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A novel algorithm for extracting the user reviews from web pages

JOURNAL OF INFORMATION SCIENCE 2017年第5期43卷 696-712页

作者： Ucar, Erdem Uzun, Erdinc Tufekci, Pinar Trakya Univ Dept Comp Engn Fac Engn Edirne Turkey Namik Kemal Univ Dept Comp Engn Corlu Fac Engn Tekirdag Turkey

Extracting the user reviews in websites such as forums, blogs, newspapers, commerce, trips, etc. is crucial for text processing applications (e.g. sentiment analysis, trend detection/monitoring and recommendation systems) which are needed to deal with structured data. Traditional algorithms have three processes consisting of Document Object Model (DOM) tree creation, extraction of features obtained from this tree and machine learning. However, these algorithms increase time complexity of extraction process. This study proposes a novel algorithm that involves two complementary stages. The first stage determines which HTML tags correspond to review layout for a web domain by using the DOM tree as well as its features and decision tree learning. The second stage extracts review layout for web pages in a web domain using the found tags obtained from the first stage. This stage is more time-efficient, being approximately 21 times faster compared to the first stage. Moreover, it achieves a relatively high accuracy of 96.67% in our experiments of review block extraction.

关键词： Efficient extraction web data extraction web user reviews

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data mining approach to monitoring the requirements of the job market: A case study

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INFORMATION SYSTEMS 2017年 65卷 1-6页

作者： Karakatsanis, Ioannis AlKhader, Wala MacCrory, Frank Alibasic, Armin Omar, Mohammad Atif Aung, Zeyar Woon, Wei Lee Masdar Inst Sci & Technol Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Masdar Inst Sci & Technol Engn Syst & Management Abu Dhabi U Arab Emirates MIT Sloan Sch Management 77 Massachusetts Ave Cambridge MA 02139 USA

In challenging economic times, the ability to monitor trends and shifts in the job market would be hugely valuable to job-seekers, employers, policy makers and investors. To analyze the job market, researchers are increasingly turning to data science and related techniques which are able to extract underlying patterns from large collections of data. One database which is of particular relevance in the presence context is O*NET, which is one of the most comprehensive publicly accessible databases of occupational requirements for skills, abilities and knowledge. However, by itself the information in O*NET is not enough to characterize the distribution of occupations required in a given market or region. In this paper, we suggest a data mining based approach for identifying the most in-demand occupations in the modern job market. To achieve this, a Latent Semantic Indexing (LSI) model was developed that is capable of matching job advertisement extracted from the web with occupation description data in the O*NET database. The findings of this study demonstrate the general usefulness and applicability of the proposed method for highlighting job trends in different industries and geographical areas, identifying occupational clusters, studying the changes in jobs context over time and for various other research embodiments.

关键词： Latent semantic indexing Text-mining Job market analysis web data extraction

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OXPath-based data Acquisition for dblp 17

OXPath-based Data Acquisition for dblp

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ACM/IEEE Joint Conference on Digital Libraries (JCDL)

作者： Michels, Christopher Fayzrakhmanov, Ruslan R. Ley, Michael Sallinger, Emanuel Schenkel, Ralf Trier Univ Dblp Trier Germany Univ Oxford Oxford England

ISBN: (纸本)9781538638613

We demonstrate how the contemporary problems of data acquisition for dblp can be tackled with OXPath. It enables web data extraction and wrapper maintenance for heterogeneous data sources on a simple declarative level. Its features render it a feasible instrument to retrieve the varying and changing web representations of the prototypical substructures in the bibliographic domain.

关键词： Bibliography dblp digital libraries OXPath web data extraction

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A Method for Querying Touristic Information Extracted from the web 22nd

A Method for Querying Touristic Information Extracted from t...

