利用特征/矢量实体检索和消歧技术创建有监督和无监督的语义表解释方法

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Roberto Avogadro , Fabio D’Adda , Marco Cremaschi
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引用次数: 0

摘要

最近,人们对提取和注释网络上的表格越来越感兴趣。这项工作可以将文本数据转换成机器可读的格式,从而执行各种人工智能任务,例如语义搜索和数据集扩展。语义表解释(STI)是对表中元素进行注释的过程。本文探讨了语义表解释,解决了知识图谱(KG)背景下实体检索和实体消歧的难题。它介绍了LamAPI(一种基于字符串/类型过滤的信息检索系统)和s-elBat(一种将启发式方法和基于ML的方法相结合的实体消歧技术)。通过应用在该领域获得的专有技术,并从我们以前的科技创新方法和最新技术中提取算法、技术和组件,我们创建了一个新的平台,能够注释任何表格数据,并确保高质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature/vector entity retrieval and disambiguation techniques to create a supervised and unsupervised semantic table interpretation approach

Recently, there has been an increasing interest in extracting and annotating tables on the Web. This activity allows the transformation of textual data into machine-readable formats to enable the execution of various artificial intelligence tasks, e.g., semantic search and dataset extension. Semantic Table Interpretation (STI) is the process of annotating elements in a table. The paper explores Semantic Table Interpretation, addressing the challenges of Entity Retrieval and Entity Disambiguation in the context of Knowledge Graphs (KGs). It introduces LamAPI, an Information Retrieval system with string/type-based filtering and s-elBat, an Entity Disambiguation technique that combines heuristic and ML-based approaches. By applying the acquired know-how in the field and extracting algorithms, techniques and components from our previous STI approaches and the state of the art, we have created a new platform capable of annotating any tabular data, ensuring a high level of quality.

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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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