What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors

"© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PUBLICATION, {VOL 13, ISS 2, (APR 2019)} http://doi.acm.org/10.1145/3316810"[EN] A Web template is a resource that implements the structure and format of a website, making it ready for plugging content into already formatted and prepared pages. For this reason, templates are one of the main development resources for website engineers, because they increase productivity. Templates are also useful for the final user, because they provide uniformity and a common look and feel for all webpages. However, from the point of view of crawlers and indexers, templates are an important problem, because templates usually contain irrelevant information, such as advertisements, menus, and banners. Processing and storing this information leads to a waste of resources (storage space, bandwidth, etc.). It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks. There exist many techniques and tools for template extraction, but, unfortunately, it is not clear at all which template extractor should a user/system use, because they have never been compared, and because they present different (complementary) features such as precision, recall, and efficiency. In this work, we compare the most advanced template extractors. We implemented and evaluated five of the most advanced template extractors in the literature. To compare all of them, we implemented a workbench, where they have been integrated and evaluated. Thanks to this workbench, we can provide a fair empirical comparison of all methods using the same benchmarks, technology, implementation language, and evaluation criteria.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Ciencia, Innovacion y Universidades/AEI under grant TIN2016-76843-C4-1-R and by the Generalitat Valenciana under grants PROMETEO-II/2015/013 (SmartLogic) and Prometeo/2019/098 (DeepTrust).Alarte, J.; Silva, J.; Tamarit Muñoz, S. (2019). What Web Template Extractor Should I Use? A Benchmarking and Comparison for Five Template Extractors. ACM Transactions on the Web. 13(2):9:1-9:19. https://doi.org/10.1145/3316810S9:19:19132Alarte, J., Insa, D., Silva, J., & Tamarit, S. (2015). TeMex. Proceedings of the 24th International Conference on World Wide Web - WWW ’15 Companion. doi:10.1145/2740908.2742835Julián Alarte David Insa Josep Silva and Salvador Tamarit. 2016. Site-Level Web Template Extraction Based on DOM Analysis. 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An XJML-based wrapper generator for Web information extraction. Proceedings of the 1999 ACM SIGMOD international conference on Management of data - SIGMOD ’99. doi:10.1145/304182.304570Ma, L., Goharian, N., Chowdhury, A., & Chung, M. (2003). Extracting unstructured data from template generated web documents. Proceedings of the twelfth international conference on Information and knowledge management - CIKM ’03. doi:10.1145/956863.956961Manjula, R., & Chilambuchelvan, A. (2013). Extracting templates from Web pages. 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE). doi:10.1109/icgce.2013.6823541Christopher D. Manning Prabhakar Raghavan and Hinrich SchÃijtze. 2008. Introduction to Information Retrieval. Cambridge University Press New York NY. Christopher D. Manning Prabhakar Raghavan and Hinrich SchÃijtze. 2008. Introduction to Information Retrieval. Cambridge University Press New York NY.Meng, X., Hu, D., & Li, C. (2003). Schema-guided wrapper maintenance for web-data extraction. Proceedings of the fifth ACM international workshop on Web information and data management - WIDM ’03. doi:10.1145/956699.956701Nguyen, D. Q., Nguyen, D. Q., Pham, S. B., & Bui, T. D. (2009). A Fast Template-Based Approach to Automatically Identify Primary Text Content of a Web Page. 2009 International Conference on Knowledge and Systems Engineering. doi:10.1109/kse.2009.39Schäfer, R. (2016). Accurate and efficient general-purpose boilerplate detection for crawled web corpora. Language Resources and Evaluation, 51(3), 873-889. doi:10.1007/s10579-016-9359-2Sivakumar, P. (2015). Effectual Web Content Mining using Noise Removal from Web Pages. Wireless Personal Communications, 84(1), 99-121. doi:10.1007/s11277-015-2596-7Song, D., Sun, F., & Liao, L. (2013). A hybrid approach for content extraction with text density and visual importance of DOM nodes. Knowledge and Information Systems, 42(1), 75-96. doi:10.1007/s10115-013-0687-xR. 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Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM ’06. doi:10.1145/1183614.1183654Thijs Vogels Octavian-Eugen Ganea and Carsten Eickhoff. 2018. Web2Text: Deep structured boilerplate removal. CoRR abs/1801.02607 (2018). Retrieved from http://arxiv.org/abs/1801.02607. Thijs Vogels Octavian-Eugen Ganea and Carsten Eickhoff. 2018. Web2Text: Deep structured boilerplate removal. CoRR abs/1801.02607 (2018). Retrieved from http://arxiv.org/abs/1801.02607.Wang, Y., Fang, B., Cheng, X., Guo, L., & Xu, H. (2008). Incremental web page template detection. Proceeding of the 17th international conference on World Wide Web - WWW ’08. doi:10.1145/1367497.1367749Yi, L., Liu, B., & Li, X. (2003). Eliminating noisy information in Web pages for data mining. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’03. doi:10.1145/956750.956785Zheng, S., Song, R., Wen, J.-R., & Giles, C. L. (2009). 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Concurrent software architectures for exploratory data analysis

