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An Analyst-Adaptive Approach to Focused Crawlers

机译:专注爬行者的分析师自适应方法

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摘要

The paper presents a general methodology to implement a flexible Focused Crawler for investigation purposes, monitoring, and Open Source Intelligence (OSINT). The resulting tool is specifically aimed to fit the operational requirements of law-enforcement agencies and intelligence analyst. The architecture of the semantic Focused Crawler features static flexibility in the definition of desired concepts, used metrics, and crawling strategy; in addition, the method is capable to learn (and adapt to) the analyst's expectations at runtime . The user may instruct the crawler with a binary feedback (yes/no) about the current performance of the surfing process, and the crawling engine progressively refines the expected targets accordingly. The method implementation is based on an existing text-mining environment, integrated with semantic networks and ontologies. Experimental results witness the effectiveness of the adaptive mechanism.
机译:本文提出了一种普遍的方法,用于实施灵活的聚焦履带,用于调查目的,监测和开源智能(Osint)。由此产生的工具专门旨在符合执法机构和情报分析师的运营要求。语义聚焦履带的架构在所需概念,使用指标和爬行策略的定义中具有静态灵活性;此外,该方法能够学习(并适应)分析师在运行时的期望。用户可以指示履带的二进制反馈(是/否)关于冲浪处理的当前性能,并且爬网发动机相应地逐渐地改进预期目标。该方法实现基于现有的文本挖掘环境,与语义网络和本体集成。实验结果见证了自适应机制的有效性。

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