知识抽取,即从不同来源、不同结构的数据中进行知识提取,形成知识(结构化数据)存入到知识图谱。

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摘要: 知识图谱的概念由谷歌于2012年提出,随后逐渐成为人工智能领域的一个研究热点,已在信息搜索、自动问答、决策分析等应用中发挥作用。虽然知识图谱在各领域展现出了巨大的潜力,但不难发现目前缺乏成熟的知识图谱构建平台,需要对知识图谱的构建体系进行研究,以满足不同的行业应用需求。文中以知识图谱构建为主线,首先介绍目前主流的通用知识图谱和领域知识图谱,描述两者在构建过程中的区别;然后,分类讨论图谱构建过程中存在的问题和挑战,并针对这些问题和挑战,分类描述目前图谱构建过程中的知识抽取、知识表示、知识融合、知识推理、知识存储5个层面的解决方法和策略;最后,展望未来可能的研究方向。

http://www.jsjkx.com/CN/10.11896/jsjkx.200700010

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In this paper, the challenges of maintaining a healthy IT operational environment have been addressed by proactively analyzing IT Service Desk tickets, customer satisfaction surveys, and social media data. A Cognitive solution goes beyond the traditional structured data analysis by deep analyses of both structured and unstructured text. The salient features of the proposed platform include language identification, translation, hierarchical extraction of the most frequently occurring topics, entities and their relationships, text summarization, sentiments, and knowledge extraction from the unstructured text using Natural Language Processing techniques. Moreover, the insights from unstructured text combined with structured data allow the development of various classification, segmentation, and time-series forecasting use-cases on the incident, problem, and change datasets. Further, the text and predictive insights together with raw data are used for visualization and exploration of actionable insights on a rich and interactive dashboard. However, it is hard not only to find these insights using traditional structured data analysis but it might also take a very long time to discover them, especially while dealing with a massive amount of unstructured data. By taking action on these insights, organizations can benefit from a significant reduction of ticket volume, reduced operational costs, and increased customer satisfaction. In various experiments, on average, upto 18-25% of yearly ticket volume has been reduced using the proposed approach.

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