Skip to content
wiki.fftac.org

Prophecy Knowledge Graph Design - Source Excerpt 07

Back to Prophecy Knowledge Graph Design

Summary

This source excerpt preserves a bounded section of Antichrist.net/agent-file-handoff/Archive/2026-05-13-content-user-seo/Improvement/Prophecy Knowledge Graph Design.md so readers can inspect the evidence without opening the full source file.

**Source path:** Antichrist.net/agent-file-handoff/Archive/2026-05-13-content-user-seo/Improvement/Prophecy Knowledge Graph Design.md

27. Large Language Models for Metaphor Detection: Bhagavad Gita and Sermon on the Mount \- IEEE Xplore, accessed May 12, 2026, [https://ieeexplore.ieee.org/iel8/6287639/10380310/10551839.pdf](https://ieeexplore.ieee.org/iel8/6287639/10380310/10551839.pdf)  
28. Applying Additional Auxiliary Context Using Large Language Model for Metaphor Detection, accessed May 12, 2026, [https://www.mdpi.com/2504-2289/9/9/218](https://www.mdpi.com/2504-2289/9/9/218)  
29. NLP Datasets for Idiom and Figurative Language Tasks \- arXiv, accessed May 12, 2026, [https://arxiv.org/html/2511.16345](https://arxiv.org/html/2511.16345)  
30. Unveiling LLMs' Metaphorical Understanding: Exploring Conceptual Irrelevance, Context Leveraging and Syntactic Influence \- arXiv, accessed May 12, 2026, [https://arxiv.org/html/2510.04120v1](https://arxiv.org/html/2510.04120v1)  
31. Interpretable Chinese Metaphor Identification via LLM-Assisted MIPVU Rule Script Generation: A Comparative Protocol Study \- arXiv, accessed May 12, 2026, [https://arxiv.org/html/2603.10784v1](https://arxiv.org/html/2603.10784v1)  
32. A Dataset for Metaphor Detection in Early Medieval Hebrew Poetry \- ACL Anthology, accessed May 12, 2026, [https://aclanthology.org/2024.eacl-short.39.pdf](https://aclanthology.org/2024.eacl-short.39.pdf)  
33. Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024\) \- ACL Anthology, accessed May 12, 2026, [https://aclanthology.org/2024.ml4al-1.0.pdf](https://aclanthology.org/2024.ml4al-1.0.pdf)  
34. Extracting Relations from Ecclesiastical Cultural Heritage Texts ..., accessed May 12, 2026, [https://aclanthology.org/2024.nlp4dh-1.5/](https://aclanthology.org/2024.nlp4dh-1.5/)  
35. Concept Drift Guided LayerNorm Tuning for Efficient Multimodal Metaphor Identification, accessed May 12, 2026, [https://arxiv.org/html/2505.11237v4](https://arxiv.org/html/2505.11237v4)  
36. Event Detection between Literary Studies and NLP. A Survey, a Narratological Reflection, and a Case Study, accessed May 12, 2026, [https://jcls.io/article/id/4215/](https://jcls.io/article/id/4215/)  
37. The Future is not One-dimensional: Complex Event ... \- ACL Anthology, accessed May 12, 2026, [https://aclanthology.org/2021.emnlp-main.422.pdf](https://aclanthology.org/2021.emnlp-main.422.pdf)  
38. Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction \- ACL Anthology, accessed May 12, 2026, [https://aclanthology.org/2023.eacl-main.39.pdf](https://aclanthology.org/2023.eacl-main.39.pdf)  
39. SciNER: Extracting Named Entities from Scientific Literature \- PMC, accessed May 12, 2026, [https://pmc.ncbi.nlm.nih.gov/articles/PMC7302801/](https://pmc.ncbi.nlm.nih.gov/articles/PMC7302801/)  
40. Sudden Event Prediction Based on Event Knowledge Graph \- MDPI, accessed May 12, 2026, [https://www.mdpi.com/2076-3417/12/21/11195](https://www.mdpi.com/2076-3417/12/21/11195)  
41. Towards Event Prediction in Temporal Graphs \- VLDB Endowment, accessed May 12, 2026, [https://www.vldb.org/pvldb/vol15/p1861-tian.pdf](https://www.vldb.org/pvldb/vol15/p1861-tian.pdf)  
42. Evidence on good forecasting practices from the Good Judgment Project \- AI Impacts, accessed May 12, 2026, [https://aiimpacts.org/evidence-on-good-forecasting-practices-from-the-good-judgment-project/](https://aiimpacts.org/evidence-on-good-forecasting-practices-from-the-good-judgment-project/)  
43. What I've Learned from the Good Judgment Project \- SOA, accessed May 12, 2026, [https://www.soa.org/globalassets/assets/Library/Newsletters/Forecasting-Futurism/2015/July/ffn-2015-iss11-campbell.pdf](https://www.soa.org/globalassets/assets/Library/Newsletters/Forecasting-Futurism/2015/July/ffn-2015-iss11-campbell.pdf)  
44. Compromising improves forecasting \- PMC \- NIH, accessed May 12, 2026, [https://pmc.ncbi.nlm.nih.gov/articles/PMC10189590/](https://pmc.ncbi.nlm.nih.gov/articles/PMC10189590/)  
45. \[2603.05575\] Prediction-Powered Conditional Inference \- arXiv, accessed May 12, 2026, [https://arxiv.org/abs/2603.05575](https://arxiv.org/abs/2603.05575)  
