Natural Language Processing (NLP) – A Solution for Knowledge Extraction from Patent Unstructured Data

Souili, Achille; Cavallucci, Denis; Rousselot, François

doi:10.1016/j.proeng.2015.12.457

Cited by 23 publications

(7 citation statements)

References 8 publications

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“…Knowledge extraction from scientific and academic texts is a relatively recent task in which structured information is mined from research publications, patents, and similar texts [39,35]. The interest in this task has been also fostered by the continuous growth of the number of scientific articles available online; in some fields the growth is such that researchers trying to perform assessment of scientific literature are overwhelmed [33].…”

Section: Related Workmentioning

confidence: 99%

CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science

Dessı̀

Osborne

Recupero

et al. 2022

Lecture Notes in Computer Science

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Section: Related Workmentioning

confidence: 99%

CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science

Dessı̀

Osborne

Recupero

et al. 2022

Lecture Notes in Computer Science

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“…To find the contradictions, it is necessary to capture the expertise of the domain by questioning the experts or by extracting in canonical form the knowledge of the domain, on the use of algorithms that can be automatically or semi-automatically populate the ontology and then alleviate the work of the experts [42].…”

Section: Idm To Resolve Contradictionsmentioning

confidence: 99%

Framework for resolving problems resulting from lean integration from the early design phases of production 3D printing machine

Slim¹,

Houssin²,

Coulibaly³

et al. 2021

FME Transactions

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We focus on improving the performance of production machines (timesaving, flexibility, cost-effectiveness, safety) from the early design phase. Lean integration during the design phases might be a more appropriate solution, but this could be a source of some contradictions related to the machine performance and the usage requirements. In this paper, we propose an approach, based on Lean and Inventive Design Methodology (IDM-TRIZ), to identify and resolve these contradictions due to the Lean integration and also to provide innovative technical solutions. The approach is illustrated by the integration of the functionalities of the ''Single Minute of Exchange of Dies'' (SMED) and the ''5S'' method from the early design phase. A case study about the ''3D printer clogged nozzle'' outlines the feasibility of the proposed approach.

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“…Most of the existing methods made use of linguistic analysis. Regular expression pattern matching techniques is proposed to parse, annotate, and extract target semantic information for knowledge sharing in machine readable format OWL [7]; extracting hyponymy lexical relations is conducted on patent documents using lexicosyntactic patterns [8] and extracting knowledge combined with domain ontology from patent unstructured data [9]. Data-intensive methods are incorporating into patent claim analysis for enhancing analysis robustness combined with symbolic grammar formalisms [10].…”

Section: Related Workmentioning

confidence: 99%

Open Relation Extraction in Patent Claims with a Hybrid Network

Geng

2021

Wireless Communications and Mobile Computing

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Research on relation extraction from patent documents, a high-priority topic of natural language process in recent years, is of great significance to a series of patent downstream applications, such as patent content mining, patent retrieval, and patent knowledge base constructions. Due to lengthy sentences, crossdomain technical terms, and complex structure of patent claims, it is extremely difficult to extract open triples with traditional methods of Natural Language Processing (NLP) parsers. In this paper, we propose an Open Relation Extraction (ORE) approach with transforming relation extraction problem into sequence labeling problem in patent claims, which extract none predefined relationship triples from patent claims with a hybrid neural network architecture based on multihead attention mechanism. The hybrid neural network framework combined with Bi-LSTM and CNN is proposed to extract argument phrase features and relation phrase features simultaneously. The Bi-LSTM network gains long distance dependency features, and the CNN obtains local content feature; then, multihead attention mechanism is applied to get potential dependency relationship for time series of RNN model; the result of neural network proposed above applied to our constructed open patent relation dataset shows that our method outperforms both traditional classification algorithms of machine learning and the-state-of-art neural network classification models in the measures of Precision, Recall, and F1.

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Natural Language Processing (NLP) – A Solution for Knowledge Extraction from Patent Unstructured Data

Cited by 23 publications

References 8 publications

CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science

CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science

Framework for resolving problems resulting from lean integration from the early design phases of production 3D printing machine

Open Relation Extraction in Patent Claims with a Hybrid Network

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