Knowledge Extraction from Task Narratives
Knowledge Extraction from Task Narratives
复制标题
DOI：
10.1145/3134230.3134234
复制
发表时间：
2017-09
期刊：
影响因子：
--
通讯作者：
Kristina Yordanova;Carlos Monserrat Aranda;David Nieves;J. Hernández-Orallo
中科院分区：
文献类型：
--
作者：
Kristina Yordanova;Carlos Monserrat Aranda;David Nieves;J. Hernández-Orallo
活动识别的主要困难之一是缺乏一个可以识别活动和事件的世界模型。当领域是固定的和重复的，我们可以使用某种本体或一组约束手动包括这些信息。然而，在许多情况下，有许多新的情况下，只有一些知识是共同的，许多其他领域特定的关系必须推断。人类能够通过自然语言的简短描述来做到这一点，描述场景或要执行的特定任务。在本文中，我们应用一种工具，从自然语言描述中提取的情况模型和规则的一系列练习中的外科领域，在其中，我们要确定的事件序列是不可能的，那些是可能的（但不正确的根据演习）和那些对应的练习或计划表示的描述在自然语言中。初步结果表明，大量有价值的知识可以自动提取，这可以用来表达领域知识和练习描述的语言，如事件演算，可以帮助桥梁这些高层次的描述与低层次的事件，从视频中识别。
One of the major difficulties in activity recognition stems from the lack of a model of the world where activities and events are to be recognised. When the domain is fixed and repetitive we can manually include this information using some kind of ontology or set of constraints. On many occasions, however, there are many new situations for which only some knowledge is common and many other domain-specific relations have to be inferred. Humans are able to do this from short descriptions in natural language, describing the scene or the particular task to be performed. In this paper we apply a tool that extracts situation models and rules from natural language description to a series of exercises in a surgical domain, in which we want to identify the sequence of events that are not possible, those that are possible (but incorrect according to the exercise) and those that correspond to the exercise or plan expressed by the description in natural language. The preliminary results show that a large amount of valuable knowledge can be extracted automatically, which could be used to express domain knowledge and exercises description in languages such as event calculus that could help bridge these high-level descriptions with the low-level events that are recognised from videos.