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18th International Conference on Research Challenges in Information Sciences (RCIS)

作者： Chevallier, Zoe Kedad, Zoubida Finance, Beatrice Chaillan, Frederic Univ Versailles Paris Saclay DAVID Lab Versailles France Grand Paris Sud Evry France

ISBN: (纸本)9783031594670;9783031594687

The increasing number of RDF data sources published on the web represents an unprecedented amount of information. However, querying these sources to extract the relevant information for a specific need represented by a target schema is a complex task as the alignment between the target and the source schemas might not be provided or incomplete. This paper presents an approach which aims at automatically populating the classes of a target schema. Our approach relies on a semi-supervised learning algorithm that iteratively identifies instance patterns in the data source that represent candidate instances for the target schema. We present some preliminary experiments showing the effectiveness of our approach.

关键词： RDF data sources Target Schema Instantiation web data extraction Semi-supervised learning

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Generating SPARQL Queries for data Discovery 28th

Generating SPARQL Queries for Data Discovery

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28th European Conference on Advances in databases and Information Systems (ADBIS)

作者： Chevallier, Zoe Kedad, Zoubida Finance, Beatrice Chaillan, Frederic Univ Versailles Paris Saclay David Lab Versailles France Grand Paris Sud Evry France

ISBN: (纸本)9783031706288;9783031706264

The number of datasets available on the web represents a huge space of meaningful information. When done manually, identifying the relevant portion of the data for a specific need is time-consuming. This paper presents an approach that identifies and extracts the relevant portion of RDF data sources for some application requirements described by a target schema. The identification process is based on a semi-supervised learning algorithm that iteratively computes for each class of the target schema the similarity between source entities and both the class description and the candidate instance patterns already identified for this class. The extraction process generates SPARQL queries for the identified candidate instance patterns. We present some evaluations showing the effectiveness of our approach.

关键词： data discovery web data extraction Semi-Supervised Learning SPARQL Query Generation

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An Empirical Comparison of web Content extraction Algorithms 23

An Empirical Comparison of Web Content Extraction Algorithms

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46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Bevendorff, Janek Gupta, Sanket Kiesel, Johannes Stein, Benno Bauhaus Univ Weimar Weimar Germany

ISBN: (纸本)9781450394086

Main content extraction from web pages-sometimes also called boilerplate removal-has been a research topic for over two decades. Yet despite web pages being delivered in a machine-readable markup format, extracting the actual content is still a challenge today. Even with the latest HTML5 standard, which defines many semantic elements to mark content areas, web page authors do not always use semantic markup correctly or to its full potential, making it hard for automated systems to extract the relevant information. A high-precision, high-recall content extraction is crucial for downstream applications such as search engines, AI language tools, distraction-free reader modes in users' browsers, and other general assistive technologies. For such a fundamental task, however, surprisingly few openly available extraction systems or training and benchmarking datasets exist. Even less research has gone into the rigorous evaluation and a true apples-to-apples comparison of the few extraction systems that do exist. To get a better grasp on the current state of the art in the field, we combine and clean eight existing human-labeled web content extraction datasets. On the combined dataset, we evaluate 14 competitive main content extraction systems and five baseline approaches. Finally, we build three ensembles as new state-of-the-art extraction baselines. We find that the performance of existing systems is quite genre-dependent and no single extractor performs best on all types of web pages.

关键词： Main Content extraction Boilerplate Removal web data extraction

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Investigating the Country of Origin and the Role of the .eu TLD in External Trade of European Union Member States

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FUTURE INTERNET 2022年第6期14卷 174-174页

作者： Giannakoulopoulos, Andreas Pergantis, Minas Limniati, Laida Kouretsis, Alexandros Ionian Univ Dept Audio & Visual Arts 7 Tsirigoti Sq Kerkira 49100 Greece BrilliantPR Digital Agcy 340 Kifisias Str Athens 15451 Greece

The Internet, and specifically the World Wide web, has always been a useful tool in the effort to achieve more outward-looking economies. The launch of the .eu TLD (top-level domain) in December of 2005 introduced the concept of a pan-European Internet identity that aimed to enhance the status of European citizens and businesses on the global web. In this study, the countries of origin of websites that choose to use the .eu TLD are investigated and the reasoning behind that choice, as well as its relation to each country's economy and external trade are discussed. Using the web as a tool, information regarding a vast number of existing .eu websites was collected, through means of web data extraction, and this information was analyzed and processed by a detailed algorithm that produced results concerning each website's most probable country of origin based on a multitude of factors. This acquired knowledge was then used to investigate relations with each member-state's presence in its local ccTLD, its GDP and its external trade revenue. The study establishes a correlation between presence in the .eu TLD and external trade that is both independent of a country's GDP and stronger than the relation between its local ccTLD presence and external trade.

