Discovery Knowledge and Data Mining from Structured Data

从结构化数据中发现知识和数据挖掘

• 批准号: 13680459
• 负责人: MIYAHARA Tetsuhiro
• 金额: $ 2.3万
• 依托单位: Hiroshima City University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2001
• 资助国家: 日本
• 起止时间: 2001 至 2003
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13680459/
• 关键词: data mining; knowledge discovery; graph-structured data; semistructured data; tree structured pattern; HTML; XML file; 帰納推論

The purpose of this research project is to give theoretical foundations of data mining systems from graph-structured data or tree-structured data. Recently, Web documents such as HTML files and XIML files have increased rapidly. Such Web documents have no rigid structure and are called semistructured data. In general, such semistructured Web documents are represented by rooted trees. We have proposed methods for discovering frequent tree structured patterns in semistructured Web documents by using a tag tree pattern as a hypothesis. A tag tree pattern is an edge labeled tree which has ordered or unordered children and structured variables. An edge label is a tag or a keyword in such Web documents, and a variable can be substituted by an arbitrary tree. So a tag tree pattern is suited for representing tree structured patterns in such Web documents. Information Extraction from semistructured data becomes more and more important. In order to extract meaningful or interesting contents from semistructured data, we need to extract common structured patterns from semistructured data. We have presented a method for extracting characteristic tag tree patterns from irregular semistructured data by using an algorithm for finding a minimally generalized tag tree pattern explaining given data. Also we have given various learning algorithms of term trees, which are tree structured patterns with structured variables, from tree structured data, since such learning algorithms give theoretical foundations of data mining from semistructured data.

本研究计画的目的是提供从图结构或树结构资料中进行资料探勘系统的理论基础。近来，诸如HTML文件和XIML文件的Web文档迅速增加。这种Web文档没有严格的结构，称为半结构化数据。一般来说，这种半结构化的Web文档表示的根树。我们已经提出了在半结构化Web文档中发现频繁的树结构模式的方法，通过使用标记树模式作为假设。标记树模式是一个边标记树，它有有序或无序的子树和结构化变量。边缘标签是这样的Web文档中的标记或关键字，并且变量可以由任意树代替。因此，标记树模式适合于表示此类Web文档中的树结构模式。从半结构化数据中提取信息变得越来越重要。为了从半结构化数据中提取有意义或有趣的内容，我们需要从半结构化数据中提取常见的结构化模式。我们已经提出了一种方法，用于提取特征标签树模式不规则的半结构化数据，通过使用一种算法，找到一个最小的广义标签树模式解释给定的数据。我们还给出了从树结构数据中学习术语树的各种学习算法，术语树是具有结构化变量的树结构模式，因为这些学习算法为从半结构化数据中进行数据挖掘提供了理论基础。

项目成果

Kazuyoshi Furukawa et al.: "Extracting characteristic structures among words in semistructured documents"Proc.PAKDD-2002, Lecture Notes in Artificial Intelligence, Springer-Verlag. 2336. 356-367 (2002)

Kazuyoshi Furukawa 等人：“提取半结构化文档中单词的特征结构”Proc.PAKDD-2002，人工智能讲座笔记，Springer-Verlag。

Yusuke Suzuki et al.: "Efficient Learning of Unlabeled Term Trees with Contractible Variables from Positive Data"Proc.ILP03, Lecture Notes in Artificial Intelligence, Springer-Verlag. 2835. 347-364 (2003)

Yusuke Suzuki 等人：“利用来自正数据的可收缩变量有效学习未标记术语树”Proc.ILP03，人工智能讲义，Springer-Verlag。

T.Miyahara, T.Shoudai, T.Uchida, K.Takahashi, H.Ueda: "Discovery of frequent tree structured patterns in semistructured web documents"Proceedings of the 5tg Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2001) (Springer-Verlag) Lect

T.Miyahara、T.Shoudai、T.Uchida、K.Takahashi、H.Ueda：“半结构化网络文档中频繁树结构模式的发现”第五届太平洋-亚洲知识发现和数据挖掘会议论文集（PAKDD-2001）

Yusuke Suzuki: "Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables."Proc.ALT03, Lecture Notes in Artificial Intelligence, Springer-Verlag. 2842. 114-128 (2003)

Yusuke Suzuki：“利用可收缩变量有效学习有序和无序树模式。”Proc.ALT03，人工智能讲义，Springer-Verlag。

Satoshi Matsumoto: "Learning of Finite Unions of Tree Patterns with Repeated Internal Structured Variables from Queries"Proc.ALT03, Lecture Notes in Artificial Intelligence, Springer-Verlag. 2842. 144-158 (2003)

Satoshi Matsumoto：“从查询中学习具有重复内部结构化变量的树模式的有限联合”Proc.ALT03，人工智能讲义，Springer-Verlag。