Finding Synonymous Attributes in Evolving Wikipedia Infoboxes

Sottovia, Paolo; Paganelli, Matteo; Guerra, Francesco; Velegrakis, Yannis

doi:10.1007/978-3-030-28730-6_11

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Encyclopedia resources (e.g., infoboxes) are good data sources for semantic relation extraction (Sottovia et al, 2019). Some researchers have investigated the automatic synonym extraction from the encyclopedia by using textual patterns and statistics information.…”

Section: Encyclopedia-based Approachesmentioning

confidence: 99%

“…Some researchers have investigated the automatic synonym extraction from the encyclopedia by using textual patterns and statistics information. Sottovia et al (2019) proposed a method to acquire synonymous attributes from Wikipedia infoboxes. They employed cooccurrence degree and similar values to identify clusters of synonymous infobox attributes.…”

Section: Encyclopedia-based Approachesmentioning

confidence: 99%

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Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

Yan

Huang

Kong

2021

International Journal of Information Technology and Web Engineering

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Discovering entity synonymous relations is an important work for many entity-based applications. Existing entity synonymous relation extraction approaches are mainly based on lexical patterns or distributional corpus-level statistics, ignoring the context semantics between entities. For example, the contexts around ''apple'' determine whether ''apple'' is a kind of fruit or Apple Inc. In this paper, an entity synonymous relation extraction approach is proposed using context-aware permutation invariance. Specifically, a triplet network is used to obtain the permutation invariance between the entities to learn whether two given entities possess synonymous relation. To track more synonymous features, the relational context semantics and entity representations are integrated into the triplet network, which can improve the performance of extracting entity synonymous relations. The proposed approach is implemented on three real-world datasets. Experimental results demonstrate that the approach performs better than the other compared approaches on entity synonymous relation extraction task.

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Section: Encyclopedia-based Approachesmentioning

confidence: 99%

Section: Encyclopedia-based Approachesmentioning

confidence: 99%

Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

Yan

Huang

Kong

2021

International Journal of Information Technology and Web Engineering

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An Automatic Approach for Extracting Chinese Entity Synonyms from Encyclopedias

Huang

Wei

Zhao

et al. 2020

Proceedings of the 2020 3rd International Conference on Big Data Technologies

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Determining the Largest Overlap between Tables

Zecchini,

Bleifuß,

Simonini

et al. 2024

Proc. ACM Manag. Data

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Both on the Web and in data lakes, it is possible to detect much redundant data in the form of largely overlapping pairs of tables. In many cases, this overlap is not accidental and provides significant information about the relatedness of the tables. Unfortunately, efficiently quantifying the overlap between two tables is not trivial. In particular, detecting their largest overlap, i.e., their largest common subtable, is a computationally challenging problem. As the information overlap may not occur in contiguous portions of the tables, only the ability to permute columns and rows can reveal it. The detection of the largest overlap can help us in relevant tasks such as the discovery of multiple coexisting versions of the same table, which can present differences in the completeness and correctness of the conveyed information. Automatically detecting these highly similar, matching tables would allow us to guarantee their consistency through data cleaning or change propagation, but also to eliminate redundancy to free up storage space or to save additional work for the editors. We present the first formal definition of this problem, and with it Sloth, our solution to efficiently detect the largest overlap between two tables. We experimentally demonstrate on real-world datasets its efficacy in solving this task, analyzing its performance and showing its impact on multiple use cases.

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Finding Synonymous Attributes in Evolving Wikipedia Infoboxes

Cited by 3 publications

References 14 publications

Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

Extracting Entity Synonymous Relations via Context-Aware Permutation Invariance

An Automatic Approach for Extracting Chinese Entity Synonyms from Encyclopedias

Determining the Largest Overlap between Tables

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