Houcemeddine Turki, Abraham Toluwase Owodunni, Mohamed Ali Hadj Taieb, René Fabrice Bile, Mohamed Ben Aouicha
| Challenge: | Several literature surveys have been done to understand how open knowledge graphs are constructed, evaluated, and integrated. |
| Approach: | They analyze 4445 scholarly articles retrieved from Scopus and analyze their results to identify trends, patterns, and impact of research in this field. |
| Outcome: | The results reveal an ever-increasing number of publications on open knowledge graphs published every year, especially in developed countries (+50 per year). |
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| Challenge: | Knowledge graphs (KGs) are a representation of semantic relations between entities . despite their popularity, there is still no general understanding of what exactly a KG is or for what tasks it is applicable. |
| Approach: | They analyze 507 papers on knowledge graphs in natural language processing (NLP) they provide a taxonomy of tasks and review the maturity of individual research streams . |
| Outcome: | The findings summarize the literature and highlight directions for future work. |
Generative Knowledge Graph Construction: A Review (2022.emnlp-main)
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| Challenge: | Knowledge Graphs (KGs) are a form of structured knowledge that rely almost exclusively on human-curated structured or semi-structured data. |
| Approach: | They propose to use the sequence-to-sequence framework to build knowledge graphs. |
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Construction of the Literature Graph in Semantic Scholar (N18-3)
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Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, Oren Etzioni
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End-to-End Construction of NLP Knowledge Graph (2021.findings-acl)
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| Challenge: | a new schema for NLP knowledge about tasks, datasets and metrics is proposed. |
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ScholarGraph:a Chinese Knowledge Graph of Chinese Scholars (L18-1)
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| Challenge: | ScholarSpace integrates chinese academic information from chin scholars and science . data integration system needs to be focused on scholars, says dr. s. k. o. j. nielson . |
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Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction (2020.acl-main)
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| Challenge: | Existing methods for predicting knowledge graphs rely on the rich structure of the knowledge graph. |
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NLP Scholar: A Dataset for Examining the State of NLP Research (2020.lrec-1)
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| Challenge: | Google Scholar is the largest web search engine for academic literature and provides access to rich metadata associated with the papers. |
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Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)
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| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
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The glass ceiling in NLP (D18-1)
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| Challenge: | a glass ceiling exists within the field of NLP, but no study has examined this issue . female representation in Computer Science is lower than the average STEM field . |
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On the Gap between Adoption and Understanding in NLP (2021.findings-acl)
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| Challenge: | a recent paper argues that current publications foster a gap between adoption and understanding of models . it also makes it easier to meet publication demands with method papers, argues the paper . |
| Approach: | They argue that current NLP publication models foster a gap between adoption and understanding of models . they argue that everlarger models make it harder to explain how our methods work . |
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