关键词： internet domains web data extraction e-commerce international trade European identity big data TLDs WorldWide web

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NEXIR: A Novel web extraction Rule Language toward a Three-Stage web data extraction Model

NEXIR: A Novel Web Extraction Rule Language toward a Three-S...

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14th International Conference on web Information Systems Engineering (WISE)

作者： Shi, Shengsheng Wei, Wu Liu, Yulong Wang, Haitao Luo, Lei Yuan, Chunfeng Huang, Yihua Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9783642412301;9783642412295

As the most popular information publishing platform, the web contains a lot of valued data information of interests to users or applications. Nowadays, although a lot of data mining or analysis techniques have been studied in last decade, there are still not many easy-to-use web data mining tools available for users to extract useful data information from the web. The web information extraction is a whole process involving web page navigation, data extraction and data integration. Unfortunately most of existing studies or systems lack of sufficient consideration toward the three-stage process. Also most of them lack the powerful rules to express the flexible extraction logic to extract data records with complicate structure. In this paper, we propose a novel web data extraction language, NEXIR, toward a three-stage web data extraction model. First of all, the language can define rules for system to automate the navigation process of the web pages, including deep web pages that need interactions from users. Then the language allows users to define flexible and complicated rules to extract data records from web pages and integrate extracted data into a pre-defined structure. A language engine and a prototype extraction system have been implemented based on the proposed language. The experimental results show that our language and system work effective and powerful compared with existing data extraction approaches.

关键词： web data extraction extraction Rule language data record web page navigation web data integration

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Firefly Optimization Algorithm Based web Scraping for web Citation extraction

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期118卷 1481-1505页

作者： Suganya, E. Vijayarani, S. Bharathiar Univ Dept Comp Sci Coimbatore Tamil Nadu India

web citation analysis is primarily used to examine the impact of an author, an article or a publication by counting the amount of intervals that has been cited by other authors. The significant goal of web citation analysis is helping the researchers to find their related papers for their further analysis. The references of the paper must be cited by the author, which is used to recognize the link among the previous relevant research. The citation provides the valuable information that directs the researchers to gain knowledge about the current trends and future developments and obtain new ideas in their respective fields. The citation's information are incorporate in the database called web citation database such as Google Scholar, web of Science, Scopus and so on. From the web citation database, extracting the user required information is very complex task. Most of the open source tools are available online but manual process is needed to select the user-required information from the web page. For instance if the user need author name and publisher from the web citation database, they required to choose the exact information tags manually in existing tools which consumes more time. To overcome this difficulty we proposed an algorithm Firefly Optimization Algorithm based web Scraping for web content extraction from web citation database. The primary purpose of this research is to extract author information extraction process extracts citation information published by an author, journal name, publisher, year and citation using web citation analysis. The user's query input will be the keyword for example big data, artificial intelligence, etc. The web citation information from multiple web pages using web crawling and web scraping techniques are applied for web citation information based on the user query and Particle Swarm Optimization, Hidden Markov Model are applied for finding the best solution from all the feasible solutions. Experiments illustrate the proposed

关键词： web data extraction Optimization Particle swarm optimization Hidden markov model Firefly optimization algorithm based web scraping

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Towards data extraction of Dynamic Content from JavaScript web Applications 32

Towards Data Extraction of Dynamic Content from JavaScript W...

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32nd International Conference on Information Networking (ICOIN)

作者： Prutsachainimmit, Korawit Nadee, Winai Prince Songkla Univ Coll Comp Phuket Thailand

ISBN: (纸本)9781538622902

An enormous data in World Wide web and social media has open opportunities for business and organization to get the significant value that leads to efficient operations. As a result, web data extraction has become an important tool for gathering and translating semi-structured documents into valuable information. However, one of the major challenges is dealing with changes from web documents, especially emerging of JavaScript web development technology that has significantly affected the way to embed and rendering data of web pages. In this paper, we propose a design and implementation of a new web data extraction system that aims for extracting data from JavaScript web applications. The proposed system enables users to select valuable data from online web documents by defining data extraction rules and data transformation patterns. The extraction engine automatically scrapes and transforms semi-structure data into relational data. The preliminary evaluation results showed that our proposed system has successfully extract data from modern JavaScript web applications.

