一句话：提出基于 ML/NLP 的上下文无关方法 Kantara，从软件制品端到端重建设计决策及其理据的知识图谱。
设计决策背后的逻辑即设计理据，具有重要价值，但已有自动提取与利用技术多局限于特定上下文，且缺乏端到端管线。本文提出 Kantara，一种上下文无关的端到端理据重建方法，借助多种机器学习与自然语言处理技术，将决策及其理据表示为知识图谱，并兼顾其历史演化与可追溯性；同时提出验证机制以保证抽取信息的正确性与开发过程的一致性。作者在 Linux 内核的小型示例上进行了初步评估，结果显示出良好前景。
The logic behind design decisions, called design rationale, is very valuable. In the past, researchers have tried to automatically extract and exploit this information, but prior techniques are only applicable to specific contexts and there is insufficient progress on an end-to-end rationale information extraction pipeline. Here we outline a path towards such a pipeline that leverages several Machine Learning (ML) and Natural Language Processing (NLP) techniques. Our proposed context-independent approach, called Kantara, produces a knowledge graph representation of decisions and of their rationales, which considers their historical evolution and traceability. We also propose validation mechanisms to ensure the correctness of the extracted information and the coherence of the development process. We conducted a preliminary evaluation of our proposed approach on a small example sourced from the Linux Kernel, which shows promising results.