摘要翻译：
大多数web用户的需求是搜索或导航时间以及得到正确匹配的结果。这些约束可以通过附加到现有搜索引擎和web服务器上的一些附加模块来满足。本文从所使用的方法出发，提出了一种强大的搜索引擎体系结构，命名为概率语义Web挖掘。随着万维网(WWW)上收集的各种数据资源越来越多，Web挖掘已经成为Web用户最重要的需求之一。Web服务器会存储各种格式的数据，包括文本、图像、音频、视频等，但服务器无法识别这些数据的内容。这些搜索技术可以通过添加一些特殊的技术来改进，包括语义web挖掘和概率分析，以获得更准确的结果。语义web挖掘技术通过在挖掘过程中消除无用信息，为数据资源提供有意义的搜索。在这种技术中，web服务器将维护特定web服务器中可用的每一个数据资源的元信息。这将有助于搜索引擎检索与用户给定输入字符串相关的信息。本文提出将语义web挖掘和概率分析这两种技术结合起来，实现高效准确的web挖掘搜索结果。SPF可以通过考虑输入字符串数据的语义准确性和句法准确性来计算。这将是产生结果的决定性因素。
---
英文标题：
《Probabilistic Semantic Web Mining Using Artificial Neural Analysis》
---
作者：
T.Krishna Kishore, T.Sasi Vardhan, N.Lakshmi Narayana
---
最新提交年份：
2010
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  Most of the web user's requirements are search or navigation time and getting correctly matched result. These constrains can be satisfied with some additional modules attached to the existing search engines and web servers. This paper proposes that powerful architecture for search engines with the title of Probabilistic Semantic Web Mining named from the methods used. With the increase of larger and larger collection of various data resources on the World Wide Web (WWW), Web Mining has become one of the most important requirements for the web users. Web servers will store various formats of data including text, image, audio, video etc., but servers can not identify the contents of the data. These search techniques can be improved by adding some special techniques including semantic web mining and probabilistic analysis to get more accurate results. Semantic web mining technique can provide meaningful search of data resources by eliminating useless information with mining process. In this technique web servers will maintain Meta information of each and every data resources available in that particular web server. This will help the search engine to retrieve information that is relevant to user given input string. This paper proposing the idea of combing these two techniques Semantic web mining and Probabilistic analysis for efficient and accurate search results of web mining. SPF can be calculated by considering both semantic accuracy and syntactic accuracy of data with the input string. This will be the deciding factor for producing results.
---
PDF链接：
https://arxiv.org/pdf/1004.1794