关键词： Information retrieval web data extraction JavaScript web application JSON

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web Page Representations and data extraction with BERyL 18th

Web Page Representations and Data Extraction with BERyL

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18th International Conference on web Engineering (ICWE)

作者： Kravchenko, Andrey Fayzrakhmanov, Ruslan R. Sallinger, Emanuel Univ Oxford Dept Comp Sci Wolfson BldgParks Rd Oxford OX1 3QD England

ISBN: (纸本)9783030030568;9783030030551

The web contains a huge amount of data, which can be primarily accessed with the use of web data extraction technology. With increasing complexity of the web development stack and the source code, a web page visual representation rendered by the browser is often the only source reflecting the semantics, functional role, and logical structure of elements. Thus, modern automatic approaches typically target visual cues and structures (e.g., DOM and CSSOM) constructed by the web browser. In this paper, we briefly analyse different representations of web pages, generic approaches, and introduce beryl, a novel framework and language, which can consolidate two "worlds", two main approaches: the rule-based approach and machine learning. The rule-based approach is used for feature engineering and pattern recognition, whilst machine learning is used for classification based on the inferred features. This is achieved through three stages including (1) feature computation, pattern construction, and application, (2) machine learning, and (3) refinement.

关键词： web data extraction Reasoning Machine learning

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A regular expression generator based on CSS selectors for efficient extraction from HTML pages

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2020年第6期28卷 3389-3401页

作者： Uzun, Erdinc Tekirda Namk Kemal Univ Corlu Fac Engn Dept Comp Engn Tekirda Turkey

Cascading style sheets (CSS) selectors are patterns used to select HTML elements. They are often preferred in web data extraction because they are easy to prepare and have short expressions. In order to be able to extract data from web pages by using these patterns, a document object model (DOM) tree is constructed by an HTML parser for a web page. The construction process of this tree and the extraction process using this tree increase time and memory costs depending on the number of HTML elements and their hierarchies. For reducing these costs, regular expressions can be considered as a solution. However, preparing regular expression patterns is a laborious task. In this study, a heuristic approach, namely Regex Generator (REGEXN), that automatically generates these patterns through CSS selectors is introduced and the performance gains are analyzed on a web crawler. The analysis shows that regular expression patterns generated by this approach can significantly reduce the average extraction time results from 743.31 ms to 1.03 ms when compared with the extraction process from a DOM tree. Similarly, the average memory usage drops from 1054.01 B to 1.59 B. Moreover, REGEXN can be easily adapted to the existing frameworks and tools in this task.

关键词： web data extraction computational efficiency regular expressions heuristic algorithms

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An Efficient Mechanism for Product data extraction from E-Commerce websites

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Computers, Materials & Continua 2020年第12期65卷 2639-2663页

作者： Malik Javed Akhtar Zahur Ahmad Rashid Amin Sultan H.Almotiri Mohammed A.Al Ghamdi Hamza Aldabbas University of Engineering and Technology TaxilaPakistan Computer Science Department Umm Al-Qura UniversityMakkah CitySaudi Arabia Al-Balqa Applied University Al-SaltJordan

A large amount of data is present on the web which can be used for useful purposes like a product recommendation,price comparison and demand forecasting for a particular *** are designed for human understanding and not for ***,to make data machine-readable,it requires techniques to grab data from web *** have addressed the problem using two approaches,i.e.,knowledge engineering and machine *** of the art knowledge engineering approaches use the structure of documents,visual cues,clustering of attributes of data records and text processing techniques to identify data records on a web *** learning approaches use annotated pages to learn *** rules are used to extract data from unseen web *** structure of web documents is continuously ***,new techniques are needed to handle the emerging requirements of web data *** this paper,we have presented a novel,simple and efficient technique to extract data from web pages using visual styles and structure of *** proposed technique detects Rich data Region(RDR)using query and correlative words of the *** is then divided into data records using style *** elements are removed using a Common Tag Sequence(CTS)and formatting *** system is implemented using JAVA and runs on the dataset of real-world working *** effectiveness of results is evaluated using precision,recall,and F-measure and compared with five existing systems.A comparison of the proposed technique to existing systems has shown encouraging results.

关键词： Document object model rich data region common tag sequence web data extraction deep web mining

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