A Progressive Clustering Algorithm to Group the XML Data by Structural and Semantic Similarity

Since the emergence in the popularity of XML for data representation and exchange over the Web, the distribution of XML documents has rapidly increased. It has become a challenge for researchers to turn these documents into a more useful information utility. In this paper, we introduce a novel clustering algorithm PCXSS that keeps the heterogeneous XML documents into various groups according to their similar structural and semantic representations. We develop a global criterion function CPSim that progressively measures the similarity between a XML document and existing clusters, ignoring the need to compute the similarity between two individual documents. The experimental analysis shows the method to be fast and accurate

Novel Method for Measuring Structure and Semantic Similarity of XML Documents Based on Extended Adjacency Matrix

AbstractSimilarity measurement of XML documents is crucial to meet various needs of approximate searches and document classifications in XML-oriented applications. Some methods have been proposed for this purpose. Nevertheless, few methods can be elegantly exploited to depict structure and semantic information and hence to effectively measure the similarity of XML documents. In this paper, we present a new method of computing the structure and semantic similarity of XML documents based on extended adjacency matrix(EAM). Different from a general adjacency matrix, in an EAM, the structure information of not only the adjacent layers but also the ancestor-descendant layers can be stored. For measuring the similarity of two XML documents, the proposed method firstly stores the structure and semantic information in two extended adjacency matrices(M1, M2). Then it computes similarity of the two documents through cos(M1, M2) Experimental results on bench-mark data show that the method holds high efficiency and accuracy

XML Data Retrieval Model Based on Two-dimensional Table Datasets

Retrieval problems of XML-based representation of data have been researched in this paper. In order to solve the large time and space overhead problem in building content index, this paper establish a data retrieval model advantageous to xml representation using the system automatically build two-dimensional table datasets. Take crop diseases and insect pests data for an example, this paper first gives the architecture of retrieval system based on XML crop diseases and insect pests' data; it also discusses about how to construct the two-dimensional table dataset and achieve the retrieval process; then it describes the text segmentation technique and the XSL style sheet conversion technology. Finally, under the VS.NET platform, using MVC design pattern develop and implement a prototype

Mining XML Documents

XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the increasing size of XML collections as information sources, mining techniques that traditionally exist for text collections or databases need to be adapted and new methods to be invented to exploit the particular structure of XML documents. Basically XML documents can be seen as trees, which are well known to be complex structures. This chapter describes various ways of using and simplifying this tree structure to model documents and support efficient mining algorithms. We focus on three mining tasks: classification and clustering which are standard for text collections; discovering of frequent tree structure which is especially important for heterogeneous collection. This chapter presents some recent approaches and algorithms to support these tasks together with experimental evaluation on a variety of large XML collections

Similarity of XML Data

Currently, XML is still more and more important format for storing and exchanging data. Evaluation of similarity of XML data plays a crucial role in efficient storing, processing and manipulating data. This work deals with possibility to evaluate similarity of DTDs. Firstly, suitable DTD tree representation is designed. Next, the algorithm based on tree edit distance technique is proposed. Finally, we are focusing on various aspects of similarity, such as, e.g., structural and linguistic information, and integrate them into our method.Jazyk XML se v dnešní době stává stále důležitějším formátem pro uchování a výměnu dat. Provnání podobnosti XML dat hraje zásadní roli v efektivním ukládání, zpracovávání a manipulaci s daty. Tato práce se zabývá možnostmi jak zjišťovat podobnost mezi DTD. Napřed je navržena vhodná reprezentace DTD stromů. Následně je navržen také algoritmus, který je založený na editační vzdálenosti stromů. Nakonec se zaměřujeme na různé aspekty podobnosti, jako jsou například strukturální a lingvistické informace, a snažíme se je zahrnout do naší metody.Department of Software EngineeringKatedra softwarového inženýrstvíFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Syntactic and Semantic Analysis and Visualization of Unstructured English Texts

People have complex thoughts, and they often express their thoughts with complex sentences using natural languages. This complexity may facilitate efficient communications among the audience with the same knowledge base. But on the other hand, for a different or new audience this composition becomes cumbersome to understand and analyze. Analysis of such compositions using syntactic or semantic measures is a challenging job and defines the base step for natural language processing. In this dissertation I explore and propose a number of new techniques to analyze and visualize the syntactic and semantic patterns of unstructured English texts. The syntactic analysis is done through a proposed visualization technique which categorizes and compares different English compositions based on their different reading complexity metrics. For the semantic analysis I use Latent Semantic Analysis (LSA) to analyze the hidden patterns in complex compositions. I have used this technique to analyze comments from a social visualization web site for detecting the irrelevant ones (e.g., spam). The patterns of collaborations are also studied through statistical analysis. Word sense disambiguation is used to figure out the correct sense of a word in a sentence or composition. Using textual similarity measure, based on the different word similarity measures and word sense disambiguation on collaborative text snippets from social collaborative environment, reveals a direction to untie the knots of complex hidden patterns of collaboration

Detecting Structural Similarities between XML Documents

In this paper we propose a technique for detecting the similarity in the structure of XML documents. The technique is based on the idea of representing the structure of an XML document as a time series in which each occurrence of a tag corresponds to a given impulse. By analyzing the frequencies of the corresponding Fourier transform, we can hence state the degree of similarity between documents. The e#ciency and e#ectiveness of this approach are compelling when compared with traditional ones

Finding Syntactic Similarities Between XML Documents

Detecting structural similarities between XML documents has been the subject of several recent work, and the proposed algorithms mostly use tree edit distance between the corresponding trees of XML documents. However, evaluating a tree edit distance is computationally expensive and does not easily scale up to large collections. We show in this paper that a tree edit distance computation often is not necessary and can be avoided. In particular, we propose a concise structural summary of XML documents and show that a comparison based on this summary is both fast and effective. Our experimental evaluation shows that this method does an excellent job of grouping documents generated by the same DTD, outperforming some of the previously proposed solutions based on a tree comparison. Furthermore, the time complexity of the algorithm is linear on the size of the structural description.