选择和增强:增强的密集检索知识图增强

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Micheal Abaho, Yousef H. Alfaifi
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引用次数: 0

摘要

在NLP社区中,将文本信息注入知识图(KG)实体表示是提高面向KG任务性能的一项有价值的探索。通常用于增强KG嵌入的外部知识范围从语义丰富的词汇依赖解析特征到一组相关关键字,再到外部语料库(如wikipedia等)提供的完整文本描述。尽管这种创新(文本增强的KG嵌入)已经取得了进展,但这项工作中的建议表明它可以进一步改进。代替使用单一的文本描述(由于文本固有的词汇歧义,不能充分代表实体),我们提出了一个多任务框架,共同选择一组与KG实体相关的文本描述,并将KG嵌入与文本描述对齐或增强。与之前插入知识库中声明的正式实体描述的工作不同,该框架利用检索器模型有选择地识别更丰富或高度相关的文本描述,以用于扩展实体。此外,框架将在增强过程中使用的描述数量作为参数,这允许在确定适当的数字之前枚举多个数字的灵活性。实验结果表明,与使用传统cnn的文本增强知识图增强方法相比,链接预测的平均倒数秩(MRR)和Hits@10分数分别提高了5.5%和3.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation
Injecting textual information into knowledge graph (KG) entity representations has been a worthwhile expedition in terms of improving performance in KG oriented tasks within the NLP community. External knowledge often adopted to enhance KG embeddings ranges from semantically rich lexical dependency parsed features to a set of relevant key words to entire text descriptions supplied from an external corpus such as wikipedia and many more. Despite the gains this innovation (Text-enhanced KG embeddings) has made, the proposal in this work suggests that it can be improved even further. Instead of using a single text description (which would not sufficiently represent an entity because of the inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a set of text descriptions relevant to KG entities as well as align or augment KG embeddings with text descriptions. Different from prior work that plugs formal entity descriptions declared in knowledge bases, this framework leverages a retriever model to selectively identify richer or highly relevant text descriptions to use in augmenting entities. Furthermore, the framework treats the number of descriptions to use in augmentation process as a parameter, which allows the flexibility of enumerating across several numbers before identifying an appropriate number. Experiment results for Link Prediction demonstrate a 5.5% and 3.5% percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10 scores respectively, in comparison to text-enhanced knowledge graph augmentation methods using traditional CNNs.
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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