Decades ago, increased volume of data made manual analysis obsolete and prompted the use of computational tools with interactive user interfaces and rich palette of data visualizations. Yet their classic, desktop-based architectures can no longer cope with the ever-growing size and complexity of data. Next-generation systems for explorative data analysis will be developed on client–server architectures, which already run concurrent software for data analytics but are not tailored to for an engaged, interactive analysis of data and models. In explorative data analysis, the key is the responsiveness of the system and prompt construction of interactive visualizations that can guide the users to uncover interesting data patterns. In this study, we review the current software architectures for distributed data analysis and propose a list of features to be included in the next generation frameworks for exploratory data analysis. The new generation of tools for explorative data analysis will need to address integrated data storage and processing, fast prototyping of data analysis pipelines supported by machine-proposed analysis workflows, preemptive analysis of data, interactivity, and user interfaces for intelligent data visualizations. The systems will rely on a mixture of concurrent software architectures to meet the challenge of seamless integration of explorative data interfaces at client site with management of concurrent data mining procedures on the servers

A New Approach to Communications Management Planning Through 3D Web and Semantic Web Technologies

Communication management plans are used to determine not only who needs what information but also how that information will be collected and transmitted. Now two evolving technologies are looking to drive project planners to develop new approaches and methods for planning communications in the coming years. The first of these technologies, the Semantic Web, is becoming a driving force in how computers are making web content available to its users. The second technology, Web three-dimensional (3D) focuses on web-based content presentation by providing a rich 3D Web-centric environment for users to access information and interact with other users. This effort discusses the advent of the Semantic Web and Web 3D technologies and identifies many of the new planning considerations driving project information collection and analysis. The planning considerations for these two technologies are also discussed to aid in the framing of a new approach to project communications planning

Building and exploiting context on the web

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Methods and tools for causal discovery and causal inference

Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

Study of result presentation and interaction for aggregated search

The World Wide Web has always attracted researchers and commercial search engine companies due to the enormous amount of information available on it. "Searching" on web has become an integral part of today's world, and many people rely on it when looking for information. The amount and the diversity of information available on the Web has also increased dramatically. Due to which, the researchers and the search engine companies are making constant efforts in order to make this information accessible to the people effectively. Not only there is an increase in the amount and diversity of information available online, users are now often seeking information on broader topics. Users seeking information on broad topics, gather information from various information sources (e.g, image, video, news, blog, etc). For such information requests, not only web results but results from different document genre and multimedia contents are also becoming relevant. For instance, users' looking for information on "Glasgow" might be interested in web results about Glasgow, Map of Glasgow, Images of Glasgow, News of Glasgow, and so on. Aggregated search aims to provide access to this diverse information in a unified manner by aggregating results from different information sources on a single result page. Hence making information gathering process easier for broad topics. This thesis aims to explore the aggregated search from the users' perspective. The thesis first and foremost focuses on understanding and describing the phenomena related to the users' search process in the context of the aggregated search. The goal is to participate in building theories and in understanding constraints, as well as providing insights into the interface design space. In building this understanding, the thesis focuses on the click-behavior, information need, source relevance, dynamics of search intents. The understanding comes partly from conducting users studies and, from analyzing search engine log data. While the thematic (or topical) relevance of documents is important, this thesis argues that the "source type" (source-orientation) may also be an important dimension in the relevance space for investigating in aggregated search. Therefore, relevance is multi-dimensional (topical and source-orientated) within the context of aggregated search. Results from the study suggest that the effect of the source-orientation was a significant factor in an aggregated search scenario. Hence adds another dimension to the relevance space within the aggregated search scenario. The thesis further presents an effective method which combines rule base and machine learning techniques to identify source-orientation behind a user query. Furthermore, after analyzing log-data from a search engine company and conducting user study experiments, several design issues that may arise with respect to the aggregated search interface are identified. In order to address these issues, suitable design guidelines that can be beneficial from the interface perspective are also suggested. To conclude, aim of this thesis is to explore the emerging aggregated search from users' perspective, since it is a very important for front-end technologies. An additional goal is to provide empirical evidence for influence of aggregated search on users searching behavior, and identify some of the key challenges of aggregated search. During this work several aspects of aggregated search will be uncovered. Furthermore, this thesis will provide a foundations for future research in aggregated search and will highlight the potential research directions

