Yamashita 2020 - Overview
Summary
Yamashita, Kanno & Furuta (2020) propose an interactive workshop method for eliciting local causal knowledge from non-experts to build large causal networks for disaster scenario creation. The system combines a three-element causal model (cause, precondition, effect) with a GUI-based workshop procedure and NLP extraction to automate construction of a causal knowledge database.
Research Question and Contribution
Problem: Creating effective disaster drill scenarios requires predicting causal chains of damage events — but non-experts struggle to do this manually, and purely automated NLP approaches lack accuracy and local specificity.
Contribution:
- A simplified causal model (cause + precondition + effect) that is more practical than FRAM yet more expressive than simple cause-effect
- An interactive GUI-based method for workshops that elicits causal knowledge through structured questioning
- NLP techniques (Method A + B) for automatically extracting causal elements from free-text input
- Word2Vec-based deduplication to handle paraphrase variation across participants
Published: HCII 2020, LNCS 12217, pp. 437–446. DOI: 10.1007/978-3-030-50334-5_30
Paper Structure
| Section | Content |
|---|---|
| §1 Introduction | Problem motivation, two approaches to knowledge elicitation |
| §2 Causal Model | Cause-precondition-effect model; comparison to FRAM |
| §3 Method | GUI design, NLP extraction (Method A + B), duplication prevention |
| §4 Preliminary Experiment | Earthquake workshop with 2 participants; 20 events, 15 preconditions elicited |
| §5 Conclusion | Performance validation; limitations (no 3+ step chain support) |
Key Results
- NLP verification on 100 sentences: Method A identified 46 correct causal relations, Method B identified 63, combined: 87 (out of 100)
- Preliminary workshop: 20 events and 15 preconditions (countermeasures) successfully elicited and integrated into database
- Participants’ local knowledge (e.g., gas stove near carpet) successfully captured and complemented each other
Limitations
- No support for evoking multi-step causal chains (3+ events deep)
- Designed and tested for Japanese language only
- Workshop requires human facilitation
See Also
- Causal Model - Cause Precondition Effect — formal model definition
- Interactive Knowledge Elicitation Method — GUI and workshop procedure
- NLP Causal Extraction Methods — Method A, Method B, Word2Vec deduplication
- Shaposhnyk 2025 - Overview — related: LLM-based automated expert elicitation