Understanding Tables on the Web

Wang, Jingjing; Wang, Haixun; Wang, Zhongyuan; Zhu, Kenny Q.

doi:10.1007/978-3-642-34002-4_11

Cited by 110 publications

(103 citation statements)

References 18 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…Wang et al [41] propose a multi-phase algorithm that using the universal probabilistic taxonomy called Probase [42] is capable of understanding the entitles, attributes and values in many tables on the Web. The approach begins by identifying a single ''entity column'' in a table and, based on its values and rest of the column headers, associates a concept from the Probase knowledge base with the table.…”

Section: Using Lod To Interpret Semi-structured Datamentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

View full text Add to dashboard Cite

a b s t r a c tData Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

show abstract

Section: Using Lod To Interpret Semi-structured Datamentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…They are used by web search engines for a variety of purposes: to answer search queries, understand short texts and annotate web tables [1,27,28]. While these knowledgebases have large numbers of entities for head concepts (e.g., country, city), they have very few entities for tail concepts (e.g., 'low-fertility country', 'emerging economies') [32,33,31].…”

Section: Introductionmentioning

confidence: 99%

Concept Expansion Using Web Tables

Wang

Chakrabarti

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

View full text Add to dashboard Cite

We study the following problem: given the name of an ad-hoc concept as well as a few seed entities belonging to the concept, output all entities belonging to it. Since producing the exact set of entities is hard, we focus on returning a ranked list of entities. Previous approaches either use seed entities as the only input, or inherently require negative examples. They suffer from input ambiguity and semantic drift, or are not viable options for ad-hoc tail concepts. In this paper, we propose to leverage the millions of tables on the web for this problem. The core technical challenge is to identify the "exclusive" tables for a concept to prevent semantic drift; existing holistic ranking techniques like personalized PageRank are inadequate for this purpose. We develop novel probabilistic ranking methods that can model a new type of table-entity relationship. Experiments with real-life concepts show that our proposed solution is significantly more effective than applying state-of-the-art set expansion or holistic ranking techniques.

show abstract

“…Free text contains a lot of variation and ambiguity and is known to be hard to understand and extract. General tables, even though structured, can have many different forms (such as row span and column span) and styles, and many of them are not meaningful if we don't know the schema of the table or the meaning of the headers [12]. Lists, on the other hand, have relatively simpler structures and are easier to identify.…”

Section: Introductionmentioning

confidence: 99%

“…To that end, we have already built one of the largest open-domain taxonomy called Probase [15,13,12,11] which consists of 2.7 million concepts and many more instances. The "top-k" lists we extracted from the web can be an important source for enriching Probase's instance space.…”

Section: Introductionmentioning

confidence: 99%

A system for extracting top-K lists from the web

Zhang

Zhu

Wang

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Self Cite

View full text Add to dashboard Cite

List data is an important source of structured data on the web. This paper is concerned with "top-k" pages, which are web pages that describe a list of k instances of a particular topic or concept. Examples include "the 10 tallest persons in the world" and "the 50 hits of 2010 you don't want to miss". Compared to normal web list data, "top-k" lists contain richer information and are easier to understand. Therefore the extraction of such lists can help enrich existing knowledge bases about general concepts, or act as a preprocessing step to produce facts for a fact answering engine. We present an efficient system that extracts the target lists from web pages with high accuracy. We have used the system to process up to 160 million, or 1/10 of a high-frequency web snapshot from Bing, and obtained over 140,000 lists with 90.4% precision.

show abstract

Understanding Tables on the Web

Cited by 110 publications

References 18 publications

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Concept Expansion Using Web Tables

A system for extracting top-K lists from the web

Contact Info

Product

Resources

About