A learning-by-reading framework for ontology learning

通过阅读学习的挑战是指一些自然语言理解任务，致力于从自由文本中提取正式的概念模型，并过滤和整合这些知识。在语义网的背景下，这一挑战通常是指从自由文本中学习领域本体的能力。当所采用的方法是开放的（独立于领域）和无监督的（没有任何指导）知识提取时，这项任务特别具有挑战性。这种领域独立的方法是至关重要的互操作性和重用的本体学习方法在不同的应用程序和领域。本研究的目的是开发一个领域独立的方法和一套工具学习本体层（术语，分类，概念关系，关系层次，公理和公理模式）从非结构化的知识源。特别地，我们的目标是自动学习最后三个层，这在现有技术中很少完成。研究还将探索识别重要信息的过滤技术和充分结合来自各种来源的联合收割机知识的集成技术。一个完整的本体（及其所有层）的可用性的利益是，它是必不可少的代理的原因，语义数据，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.