An assessment of open relation extraction systems for the semantic web

Zouaq, Amal; Gagnon, Michel; Jean-Louis, Ludovic

doi:10.1016/j.is.2017.08.008

Cited by 10 publications

(6 citation statements)

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“…Relation extraction associates pairs of named entities and identifies a pre-defined relationship between them. Closed relation extraction defines a closed set of relation types including a special type indicating "no relation" while open relation extraction conducts a binary classification of whether there exists a relationship between the two entities [1,24,25,32,46]. Composite extraction aims to extract more complex concepts such as reviews, opinions, and sentiment mentions.…”

Section: Related Work 51 Web Information Extractionmentioning

confidence: 99%

Simplified DOM Trees for Transferable Attribute Extraction from the Web

Zhou,

Sheng,

et al. 2021

Preprint

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There has been a steady need to precisely extract structured knowledge from the web (i.e. HTML documents). Given a web page, extracting a structured object along with various attributes of interest (e.g. price, publisher, author, and genre for a book) can facilitate a variety of downstream applications such as large-scale knowledge base construction, e-commerce product search, and personalized recommendation. Considering each web page is rendered from an HTML DOM tree, existing approaches formulate the problem as a DOM tree node tagging task. However, they either rely on computationally expensive visual feature engineering or are incapable of modeling the relationship among the tree nodes. In this paper, we propose a novel transferable method, Simplified DOM Trees for Attribute Extraction (SimpDOM), to tackle the problem by efficiently retrieving useful context for each node by leveraging the tree structure. We study two challenging experimental settings: (i) intra-vertical few-shot extraction, and (ii) cross-vertical fewshot extraction with out-of-domain knowledge, to evaluate our approach. Extensive experiments on the SWDE public dataset show that SimpDOM outperforms the state-of-the-art (SOTA) method by 1.44% on the F1 score. We also find that utilizing knowledge from a different vertical (cross-vertical extraction) is surprisingly useful and helps beat the SOTA by a further 1.37%.

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Section: Related Work 51 Web Information Extractionmentioning

confidence: 99%

Simplified DOM Trees for Transferable Attribute Extraction from the Web

Zhou,

Sheng,

et al. 2021

Preprint

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“…Hameed et al provided a way to classify the recommended systems by calculating similarity (Kumar et al, 2017). Other scholars have also conducted research on keyword similarity matching models (Hameed et al, 2012Beheshti et al, 2017Zouaq et al, 2017;Ding et al, 2016). Among them, Kumar et al (2017) proposed a weighted semantic information extraction algorithm idea by extracting nouns and verbs from the marker data, then providing different possibilities for each noun or verb to extract semantic-based terms; next these weights were established using cosine similarity.…”

Section: Semantic Extraction Of Keyword Matching and Similarity Retrimentioning

confidence: 99%

A decision system based on active perception and intelligent analysis for key location security information

Chen

Cao

et al. 2019

IJCSE

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In various enterprises, the security data are manually entered by multiple security workers at different times, which can lead to disordered data in the system, and also, analysis decision files need to be manually processed. To solve these problems, this paper presents a decision system for key location security information based on active perception and intelligent analysis. First, the designed system is developed based on C/S framework. Then, a deep learning perception model is constructed. Experimental results show that this system can significantly reduce the heavy workload of security inspectors, and reduce the frequency of safety accidents.

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“…We should discuss here the merits and demerits of the newly presented methods. A more detailed discussion on the previous Open IE systems was presented in [28]. REVERB extracts relations under lexical constraints and syntactic constraints resulting in more meaningful and informative relation phrases.…”

Section: Related Workmentioning

confidence: 99%

A Distance Approach for Open Information Extraction Based on Word Vector

2018

KSII TIIS

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Web-scale open information extraction (Open IE) plays an important role in NLP tasks like acquiring common-sense knowledge, learning selectional preferences and automatic text understanding. A large number of Open IE approaches have been proposed in the last decade, and the majority of these approaches are based on supervised learning or dependency parsing. In this paper, we present a novel method for web scale open information extraction, which employs cosine distance based on Google word vector as the confidence score of the extraction. The proposed method is a purely unsupervised learning algorithm without requiring any hand-labeled training data or dependency parse features. We also present the mathematically rigorous proof for the new method with Bayes Inference and Artificial Neural Network theory. It turns out that the proposed algorithm is equivalent to Maximum Likelihood Estimation of the joint probability distribution over the elements of the candidate extraction. The proof itself also theoretically suggests a typical usage of word vector for other NLP tasks. Experiments show that the distance-based method leads to further improvements over the newly presented Open IE systems on three benchmark datasets, in terms of effectiveness and efficiency.

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An assessment of open relation extraction systems for the semantic web

Cited by 10 publications

References 10 publications

Simplified DOM Trees for Transferable Attribute Extraction from the Web

Simplified DOM Trees for Transferable Attribute Extraction from the Web

A decision system based on active perception and intelligent analysis for key location security information

A Distance Approach for Open Information Extraction Based on Word Vector

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