Improving National and Homeland Security through a proposed Laboratory for Information Globalization and Harmonization Technologies (LIGHT)

A recent National Research Council study found that: "Although there are many private and public databases that contain information potentially relevant to counter terrorism programs, they lack the necessary context definitions (i.e., metadata) and access tools to enable interoperation with other databases and the extraction of meaningful and timely information" [NRC02, p.304, emphasis added] That sentence succinctly describes the objectives of this project. Improved access and use of information are essential to better identify and anticipate threats, protect against and respond to threats, and enhance national and homeland security (NHS), as well as other national priority areas, such as Economic Prosperity and a Vibrant Civil Society (ECS) and Advances in Science and Engineering (ASE). This project focuses on the creation and contributions of a Laboratory for Information Globalization and Harmonization Technologies (LIGHT) with two interrelated goals: (1) Theory and Technologies: To research, design, develop, test, and implement theory and technologies for improving the reliability, quality, and responsiveness of automated mechanisms for reasoning and resolving semantic differences that hinder the rapid and effective integration (int) of systems and data (dmc) across multiple autonomous sources, and the use of that information by public and private agencies involved in national and homeland security and the other national priority areas involving complex and interdependent social systems (soc). This work builds on our research on the COntext INterchange (COIN) project, which focused on the integration of diverse distributed heterogeneous information sources using ontologies, databases, context mediation algorithms, and wrapper technologies to overcome information representational conflicts. The COIN approach makes it substantially easier and more transparent for individual receivers (e.g., applications, users) to access and exploit distributed sources. Receivers specify their desired context to reduce ambiguities in the interpretation of information coming from heterogeneous sources. This approach significantly reduces the overhead involved in the integration of multiple sources, improves data quality, increases the speed of integration, and simplifies maintenance in an environment of changing source and receiver context - which will lead to an effective and novel distributed information grid infrastructure. This research also builds on our Global System for Sustainable Development (GSSD), an Internet platform for information generation, provision, and integration of multiple domains, regions, languages, and epistemologies relevant to international relations and national security. (2) National Priority Studies: To experiment with and test the developed theory and technologies on practical problems of data integration in national priority areas. Particular focus will be on national and homeland security, including data sources about conflict and war, modes of instability and threat, international and regional demographic, economic, and military statistics, money flows, and contextualizing terrorism defense and response. Although LIGHT will leverage the results of our successful prior research projects, this will be the first research effort to simultaneously and effectively address ontological and temporal information conflicts as well as dramatically enhance information quality. Addressing problems of national priorities in such rapidly changing complex environments requires extraction of observations from disparate sources, using different interpretations, at different points in times, for different purposes, with different biases, and for a wide range of different uses and users. This research will focus on integrating information both over individual domains and across multiple domains. Another innovation is the concept and implementation of Collaborative Domain Spaces (CDS), within which applications in a common domain can share, analyze, modify, and develop information. Applications also can span multiple domains via Linked CDSs. The PIs have considerable experience with these research areas and the organization and management of such large scale international and diverse research projects. The PIs come from three different Schools at MIT: Management, Engineering, and Humanities, Arts & Social Sciences. The faculty and graduate students come from about a dozen nationalities and diverse ethnic, racial, and religious backgrounds. The currently identified external collaborators come from over 20 different organizations and many different countries, industrial as well as developing. Specific efforts are proposed to engage even more women, underrepresented minorities, and persons with disabilities. The anticipated results apply to any complex domain that relies on heterogeneous distributed data to address and resolve compelling problems. This initiative is supported by international collaborators from (a) scientific and research institutions, (b) business and industry, and (c) national and international agencies. Research products include: a System for Harmonized Information Processing (SHIP), a software platform, and diverse applications in research and education which are anticipated to significantly impact the way complex organizations, and society in general, understand and manage critical challenges in NHS, ECS, and ASE

