Open information extraction from the web

Etzioni, Oren; Banko, Michele; Soderland, Stephen; Weld, Daniel S.

doi:10.1145/1409360.1409378

Cited by 513 publications

(327 citation statements)

References 13 publications

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“…The first, of course, is more evaluation, including comparisons with 'benchmark' systems such as the use of random suggestions, but also evaluations with real users. The second is to try some of the many ways of learning the Feature Extraction rules, see, e.g., [4]. The third is to investigate variants of the algorithm, where we would use different ways of ranking the cases, features and feature-values.…”

Section: Discussionmentioning

confidence: 99%

An Evaluation of the GhostWriter System for Case-Based Content Suggestions

Waugh

Bridge

2010

Artificial Intelligence and Cognitive Science

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Abstract. The Web has many sites where users can exchange goods and services. Often, the end-users must write free-text descriptions of the goods and services they have available, or the goods and services they are seeking. The quality of these descriptions is often low. In this paper, we describe the GhostWriter system, which encourages users to write descriptions that are more comprehensive. The system makes content suggestions from a case base of successful descriptions. The paper describes a new off-line ablation study that we have carried out to evaluate the system. The results show that GhostWriter has a high success rate in making suggestions that quickly recover ablated content. 1

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Section: Discussionmentioning

confidence: 99%

An Evaluation of the GhostWriter System for Case-Based Content Suggestions

Waugh

Bridge

2010

Artificial Intelligence and Cognitive Science

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“…[21]. IESs, such as Never-Ending Language Learner (NELL), Know It All, TextRunner, or Snowball represent this approach [1,3,6,9,10,22,23,56,59,68,78]. The systems mentioned above represent the trend called open IE.…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction

Mirończuk

2017

Knowl Inf Syst

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The aim of this study is to propose an information extraction system, called BigGrams, which is able to retrieve relevant and structural information (relevant phrases, keywords) from semi-structural web pages, i.e. HTML documents. For this purpose, a novel semi-supervised wrappers induction algorithm has been developed and embedded in the BigGrams system. The wrappers induction algorithm utilizes a formal concept analysis to induce information extraction patterns. Also, in this article, the author (1) presents the impact of the configuration of the information extraction system components on information extraction results and (2) tests the boosting mode of this system. Based on empirical research, the author established that the proposed taxonomy of seeds and the HTML tags level analysis, with appropriate pre-processing, improve information extraction results. Also, the boosting mode works well when certain requirements are met, i.e. when well-diversified input data are ensured.

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“…In text, this usually amounts to examining pairs of entities in a document and determining (from local language cues) whether a relation exists between them" [32]. Works reported in the literature on relation extraction uses numerous methods that, according to [33], can be divided into four main classes: knowledge-based methods, which usually rely on patterns and are thus sometimes called pattern-based methods, supervised methods, semisupervised methods, and self-supervised (unsupervised) methods. This section reviews the existing works related to relation extractions in general and for the Arabic language.…”

Section: Related Workmentioning

confidence: 99%

Ontological Lexicon Enrichment: The Badea System For Semi-Automated Extraction Of Antonymy Relations From Arabic Language Corpora

Al-Yahya

Al-Malak

Aldhubayi

2016

MJCS

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Ontological lexicons are considered a rich source of knowledge for the development of various natural language processing tools and applications; however, they are expensive to build, maintain, and extend. In this paper, we present the Badea system for the semi-automated extraction of lexical relations, specifically antonyms using a pattern-based approach to support the task of ontological lexicon enrichment. The approach is based on an ontology of "seed" pairs of antonyms in the Arabic language; we identify patterns in which the pairs occur and then use the patterns identified to find new antonym pairs in an Arabic textual corpora. Experiments are conducted on Badea using texts from three Arabic textual corpuses: KSUCCA, KACSTAC, and CAC. The system is evaluated and the patterns' reliability and system performance is measured. The results from our experiments on the three Arabic corpora show that the pattern-based approach can be useful in the ontological enrichment task, as the evaluation of the system resulted in the ontology being updated with over 300 new antonym pairs, thereby enriching the lexicon and increasing its size by over 400%. Moreover, the results show important findings on the reliability of patterns in extracting antonyms for Arabic. The Badea system will facilitate the enrichment of ontological lexicons that can be very useful in any Arabic natural language processing system that requires semantic relation extraction.Keywords: Antonym Extraction, Ontology, Arabic Lexicon, Semantic Relation, Arabic NLP INTRODUCTIONA lexicon is defined as "the knowledge that a native speaker has about a language. This includes information about the form and meanings of words and phrases, lexical categorization, the appropriate usage of words and phrases, relationships between words and phrases, and categories of words and phrases" [1]. Lexicon is an essential element for natural language processing (NLP) applications. For some applications, such as machine translation, lexicon is a critical resource [2]. An important aspect of such lexicons that renders them effective, reusable, and sharable within the community is building them upon standards such as semantic Web standards in the form of ontologies. An ontological lexicon is a lexicon designed using an ontological model and developed as an ontology. Ontological lexicons play a vital role in NLP applications such as language analysis, semantic annotation, summarization, machine translation, sense disambiguation, generation of lexical-competence questions used in standard language tests, and other applications that rely on implicit information in the text.Although ontological lexicons provide a rich source of knowledge for NLP applications, like other types of computational lexicons, they are expensive to build, maintain, and extend [2] [3] [4]. Moreover, the task of relation extraction is essential in any ontological lexicon development. Relation extraction focuses on the extraction of structured relations from unstructured sources such as Web documents ...

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Open information extraction from the web

Cited by 513 publications

References 13 publications

An Evaluation of the GhostWriter System for Case-Based Content Suggestions

An Evaluation of the GhostWriter System for Case-Based Content Suggestions

The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction

Ontological Lexicon Enrichment: The Badea System For Semi-Automated Extraction Of Antonymy Relations From Arabic Language Corpora

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