Blocking Techniques for Entity Linkage: A Semantics-Based Approach

Azzalini, Fabio; Jin, Songle; Renzi, Marco; Tanca, Letizia

doi:10.1007/s41019-020-00146-w

Cited by 24 publications

(3 citation statements)

References 22 publications

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“…(4) Diverse data processing and storage methods: based on their own business needs, each software manufacturer has great differences in data model, presentation content, and storage format. It also results in the characteristics of multisource data [15].…”

Section: E Concept and Characteristics Of Data Integrationmentioning

confidence: 99%

Data Integration Method Design of Decision Spatial Information System

2022

Security and Communication Networks

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With the development of technology, more and more enterprises have begun to use data integration analysis to make decisions. The decision spatial information system is an information system that provides decision support after analyzing and processing the spatial information. Data integration refers to a method of reasonably centralizing different or multisource data for data sharing, which can assist in decision management. This paper aims to study a more efficient data integration method for decision space information system. In this paper, an emergency information decision-making system for digital water network is designed, and a specific data integration algorithm is proposed. And the performance test of the early warning capability and power consumption of the system is carried out. The results show that the early warning time of the system designed in this paper is within 1 to 4 seconds, while the early warning time of the traditional system is within 1.5 seconds to 8 seconds. The power consumption of the system designed in this paper is not more than 25 W, while the traditional system power consumption is up to 50 W. The power consumption of the system in this paper is about half of the traditional system. Compared with the traditional system early warning capability, the system designed in this paper is significantly faster, the system power consumption is lower, and the system is more stable.

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Section: E Concept and Characteristics Of Data Integrationmentioning

confidence: 99%

Data Integration Method Design of Decision Spatial Information System

2022

Security and Communication Networks

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“…In the process of constructing knowledge map, entity link is also very necessary. Azzalini et al [9] use deep learning to capture the semantic properties of data. Utilizing the subject-predicateobject triples to build knowledge attracts more attention.…”

Section: Introductionmentioning

confidence: 99%

EAGS: An extracting auxiliary knowledge graph model in multi-turn dialogue generation

et al. 2022

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Multi-turn dialogue generation is an essential and challenging subtask of text generation in the question answering system. Existing methods focused on extracting latent topic-level relevance or utilizing relevant external background knowledge. However, they are prone to ignore the fact that relying too much on latent aspects will lose subjective key information. Furthermore, there is not so much relevant external knowledge that can be used for referencing or a graph that has complete entity links. Dependency tree is a special structure that can be extracted from sentences, it covers the explicit key information of sentences. Therefore, in this paper, we proposed the EAGS model, which combines the subjective pivotal information from the explicit dependency tree with sentence implicit semantic information. The EAGS model is a knowledge graph enabled multi-turn dialogue generation model, and it doesn't need extra external knowledge, it can not only extract and build a dependency knowledge graph from existing sentences, but also prompt the node representation, which is shared with Bi-GRU each time step word embedding in node semantic level. We store the specific domain subgraphs built by the EAGS, which can be retrieved as external knowledge graph in the future multi-turn dialogue generation task. We design a multi-task training approach to enhance semantics and structure local feature extraction, and balance with the global features. Finally, we conduct experiments on Ubuntu large-scale English multi-turn dialogue community dataset and English Daily dialogue dataset. Experiment results show that our EAGS model performs well on both automatic evaluation and human evaluation compared with the existing baseline models.

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“…As organizations ingest and process larger amounts of data, the time and effort it takes to prepare and integrate data into useful products are also increasing, and many researchers are working to alleviate this bottleneck using several different approaches [1], [2], [3]. The root cause of the time delay is human supervision of the curation steps including data quality analysis, data cleansing and standardization, entity resolution (ER), and data integration [4]. The goal of ER is to link two references if, and only if, the references are equivalent [5], [6].…”

Section: Introductionmentioning

confidence: 99%