Web-scale information extraction in knowitall: (preliminary results)

By Oren Etzioni, Michael J. Cafarella, Doug Downey, Stanley Kok, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, Alexander Yates. 2004

931 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Management Science and Operations Research

Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “Early work include RoadRunner [139], which compares HTML pages to generates wrappers and KnowItAll [140], which uses extraction rules and a search engine to identify and rank facts.”
Never-Ending Learning (2015, background): “…coupling constraints C = { 〈 φ k , V k 〉 } among the solutions to these learning tasks, where φ k is a real-valued function over two or more learning tasks, specifying the degree of satisfaction of the constraint, and V k is a vector of indices over learning tasks, specifying the arguments to φ k .”
Understandable Big Data: A survey (2015, background): “Similarly if we want to tackle variety, wemust deal with various data formats (tweets in [45,46,88] and natural language texts [47,80,62,76]) and distributed data [38,39].”
Knowledge Graphs (2020, neutral): “Other learning-based approaches again use bootstrapping [78, 148] and distant supervision [244, 355, 439, 486, 511, 565] to forgo the need for manual labelling; the former requires a subset of manually-labelled seed examples, while the latter finds sentences in a large corpus of text mentioning pair”
VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations (2004, background): “Hearst (1992) was the first followed by recent larger-scale and more fully automated efforts (Pantel and Ravichandran 2004; Etzioni et al. 2004; Ravichandran and Hovy 2002).”
Management of probabilistic data: foundations and challenges (2007, background): “The central problem discussed in this paper is the following: given a boolean query q on a probabilistic database P DB, compute the probability of q on P DB, in notation P(q).”
ASER: A Large-scale Eventuality Knowledge Graph (2019, background): “Nevertheless, these KGs may fall short in circumstances that require not only knowledge about Things or Objects, but also those about Activities, States, and Events.”
Navigating the Data Lake with DATAMARAN: Automatically Extracting Structure from Log Datasets (2017, background): “Some papers do not rely on the HTML structure, opting instead to use NLP [5, 14].”
Data Integration for the Relational Web (2009, methodology): “The system currently uses data extracted from HTML tables and HTML lists, but in principle can manipulate data obtained from any information extraction technique that emits relational-style data ( e.g. , we might use a transformed version of the outputs of [10, 17]).”
Exploratory Learning (2013, background): “Another line of research considers the problem of “open information extraction”, in which no classes or seeds are used at all [28,12,9].”
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases (2016, background): “For relation extraction, similarly, weak supervision [6, 13] and distant supervision [35, 53, 49, 21, 43, 31] approaches are proposed to address the domain restriction issue in traditional supervised systems [2, 33, 17].”
YAGO: A Large Ontology from Wikipedia and WordNet (2008, methodology): “These approaches use information extraction technologies that include pattern matching, natural-language parsing, and statistical learning [137, 65, 27, 2, 128, 116, 50].”
Unsupervised named-entity extraction from the Web: An experimental study (2005, background): “Our pattern learning (PL), subclass extraction (SE), and list extraction (LE) methods greatly improve on the recall of the baseline K NOW I T A LL system described in [20], while maintaining precision and improving extraction rate.”
WebTables: exploring the power of tables on the web (2008, methodology): “Snowball: A prototype system for extracting relations from large text collections.”
YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia (2007, background): “KnowItAll [9] and KnowItNow [4] are re-ported to have accuracy rates of 85% and 80%, respectively.”