# Web Data Mining and Analysis

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/web-data-mining-and-analysis/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on techniques and technologies for extracting structured data from web pages, including web crawling, automatic wrapper generation, page segmentation, and mining data records. It also covers topics related to the hidden web, information retrieval, and content adaptation for different devices. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t12016 |
| Works | 36 |

## Topic papers all

Showing 15 of 36.

- [In Google We Trust: Users’ Decisions on Rank, Position, and Relevance](https://scholariq.org/papers/in-google-we-trust-users-decisions-on-rank-position-and-relevance/)
- [Eye-tracking analysis of user behavior in WWW search](https://scholariq.org/papers/eye-tracking-analysis-of-user-behavior-in-www-search/)
- [Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases](https://scholariq.org/papers/google-scholar-to-overshadow-them-all-comparing-the-sizes-of-12-academic-search/)
- [Learning to extract symbolic knowledge from the World Wide Web](https://scholariq.org/papers/learning-to-extract-symbolic-knowledge-from-the-world-wide-web/)
- [Learning to construct knowledge bases from the World Wide Web](https://scholariq.org/papers/learning-to-construct-knowledge-bases-from-the-world-wide-web/)
- [Data Mining of User Navigation Patterns](https://scholariq.org/papers/data-mining-of-user-navigation-patterns/)
- [Pyramid: A Layered Model for Nested Named Entity Recognition](https://scholariq.org/papers/pyramid-a-layered-model-for-nested-named-entity-recognition/)
- [The tangled Web we wove](https://scholariq.org/papers/the-tangled-web-we-wove/)
- [Sumblr](https://scholariq.org/papers/sumblr/)
- [Overview of the INEX 2009 Entity Ranking Track](https://scholariq.org/papers/overview-of-the-inex-2009-entity-ranking-track/)
- [A Review of Feature Extraction in Sentiment Analysis](https://scholariq.org/papers/a-review-of-feature-extraction-in-sentiment-analysis/)
- [Discriminative and Correlative Partial Multi-Label Learning](https://scholariq.org/papers/discriminative-and-correlative-partial-multi-label-learning/)
- [An exploratory study of Google Scholar](https://scholariq.org/papers/an-exploratory-study-of-google-scholar/)
- [Exploring the academic invisible web](https://scholariq.org/papers/exploring-the-academic-invisible-web/)
- [Zipf's Law for Web Surfers](https://scholariq.org/papers/zipf-s-law-for-web-surfers/)

## Topic primary papers

- [An exploratory study of Google Scholar](https://scholariq.org/papers/an-exploratory-study-of-google-scholar/)
- [Exploring the academic invisible web](https://scholariq.org/papers/exploring-the-academic-invisible-web/)
- [JRC: A Job Post and Resume Classification System for Online Recruitment](https://scholariq.org/papers/jrc-a-job-post-and-resume-classification-system-for-online-recruitment/)
- [A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents](https://scholariq.org/papers/a-benchmark-of-pdf-information-extraction-tools-using-a-multi-task-and-multi/)
- [Enterprise Collaboration Framework for Managing, Advancing and Unifying the Functionality of Multiple Cloud-Based Services with the Help of a Graph API](https://scholariq.org/papers/enterprise-collaboration-framework-for-managing-advancing-and-unifying-the/)
- [A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing](https://scholariq.org/papers/a-neoteric-web-recommender-system-based-on-approach-of-mining-frequent/)
- [Main Content Extraction from Web Pages](https://scholariq.org/papers/main-content-extraction-from-web-pages/)
- [Bug Triaging Automation using Text Processing and Machine Learning Techniques](https://scholariq.org/papers/bug-triaging-automation-using-text-processing-and-machine-learning-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.