Abstract
Knowledge management is a critical challenge for enterprises in today's digital world, as the volume and complexity of data being generated and collected continue to grow incessantly. Knowledge graphs (KG) emerged as a promising solution to this problem by providing a flexible, scalable, and semantically rich way to organize and make sense of data. This paper builds upon a recent survey of the research literature on combining KGs and Natural Language Processing (NLP). Based on selected application scenarios from enterprise context, we discuss synergies that result from such a combination. We cover various approaches from the three core areas of KG construction, reasoning as well as KG-based NLP tasks. In addition to explaining innovative enterprise use cases, we assess their maturity in terms of practical applicability and conclude with an outlook on emergent application areas for the future.
Abstract (translated)
知识管理是当今数字世界企业面临的一个关键挑战，因为随着数据生成和收集的不断增长，数据量和复杂性也在不断增加。知识图（KG）作为一种有前途的解决方案，为组织提供了灵活、可扩展和语义丰富的方式来组织和理解数据。本文基于对相关研究文献的最近调查，讨论了这种组合产生的协同作用。我们涵盖了知识图构建的三个核心领域：推理、知识图和自然语言处理（NLP）任务。除了解释创新企业应用案例外，我们还评估了它们在实践适用性方面的成熟度，并展望了未来可能出现的新应用领域。
URL
https://arxiv.org/abs/2404.01443
