mlsanityData to 7 Oct 2026

Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences

arXiv 2204.04888 ↗First posted 11 Apr 2022cs.DL cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

In recent years, with the continuous progress of science and technology, the number of scientific research achievements has increased rapidly. As an exchange platform and medium for scientific research achievements, scientific and technological academic conferences have become increasingly abundant. The convening of academic conferences brings large numbers of papers, researchers, institutions, projects, and research topics, but massive conference data also makes it difficult for researchers to obtain valuable information efficiently. It is therefore meaningful to use deep learning, knowledge graph technology, semantic similarity calculation, and portrait modeling to mine core information from conference data. This paper reviews the key technologies for constructing knowledge graphs and accurate portraits of scientific and technological academic conferences, including named entity recognition, semantic text similarity, trend prediction, graph storage, search engines, and visualization components. These techniques jointly support the construction of conference knowledge services that help researchers acquire scientific information more quickly.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 95 references that are in the map. First posted on arXiv 11 Apr 2022.

Builds on

22 of 95 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  7. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  8. 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
  9. 2022Filter-enhanced MLP is All You Need for Sequential Recommendationcited by 96
  10. 2018Chinese NER Using Lattice LSTMcited by 81
  11. 2019TENER: Adapting Transformer Encoder for Named Entity Recognitioncited by 69
  12. 2020FLAT: Chinese NER Using Flat-Lattice Transformercited by 67
10 more
  1. 2021Self-Supervised Graph Co-Training for Session-based Recommendationcited by 43
  2. 2017Fast and Accurate Entity Recognition with Iterated Dilated Convolutionscited by 40
  3. 2021Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognitioncited by 39
  4. 2017Natural Language Inference over Interaction Spacecited by 34
  5. 2018Forecasting Economics and Financial Time Series: ARIMA vs. LSTMcited by 33
  6. 2022Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrievalcited by 33
  7. 2018Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Informationcited by 28
  8. 2014Semantic Modelling with Long-Short-Term Memory for Information Retrievalcited by 17
  9. 2018Predicting the Semantic Textual Similarity with Siamese CNN and LSTMcited by 14
  10. 2019A Novel Approach for Automatic Bengali Question Answering System using Semantic Similarity Analysiscited by 2

Built on by

Not cited yet.