A learning-by-reading framework for ontology learning

“阅读学习”挑战指的是一系列自然语言理解任务，这些任务致力于从自由文本中提取正式的概念模型，并对这些知识进行过滤和整合。在语义Web的上下文中，这个挑战通常是指从自由文本中学习领域本体的能力。当采用的方法是开放的（领域独立的）和无监督的（没有任何指导的）知识提取时，这项任务尤其具有挑战性。这种领域独立的方法对于跨各种应用程序和领域的本体学习方法的互操作性和重用至关重要。本研究旨在开发一种领域独立的方法和一套工具，用于从非结构化知识来源中学习本体层（术语、分类法、概念关系、关系层次、公理和公理图式）。特别是，我们的目标是自动学习最后三层，这在目前的技术水平上是很少做到的。研究还将探索识别重要信息的过滤技术和充分结合来自不同来源的知识的集成技术。完整本体（及其所有层）的可用性的兴趣在于，智能体对语义数据进行推理是必不可少的，这将提供有效的知识检索能力。

The learning-by-reading challenge refers to a number of natural language understanding tasks dedicated to the extraction of a formal conceptual model from free texts and to the filtering and integration of this knowledge. In the context of the Semantic Web, this challenge often refers to the ability of learning a domain ontology from free text. This task is particularly challenging when the adopted approach is an open (domain independent) and unsupervised (without any guidance) knowledge extraction. Such a domain independent approach is crucial to the interoperability and reuse of ontology learning methodologies across various applications and domains. This research aims at developing a domain independent methodology and a set of tools for learning the ontology layers (terms, taxonomy, conceptual relations, relation hierarchies, axioms and axiom schemata) from unstructured knowledge sources. In particular, we aim at automatically learning the three last layers, which has been done very rarely in the state of the art.The research will also explore filtering techniques that identify important information and integration techniques that adequately combine knowledge coming from various sources. The interest of the availability of a complete ontology (with all its layers) is that it is essential for agents to reason over semantic data and this will provide effective knowledge retrieval capabilities.