Improving National and Homeland Security through a proposed Laboratory for nformation Globalization and Harmonization Technologies (LIGHT)

A recent National Research Council study found that: "Although there are many private and public databases that contain information potentially relevant to counter terrorism programs, they lack the necessary context definitions (i.e., metadata) and access tools to enable interoperation with other databases and the extraction of meaningful and timely information" [NRC02, p.304, emphasis added] That sentence succinctly describes the objectives of this project. Improved access and use of information are essential to better identify and anticipate threats, protect against and respond to threats, and enhance national and homeland security (NHS), as well as other national priority areas, such as Economic Prosperity and a Vibrant Civil Society (ECS) and Advances in Science and Engineering (ASE). This project focuses on the creation and contributions of a Laboratory for Information Globalization and Harmonization Technologies (LIGHT) with two interrelated goals: (1) Theory and Technologies: To research, design, develop, test, and implement theory and technologies for improving the reliability, quality, and responsiveness of automated mechanisms for reasoning and resolving semantic differences that hinder the rapid and effective integration (int) of systems and data (dmc) across multiple autonomous sources, and the use of that information by public and private agencies involved in national and homeland security and the other national priority areas involving complex and interdependent social systems (soc). This work builds on our research on the COntext INterchange (COIN) project, which focused on the integration of diverse distributed heterogeneous information sources using ontologies, databases, context mediation algorithms, and wrapper technologies to overcome information representational conflicts. The COIN approach makes it substantially easier and more transparent for individual receivers (e.g., applications, users) to access and exploit distributed sources. Receivers specify their desired context to reduce ambiguities in the interpretation of information coming from heterogeneous sources. This approach significantly reduces the overhead involved in the integration of multiple sources, improves data quality, increases the speed of integration, and simplifies maintenance in an environment of changing source and receiver context - which will lead to an effective and novel distributed information grid infrastructure. This research also builds on our Global System for Sustainable Development (GSSD), an Internet platform for information generation, provision, and integration of multiple domains, regions, languages, and epistemologies relevant to international relations and national security. (2) National Priority Studies: To experiment with and test the developed theory and technologies on practical problems of data integration in national priority areas. Particular focus will be on national and homeland security, including data sources about conflict and war, modes of instability and threat, international and regional demographic, economic, and military statistics, money flows, and contextualizing terrorism defense and response. Although LIGHT will leverage the results of our successful prior research projects, this will be the first research effort to simultaneously and effectively address ontological and temporal information conflicts as well as dramatically enhance information quality. Addressing problems of national priorities in such rapidly changing complex environments requires extraction of observations from disparate sources, using different interpretations, at different points in times, for different purposes, with different biases, and for a wide range of different uses and users. This research will focus on integrating information both over individual domains and across multiple domains. Another innovation is the concept and implementation of Collaborative Domain Spaces (CDS), within which applications in a common domain can share, analyze, modify, and develop information. Applications also can span multiple domains via Linked CDSs. The PIs have considerable experience with these research areas and the organization and management of such large scale international and diverse research projects. The PIs come from three different Schools at MIT: Management, Engineering, and Humanities, Arts & Social Sciences. The faculty and graduate students come from about a dozen nationalities and diverse ethnic, racial, and religious backgrounds. The currently identified external collaborators come from over 20 different organizations and many different countries, industrial as well as developing. Specific efforts are proposed to engage even more women, underrepresented minorities, and persons with disabilities. The anticipated results apply to any complex domain that relies on heterogeneous distributed data to address and resolve compelling problems. This initiative is supported by international collaborators from (a) scientific and research institutions, (b) business and industry, and (c) national and international agencies. Research products include: a System for Harmonized Information Processing (SHIP), a software platform, and diverse applications in research and education which are anticipated to significantly impact the way complex organizations, and society in general, understand and manage critical challenges in NHS, ECS, and ASE