46. Self-Correcting Knowledge Graphs with Neo4j and LLMs | by Akash Choudhuri \- Medium, accessed May 12, 2026, [https://medium.com/globant/self-correcting-knowledge-graphs-with-neo4j-and-llms-35fd36f31ec8](https://medium.com/globant/self-correcting-knowledge-graphs-with-neo4j-and-llms-35fd36f31ec8)  
47. Explaining Misleading Claims Using Graphs of Entities \- ESWC 2024, accessed May 12, 2026, [https://2024.eswc-conferences.org/wp-content/uploads/2024/05/77770426.pdf](https://2024.eswc-conferences.org/wp-content/uploads/2024/05/77770426.pdf)  
48. Fact Checking in Knowledge Graphs by Logical Consistency \- Semantic Web Journal, accessed May 12, 2026, [https://www.semantic-web-journal.net/content/fact-checking-knowledge-graphs-logical-consistency](https://www.semantic-web-journal.net/content/fact-checking-knowledge-graphs-logical-consistency)  
49. What If: Causal Analysis with Graph Databases \- VLDB Endowment, accessed May 12, 2026, [https://www.vldb.org/pvldb/vol18/p4009-pachera.pdf](https://www.vldb.org/pvldb/vol18/p4009-pachera.pdf)  
50. An Ontology Design Pattern for Representing Causality \- Scholar Commons, accessed May 12, 2026, [https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1615\&context=aii\_fac\_pub](https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1615&context=aii_fac_pub)  
51. When accurate prediction models yield harmful self-fulfilling prophecies \- PMC \- NIH, accessed May 12, 2026, [https://pmc.ncbi.nlm.nih.gov/articles/PMC12010445/](https://pmc.ncbi.nlm.nih.gov/articles/PMC12010445/)  
52. Choosing a Database: AWS Neptune, Neo4J, ArangoDB, or Redis, accessed May 12, 2026, [https://cycode.com/blog/aws-neptune-neo4j-arangodb-or-redisgraph-how-we-chose-our-graph-database/](https://cycode.com/blog/aws-neptune-neo4j-arangodb-or-redisgraph-how-we-chose-our-graph-database/)  
53. Operational differences between Neptune and Neo4j \- AWS Documentation, accessed May 12, 2026, [https://docs.aws.amazon.com/neptune/latest/userguide/migration-operational-differences.html](https://docs.aws.amazon.com/neptune/latest/userguide/migration-operational-differences.html)  
54. Neo4j vs ArangoDB for high volume-ingest \+ multi-hop traversal use case? \- Reddit, accessed May 12, 2026, [https://www.reddit.com/r/Database/comments/1sfr4sh/neo4j\_vs\_arangodb\_for\_high\_volumeingest\_multihop/](https://www.reddit.com/r/Database/comments/1sfr4sh/neo4j_vs_arangodb_for_high_volumeingest_multihop/)  
55. Comparison of MongoDB, Neo4j and ArangoDB databases using the developed data generator for NoSQL databases \- Czasopisma, accessed May 12, 2026, [https://www.czasopisma.uws.edu.pl/studiainformatica/article/download/3095/2803](https://www.czasopisma.uws.edu.pl/studiainformatica/article/download/3095/2803)  
56. Architectural differences between Neptune and Neo4j \- AWS Documentation, accessed May 12, 2026, [https://docs.aws.amazon.com/neptune/latest/userguide/migration-architectural-differences.html](https://docs.aws.amazon.com/neptune/latest/userguide/migration-architectural-differences.html)  
57. Comparision of AWS Neptune vs Neo4j | by Kavitha Reddy \- Medium, accessed May 12, 2026, [https://medium.com/@kavithareddy.gade/comparision-of-aws-neptune-vs-neo4j-e9110a827057](https://medium.com/@kavithareddy.gade/comparision-of-aws-neptune-vs-neo4j-e9110a827057)  
58. ArangoDB vs Neo4j : Key Differences & Comparison \- PuppyGraph, accessed May 12, 2026, [https://www.puppygraph.com/blog/arangodb-vs-neo4j](https://www.puppygraph.com/blog/arangodb-vs-neo4j)  
59. Graph Data Structures in ArangoDB and Usage with .NET | by Ramazan Gunes \- Medium, accessed May 12, 2026, [https://gunesramazan.medium.com/graph-data-structures-in-arangodb-and-usage-with-net-e615bded31b7](https://gunesramazan.medium.com/graph-data-structures-in-arangodb-and-usage-with-net-e615bded31b7)  
60. ArangoDB v3.3.10 AQL Documentation, accessed May 12, 2026, [https://download.arangodb.com/arangodb33/doc/ArangoDB\_AQL\_3.3.10.pdf](https://download.arangodb.com/arangodb33/doc/ArangoDB_AQL_3.3.10.pdf)  
61. Build applications with Neo4j and Python \- Neo4j Python Driver Manual, accessed May 12, 2026, [https://neo4j.com/docs/python-manual/current/](https://neo4j.com/docs/python-manual/current/)  
62. Representing State Machine Capabilities using neo4j / graph databases | Scattered Code, accessed May 12, 2026, [https://scatteredcode.net/representing-state-machine-capabilities-using-neo4j-graph-databases/](https://scatteredcode.net/representing-state-machine-capabilities-using-neo4j-graph-databases/)  
63. Video: 066 Playing With State Machines \- NODES2022 \- Pierre Halftermeyer \- Neo4j, accessed May 12, 2026, [https://neo4j.com/videos/066-playing-with-state-machines-nodes2022-pierre-halftermeyer/](https://neo4j.com/videos/066-playing-with-state-machines-nodes2022-pierre-halftermeyer/)