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22nd International Conference on Applications of Natural Language to Information Systems (NLDB)

作者： Oro, Ermelinda Ruffolo, Massimo Natl Res Council CNR Via P Bucci 7-11C I-87036 Arcavacata Di Rende Italy

ISBN: (纸本)9783319595696;9783319595689

In this paper, we define a method that enables to: (i) exploit ontologies and terminology to represent concepts into specific domains, (ii) extract information from deep web pages, and opinions from social and community networks, (iii) semantically query information by using natural language interface. The method has been applied to query the touristic domain. This use case has shown the effectiveness of the method that can be easily extended to other domains.

关键词： Natural language interface web data extraction Ontology Linked data Question answering Tourism domain

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Synthesis of Forgiving data Extractors 17

Synthesis of Forgiving Data Extractors

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10th ACM International Conference on web Search and data Mining (WSDM)

作者： Omari, Adi Shoham, Sharon Yahav, Eran Technion Haifa Israel Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781450346757

We address the problem of synthesizing a robust data-extractor from a family of websites that contain the same kind of information. This problem is common when trying to aggregate information from many web sites, for example, when extracting information for a price-comparison site. Given a set of example annotated web pages from multiple sites in a family, our goal is to synthesize a robust data extractor that performs well on all sites in the family (not only on the provided example pages). The main challenge is the need to trade off precision for generality and robustness. Our key contribution is the introduction of forgiving extractors that dynamically adjust their precision to handle structural changes, without sacrificing precision on the training set. Our approach uses decision tree learning to create a generalized extractor and converts it into a forgiving extractor, in the form of an XPath query. The forgiving extractor captures a series of pruned decision trees with monotonically decreasing precision, and monotonically increasing recall, and dynamically adjusts precision to guarantee sufficient recall. We have implemented our approach in a tool called TREEX and applied it to synthesize extractors for real-world large scale web sites. We evaluate the robustness and generality of the forgiving extractors by evaluating their precision and recall on: (i) different pages from sites in the training set (ii) pages from different versions of sites in the training set (iii) pages from different (unseen) sites. We compare the results of our synthesized extractor to those of classifier-based extractors, and pattern-based extractors, and show that TREEX significantly improves extraction accuracy.

关键词： data mining wrappers wrapper induction data extraction web data extraction

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A Bottom-up Approach of web data extraction based on Entity Recognition and Integration

A Bottom-up Approach of Web Data Extraction based on Entity ...

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第8届全国web信息系统及应用学术会议

作者： Tong Liu Derong Shen Jing Shan Tiezheng Nie Yue Kou College of Information Science and Engineering Northeastern University Shenyang China

Nowadays, most popular methods for web data extraction (WDE) are top-down ones depending on structure. However, these techniques are not scalable enough when coming to complex pages. Consequently, we put forward a bottom-up approach for WDE based on entity recognition and integration to avoid over dependency to structure of web pages. The approach proposed focuses on primary text sequences labeling first and also gives consideration to repetitive patterns of them as well. We propose a Two-Level extraction model for entity recognition and repetitive pattern extraction algorithm for entity integration. Our approach can effectively reduce the attribute labeling mistakes. Also, we demonstrate our approach by scientifically experimental results. The conclusion is that our approach perform better than the traditional extraction techniques, especially on complex web pages.

关键词： web data extraction entity recognition entity integration bottom-up

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AutoRM: An effective approach for automatic web data record mining

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KNOWLEDGE-BASED SYSTEMS 2015年 89卷 314-331页

作者： Shi, Shengsheng Liu, Chengfei Shen, Yi Yuan, Chunfeng Huang, Yihua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Jiangsu Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic Australia

