Results 41 to 50 of about 2,563,850 (352)
OEQA: Knowledge- and Intention-Driven Intelligent Ocean Engineering Question-Answering Framework
Applied Sciences, 2023 The constantly updating big data in the ocean engineering domain has challenged the traditional manner of manually extracting knowledge, thereby underscoring the current absence of a knowledge graph framework in such a special field.
Rui Zhu +4 more
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IEEE Access, 2023 We propose a novel approach that uses semi-supervised learning to extract triplets from domain-specific texts and create a Knowledge Graph (KG), with a focus on the agricultural domain.
G. Veena, Deepa Gupta, Vani Kanjirangat
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Named Entity Extraction for Knowledge Graphs: A Literature Overview [PDF]
IEEE Access, 2020 An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts.
Tareq Al-Moslmi +3 more
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Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution [PDF]
ACM Transactions on Knowledge Discovery from Data, 2021 Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for
Fuxian Li +5 more
semanticscholar +1 more source
Graph-based methods for Author Name Disambiguation: a survey
PeerJ Computer Science, 2023 Scholarly knowledge graphs (SKG) are knowledge graphs representing research-related information, powering discovery and statistics about research impact and trends. Author name disambiguation (AND) is required to produce high-quality SKGs, as a disambiguated set of authors is fundamental to ensure a coherent view of researchers’ activity.
De Bonis M, Falchi F, Manghi P
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Stack graphs: Name resolution at scale
, 2022 We present stack graphs, an extension of Visser et al.'s scope graphs framework. Stack graphs power Precise Code Navigation at GitHub, allowing users to navigate name binding references both within and across repositories. Like scope graphs, stack graphs encode the name binding information about a program in a graph structure, in which paths represent ...
Creager, Douglas A. +1 more
openaire +3 more sources
Cross-domain Named Entity Recognition via Graph Matching [PDF]
FindingsCross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains.
Junhao Zheng, Haibin Chen, Qianli Ma
semanticscholar +1 more source
Knowledge-Graph Augmented Word Representations for Named Entity Recognition
AAAI Conference on Artificial Intelligence, 2020 By modeling the context information, ELMo and BERT have successfully improved the state-of-the-art of word representation, and demonstrated their effectiveness on the Named Entity Recognition task.
Q. He, Liang Wu, Yida Yin, Heming Cai
semanticscholar +1 more source
KGAT: Knowledge Graph Attention Network for Recommendation [PDF]
Knowledge Discovery and Data Mining, 2019 To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
Xiang Wang +4 more
semanticscholar +1 more source
On irregularity descriptors of derived graphs
AIMS Mathematics, 2020 Topological indices are molecular structural descriptors which computationally and theoretically describe the natures of the underlying connectivity of nanomaterials and chemical compounds, and hence they provide quicker methods to examine their ...
Wei Gao +4 more
doaj +1 more source