The ability to continuously discover domain-specific content from the Web is critical for many applications. While focused crawling strategies have been shown to be effective for discovery, configuring a focused crawler is difficult and time-consuming. Given a domain of interest $D$, subject-matter experts (SMEs) must search for relevant websites and collect a set of representative Web pages to serve as training examples for creating a classifier that recognizes pages in $D$, as well as a set of pages to seed the crawl. In this paper, we propose DISCO, an approach designed to bootstrap domain-specific search. Given a small set of websites, DISCO aims to discover a large collection of relevant websites. DISCO uses a ranking-based framework that mimics the way users search for information on the Web: it iteratively discovers new pages, distills, and ranks them. It also applies multiple discovery strategies, including keyword-based and related queries issued to search engines, backward and forward crawling. By systematically combining these strategies, DISCO is able to attain high harvest rates and coverage for a variety of domains. We perform extensive experiments in four social-good domains, using data gathered by SMEs in the respective domains, and show that our approach is effective and outperforms state-of-the-art methods.
PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Oak Ridge National Laboratory（橡树岭国家实验室）；Fayetteville State University（费耶特维尔州立大学）；North Carolina State University（北卡罗来纳州立大学）
Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design
Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier
Heritage Institute of Technology（遗产技术学院）；Institute of Engineering & Management（工程与管理学院）
Web 2.0的出现导致了Web中可用的情感内容数量的增加。这样的内容通常以电影或产品评论、用户评论、推荐、论坛中的消息等形式出现在社交媒体网站中。及时发现情感或固执己见的网络内容具有许多优点，其中最重要的是货币化。了解大众对不同实体和产品的情感，可以为上下文广告、推荐系统和市场趋势分析提供更好的服务。我们项目的重点是情感集中的网络爬行框架，以方便快速发现电影评论和酒店评论的情感内容，并进行分析。我们使用统计方法来捕捉主观风格和句子极性的元素。本文详细讨论了两种有监督的机器学习算法：K-最近邻（K-NN）和朴素贝叶斯（Naive Bayes），并比较了它们的总体准确率、精确率和召回率。可以看出，在电影评论的情况下，朴素贝叶斯给出了比K-NN好得多的结果，但对于酒店评论，这些算法给出了较少的，几乎相同的准确性。
