Entity Matching across Heterogeneous Sources

Yang, Yang; Sun, Yizhou; Tang, Jie; Ma, Bo; Li, Juanzi

doi:10.1145/2783258.2783353

Cited by 30 publications

(13 citation statements)

References 24 publications

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“…The challenge is that different sources may use different languages or terminologies to describe the same topic. A probabilistic model was proposed to integrate the topic extraction and matching into a unified model (Yang et al 2015). As we don't have access to the full text of the reference datasets, this method is not applicable to our problem.…”

Section: Related Workmentioning

confidence: 99%

Cleaning Noisy and Heterogeneous Metadata for Record Linking across Scholarly Big Datasets

Sefid

et al. 2019

AAAI

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Automatically extracted metadata from scholarly documents in PDF formats is usually noisy and heterogeneous, often containing incomplete fields and erroneous values. One common way of cleaning metadata is to use a bibliographic reference dataset. The challenge is to match records between corpora with high precision. The existing solution which is based on information retrieval and string similarity on titles works well only if the titles are cleaned. We introduce a system designed to match scholarly document entities with noisy metadata against a reference dataset. The blocking function uses the classic BM25 algorithm to find the matching candidates from the reference data that has been indexed by ElasticSearch. The core components use supervised methods which combine features extracted from all available metadata fields. The system also leverages available citation information to match entities. The combination of metadata and citation achieves high accuracy that significantly outperforms the baseline method on the same test dataset. We apply this system to match the database of CiteSeerX against Web of Science, PubMed, and DBLP. This method will be deployed in the CiteSeerX system to clean metadata and link records to other scholarly big datasets.

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Section: Related Workmentioning

confidence: 99%

Cleaning Noisy and Heterogeneous Metadata for Record Linking across Scholarly Big Datasets

Sefid

et al. 2019

AAAI

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“…However, the approaches suffer from the requirement of heavy human efforts, and is thus costly and labor-expensive. Many works leverage the extra resources, such as OWL properties [9], entity descriptions [37], information of entities and relations [16,22,27]. Such methods are complex and usually limited by the availability of the extra information about a knowledge graph.…”

Section: Entity Alignmentmentioning

confidence: 99%

Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference

Pei

Hoehndorf

et al. 2019

The World Wide Web Conference

114

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Entity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection. Most of the recent solutions for entity alignment are based on knowledge graph embedding, which maps knowledge entities in a low-dimension space where entities are connected with the guidance of prior aligned entity pairs. The study in this paper focuses on two important issues that limit the accuracy of current entity alignment solutions: 1) labeled data of priorly aligned entity pairs are difficult and expensive to acquire, whereas abundant of unlabeled data are not used; and 2) knowledge graph embedding is affected by entity's degree difference, which brings challenges to align high frequent and low frequent entities. We propose a semi-supervised entity alignment method (SEA) to leverage both labeled entities and the abundant unlabeled entity information for the alignment. Furthermore, we improve the knowledge graph embedding with awareness of the degree difference by performing the adversarial training. To evaluate our proposed model, we conduct extensive experiments on real-world datasets. The experimental results show that our model consistently outperforms the state-of-the-art methods with significant improvement on alignment accuracy.

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“…This process is called "Duplicate Records Detection" or "Entity Matching", and due to the duplicates; inconsistencies appear, so this process also called "Inconsistencies Detection". In this process; duplicates are detected [5,6,7,8,9,10] in preparation to remove the ambiguities in the generated answers and to fuse inconsistencies before passing the answers to the user. Detected duplicates are marked in the answer set, and passed as an input to the successive process to resolve the inconsistencies.…”

Section: Inconsistency Detection Processmentioning

confidence: 99%

A Mapping Approach for Fully Virtual Data Integration System Processes

Qutaany¹,

Hegazi²,

Bastawissy³

2018

ijacsa

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Nowadays, organizations cannot satisfy their information needs from one data source. Moreover, multiple data sources across the organization fuels the need for data integration. Data integration system's users pose queries in terms of an integrated schema and expect accurate, unambiguous, and complete answers. So the data integration system is not limited to, getting the answers to the queries from the sources, but also it is extended to detect and resolve the data quality problems appeared due to the integration process. The most crucial component in any data integration system is the mappings constructed between the data sources and the integrated schema. In this paper a new mapping approach is proposed to map not only the elements of the integrated schema as done by the existing approaches, but also it maps other elements required in detecting and resolving the duplicates. It provides a means to facilitate future extensibility and changes to both the sources and the integrated schema. The proposed approach provides a linkage between the fundamental components required to provide accurate and unambiguous answers to the users' queries from the integration system.

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Entity Matching across Heterogeneous Sources

Cited by 30 publications

References 24 publications

Cleaning Noisy and Heterogeneous Metadata for Record Linking across Scholarly Big Datasets

Cleaning Noisy and Heterogeneous Metadata for Record Linking across Scholarly Big Datasets

Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference

A Mapping Approach for Fully Virtual Data Integration System Processes

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