Making Sense of Entities and Quantities in Web Tables

Ibrahim, Yusra; Riedewald, Mirek; Weikum, Gerhard

doi:10.1145/2983323.2983772

Cited by 31 publications

(32 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In follow-up work, Mulwad et al (2013) implement an improved semantic message passing method to extract RDF triples from tables. The semantic message passing first pre-processes the input (Ibrahim et al, 2016).…”

Section: Relation Extractionmentioning

confidence: 99%

“…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%

See 1 more Smart Citation

Web Table Extraction, Retrieval and Augmentation

Zhang

Balog

2019

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

View full text Add to dashboard Cite

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.

show abstract

Section: Relation Extractionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Note that Web tables are not always fully consistent in data representation and column descriptions [17,26]. As ambiguous representations of numeric types can generate wrong classification labels to categorical attributes, Algorithm 1 excludes all attributes of numeric type, which results in removing categorical attributes such as years etc., when generating the training data.…”

Section: Algorithmmentioning

confidence: 99%

Learning interesting attributes for automated data categorization

Pal

Michel

2018

Proceedings of the 30th International Conference on Scientific and Statistical Database Management

View full text Add to dashboard Cite

This work proposes and evaluates a novel approach to determine interesting categorical attributes for lists of entities. Once identified, such categories are of immense value to allow constraining (filtering) a current view of a user to subsets of entities. We show how a classifier is trained that is able to tell whether or not a categorical attribute can act as a constraint, in the sense of human-perceived interestingness. The training data is harnessed from Web tables, treating the presence or absence of a table as an indication that the attribute used as a filter constraint is reasonable or not. For learning the classification model, we review four well-known statistical measures (features) for categorical attributes-entropy, unalikeability, peculiarity, and coverage. We additionally propose three new statistical measures to capture the distribution of data, tailored to our main objective. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the applicability of the approach as a whole and, further, demonstrates the superiority of the proposed diversity measures over existing statistical measures like information entropy.

show abstract

“…Numbers and Relation Cardinalities Numbers in text are an important source of information. Much work has been done on understanding numbers that express temporal information (Ling and Weld, 2010;Strötgen and Gertz, 2010), and more recently, on numbers that express physical quantities or measures, either mentioned in text (Chaganty and Liang, 2016) or in the context of web tables (Ibrahim et al, 2016;Neumaier et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Cardinal Virtues: Extracting Relation Cardinalities from Text

Mirza

Razniewski²,

Darari³

et al. 2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 2: Short Papers)

Self Cite

View full text Add to dashboard Cite

Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discuss specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.

show abstract

Making Sense of Entities and Quantities in Web Tables

Cited by 31 publications

References 30 publications

Web Table Extraction, Retrieval and Augmentation

Web Table Extraction, Retrieval and Augmentation

Learning interesting attributes for automated data categorization

Cardinal Virtues: Extracting Relation Cardinalities from Text

Contact Info

Product

Resources

About