A web database typically responds to a query with a web page, which encodes the query results into semi-structured data objects using HTML tags. We call such data objects web data records or data records. Mining web data records is very important for many applications, e.g., meta search, comparative shopping, etc. This paper proposes a new effective approach called AutoRM, which mines data records from single web page automatically. AutoRM involves three major steps: (1) constructing the DOM tree of the given web page;(2) mining all sets of adjacent similar C-Records (Candidate data Records) from the constructed DOM tree;(3) mining actual data records from C-Records. In many web pages, similar data records are distributed in bigger and adjacent similar objects. Existing approaches typically identify such objects as data records. Conversely, AutoRM views such objects as C-Records, and mines actual data records from them. One key issue for mining similar data records is the boundary detection of each data record. Existing approaches typically make some brittle assumptions for handling this issue. By making more robust assumptions, AutoRM tends to detect data record boundaries more accurately. Experimental results show that AutoRM is highly effective, and outperforms state-of-the-art approaches. (C) 2015 Elsevier B.V. All rights reserved.

关键词： web data extraction web mining data record mining

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Content-based Title extraction from web Page 12

Content-based Title Extraction from Web Page

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12th International Conference on web Information Systems and Technologies (webIST)

作者： Gali, Najlah Franti, Pasi Univ Eastern Finland Sch Comp Speech & Image Proc Unit Joensuu Finland

ISBN: (纸本)9789897581861

web pages are usually designed in a presentation oriented fashion, having therefore a large amount of non-informative data such as navigation banners, advertisement and functional text. For a particular user, only informative data such as title, main content, and representative images are considered useful. Existing methods for title extraction rely on the structural and visual features of the web page. In this paper, we propose a simpler, but more effective method by analysing the content of the title and meta tags in respect to the main body of the page. We segment the title and meta tags using a set of predefined delimiters and score the segments using three criteria: placement in tag, popularity within all header tags in the page, and the position in the link of the web page. The method is fully automated, template independent, and not limited to any certain type of web pages. Experimental results show that the method significantly improves the accuracy (average similarity to the ground truth title) from 62 % to 84 %.

关键词： Title extraction Information extraction web data extraction web Mining

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AFIS: Aligning Detail-Pages for Full Schema Induction

AFIS: Aligning Detail-Pages for Full Schema Induction

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Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Yuliana, Oviliani Yenty Chang, Chia-Hui Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan

ISBN: (纸本)9781509057320

web data extraction is an essential task for web data integration. Most researches focus on data extraction from list-pages by detecting data-rich section and record boundary segmentation. However, in detail-pages which contain all-inclusive product information in each page, so the number of data attributes need to be aligned is much larger. In this paper, we formulate data extraction problem as alignment of leaf nodes from DOM Trees. We propose AFIS, Annotation-Free Induction of Full Schema for detail pages in this paper. AFIS applies Divide-and-Conquer and Longest Increasing Sequence (LIS) algorithms to mine landmarks from input. The experiments show that AFIS outperforms RoadRunner, FivaTech and TEX (F10.990) in terms of selected data. For full schema evaluation (all data), AFIS also represents the highest average performance (F10.937) compared with TEX and RoadRunner.

关键词： web data extraction semi-structured data detail-pages alignment divide-conquer alignment landmark equivalence class

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Method for Fast and Accurate extraction of Key Information from webpages 23

Method for Fast and Accurate Extraction of Key Information f...

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IEEE 23rd International Conference on web Services (ICWS)

作者： Kasi, Sainath Gadhamsetty Tripathi, Samarth Samsung Res & Dev Inst Web Serv Bangalore Karnataka India

ISBN: (纸本)9781509026753

As the World Wide web continues to grow unbounded;users expect intelligent processing and accurate coverage of all its domains. To allow for the same, we present a novel approach to identify and extract key information from web pages with commendable accuracy. We extract important information such as the Title, Main Image, Description, Keywords and FavIcon from a webpage where available, using only the HTML responses without any explicit webpage rendering. The algorithm was modelled to be fast without compromising on its accuracy, is fully automatic, language independent and runs without any human supervision or training. We test our algorithm extensively on over one hundred thousand webpages and successfully extract the key information for 97% of them with an impressive average extraction time of less than 500 milliseconds per webpage.

关键词： web mining web data extraction web Services Mobile web

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