An ontology-based approach for web information extraction

Jellouli, Ismail; Mohajir, Mohammed El

doi:10.1109/cist.2011.6148583

Cited by 5 publications

(4 citation statements)

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“…Hence linguistic analyses are not commonly utilized. To extract online data, other methods are applied [20][21][22][23][24]. Information extraction methods fall into two categories: wrapper-based information extraction and Information extraction based on conceptual model.…”

Section: Information Extractionmentioning

confidence: 99%

Designing an Agent for Information Extraction from Persian E-shops

2018

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E-shops are among the most conventional applications of Electronic Commerce. In these shops, the buyers search for their goods through key words or classifications and read the product description provided by the sellers. Though, when the number of items is high, this gets to be difficult for the users. On the one hand, there are too many e-shops, and browsing in these shops to find the best and most appropriate goods is a difficult and time-consuming process. On the other hand, product descriptions are not the same in different websites, and there are different product forms. This study investigates about products and sellers in various websites based on the conditions and user requirements through software agents which present the extracted information in the form of a table to the users which enables them to compare prices and each seller's conditions without spending too much time for browsing. Using this method increases precision and recall indices comparing to a conventional user browsing

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Section: Information Extractionmentioning

confidence: 99%

Designing an Agent for Information Extraction from Persian E-shops

2018

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“…Penalty calculated based on Ω guarantees, for example, that strings "ba" and "baba" will NOT result a similarity = 1. When the lengths of both strings (S M AX and s min ) are different, there is a result adjustment in order to provide another penalty in the similarity level, based on the difference on the length of words and the factor Υ (lines [7][8][9][10][11][12][13][14][15]. Ω (=0.975) and Υ (=0.005) were manually adjusted after testing the proposed function in an application that searches for similar names of people and companies.…”

Section: String Similaritymentioning

confidence: 99%

“…A large amount of unstructured data is being produced by different kinds of information systems [8], as free text from the medical records. String similarity algorithms are used to identify concepts when text is loaded with misspellings [4].…”

Section: Introductionmentioning

confidence: 99%

Fast Phonetic Similarity Search over Large Repositories

Tissot

Peschl

Fabro

2014

Lecture Notes in Computer Science

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Analysis of unstructured data may be inefficient in the presence of spelling errors. Existing approaches use string similarity methods to search for valid words within a text, with a supporting dictionary. However, they are not rich enough to encode phonetic information to assist the search. In this paper, we present a novel approach for efficiently perform phonetic similarity search over large data sources, that uses a data structure called PhoneticMap to encode language-specific phonetic information. We validate our approach through an experiment over a data set using a Portuguese variant of a well-known repository, to automatically correct words with spelling errors.

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“…There is a large amount of unstructured data being produced by different kinds of information systems, in a variety of formats, due to the advancement of communication and information technologies [1, 2]. Within the clinical domain, Electronic Health Record (EHR) systems are becoming widely adopted, from which information describing the patient’s health conditions is often presented and stored in the form of free text notes [3].…”

Section: Introductionmentioning

confidence: 99%

Combining string and phonetic similarity matching to identify misspelt names of drugs in medical records written in Portuguese

Tissot

Dobson

2019

J Biomed Semant

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BackgroundThere is an increasing amount of unstructured medical data that can be analysed for different purposes. However, information extraction from free text data may be particularly inefficient in the presence of spelling errors. Existing approaches use string similarity methods to search for valid words within a text, coupled with a supporting dictionary. However, they are not rich enough to encode both typing and phonetic misspellings.ResultsExperimental results showed a joint string and language-dependent phonetic similarity is more accurate than traditional string distance metrics when identifying misspelt names of drugs in a set of medical records written in Portuguese.ConclusionWe present a hybrid approach to efficiently perform similarity match that overcomes the loss of information inherit from using either exact match search or string based similarity search methods.

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An ontology-based approach for web information extraction

Cited by 5 publications

References 0 publications

Designing an Agent for Information Extraction from Persian E-shops

Designing an Agent for Information Extraction from Persian E-shops

Fast Phonetic Similarity Search over Large Repositories

Combining string and phonetic similarity matching to identify misspelt names of drugs in medical records written in Portuguese

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