A learning-by-reading framework for ontology learning

本体学习的边读边学框架

• 批准号: 402263-2011
• 负责人: Zouaq, Amal
• 金额: $ 1.38万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573221
• 关键词: learning; reading; framework; ontology

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. The long-term objective of this research is to build learning-by-reading systems by exploring the integration of deep semantic analysis, filtering and knowledge integration techniques. The short-term objective is to build a proof of concept by extracting a domain ontology using a combination of some of these techniques and by focusing on layers that require deep semantic analysis. The major contribution of our proposal is the extraction of domain conceptual models from unstructured sources enabling the indexing of texts and reasoning as well as the exploitation of such conceptual models in various areas such as the Semantic Web and the corporate world.

阅读学习挑战指的是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，并过滤和整合这一知识。在语义网的背景下，这一挑战通常指的是从自由文本学习领域本体的能力。当采用的方法是开放(独立于领域)和无监督(没有任何指导)的知识提取时，这项任务尤其具有挑战性。这种与领域无关的方法对于跨各种应用和领域的本体学习方法的互操作性和重用性至关重要。该研究旨在开发一种独立于领域的方法和一套工具，用于从非结构化知识源中学习本体层(术语、分类、概念关系、关系层次、公理和公理模式)。特别是，我们的目标是自动学习最后三层，这在现有技术的状态下很少完成。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知识的集成技术。一个完整的本体(包括它的所有层)的可用性的好处是，对代理来说，它是对语义数据进行推理的关键，这将提供有效的知识检索能力。 本研究的长期目标是通过探索深度语义分析、过滤和知识整合技术的整合来构建阅读学习系统。短期目标是通过使用其中一些技术的组合提取领域本体并关注需要深入语义分析的层来构建概念证明。我们的建议的主要贡献是从非结构化来源提取领域概念模型，使得能够对文本和推理进行索引，以及在诸如语义网和企业世界等不同领域中利用这些概念模型。