Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings

Efthymiou, Vasilis; Hassanzadeh, Oktie; Rodríguez-Muro, Mariano; Christophides, Vassilis

doi:10.1007/978-3-319-68288-4_16

Cited by 73 publications

(112 citation statements)

References 21 publications

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“…The third method is based on ontology matching, and exploits schematic and instance information of entities available both in a knowledge base and in a web table. Efthymiou et al (2017) find that hybrid methods that combine the second and third methods (in any order) tend to perform best. The column type identification component of TableMiner+ (Zhang, 2017) has already been discussed earlier, in Sect.…”

Section: Entity Linkingmentioning

confidence: 91%

“…We organize relevant literature based on the task that is being addressed into six main categories. These are: ; Chen and Cafarella (2013); Cafarella et al (2008b); Balakrishnan et al (2015); Cafarella et al (2009); ; Bhagavatula et al (2015) Wang and Hu (2002b,a); ; Chen and Cafarella (2013); Cafarella et al (2008b); Crestan and Pantel (2011); Lautert et al (2013); Nishida et al (2017); Venetis et al (2011); Mulwad et al (2010); Fan et al (2014); Bhagavatula et al (2015); ; Efthymiou et al (2017); ; Hassanzadeh et al (2015); Mulwad et al (2013); Sekhavat et al (2014); Ibrahim et al (2016); Limaye et al (2010); Muñoz et al (2014); ; Ritze and Bizer (2017) Table search Query Ranked list of tables Cafarella et al (2009); Pimplikar and Sarawagi (2012); Cafarella et al (2008a); Bhagavatula et al (2013); ; ; Das Sarma et al (2012); Yakout et al (2012); Nguyen et al (2015); Zhang and Balog (2018a); Limaye et al (2010); Nargesian et al (2018); Zhang and Balog (2019b) Question Answering Natural language query Structured data Pasupat and Liang (2015); Sun et al (2016);…”

Section: Introductionmentioning

confidence: 99%

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Web Table Extraction, Retrieval and Augmentation

Zhang

Balog

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represent a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks : table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.

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Section: Entity Linkingmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Web Table Extraction, Retrieval and Augmentation

Zhang

Balog

2019

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Before feeding the record sets returned by data extraction into a particular application, it is commonly necessary to perform some of the following integration tasks: semantisation [25,45,54,55,60,63,71], which either maps the descriptors onto the terminology box of a particular ontology or the tuples onto its assertion box [19]; union [23], which merges record sets that provide similar data; finding primary keys [62], which determines which components of the tuples identify them as univocally as possible; record linkage [8,11,12], which finds different records that refer to the same actual entities; augmentation [6,52,67], which joins record sets on the same topic to complete the information that they provide individually; and cleaning [10,31,61], which fixes data. Note that the integration tasks are orthogonal to data extraction because they are independent from the source of the record sets, which is the reason why they fall out of the scope of this article.…”

Section: Data-extraction Vocabularymentioning

confidence: 99%

On extracting data from tables that are encoded using HTML

Roldán

Jiménez

Corchuelo

2020

Knowledge-Based Systems

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Tables are a common means to display data in human-friendly formats. Many authors have worked on proposals to extract those data back since this has many interesting applications. In this article, we summarise and compare many of the proposals to extract data from tables that are encoded using HTML and have been published between 2000 and 2018. We first present a vocabulary that homogenises the terminology used in this field; next, we use it to summarise the proposals; finally, we compare them side by side. Our analysis highlights several challenges to which no proposal provides a conclusive solution and a few more that have not been addressed sufficiently; simply put, no proposal provides a complete solution to the problem, which seems to suggest that this research field shall keep active in the near future. We have also realised that there is no consensus regarding the datasets and the methods used to evaluate the proposals, which hampers comparing the experimental results.

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“…First, we seamlessly process facet values that contain entities and literal values, such as dates or numbers. Second, entity linking algorithms are usually optimized for text [21], while algorithms tailored for tables use table rows as context to support disambiguation [9]. We have plain sets of values, which would provide little context to support disambiguation.…”

Section: Predicate Filteringmentioning

confidence: 99%