéfinie, desdites unités lexicales d'entité ontologique d'entrée et de la relation, le processeur peut générer une interrogation structurée. En fonction de l'interrogation structurée et du graphe de connaissances, le processeur peut produire une unité lexicale d'entité ontologique de sortie présentant le type cible d'entité ontologique.
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