Web Documents Research Articles

Vertical Navigation of Layout Adapted Web Documents

With the development of mobile devices, digital televisions and game consoles, a vast amount of different client terminals become capable to browse the Internet. Content providers have to find adaptation solutions to serve all devices at the same time. This paper introduces a dynamic layout adaptation algorithm for web documents and an eXtensible Markup Language (XML) that supports layout adaptation. The presented layout adaptation language is based on existing World Wide Web Consortium (W3C) specifications, i.e., XFrames and CSS. Adaptation might results in several pages within one document. There is a need for a navigation method for browsing such documents. Therefore, we introduce a navigation method that supports paging. An implementation of the adaptation algorithm and the navigation method is also discussed in this paper.

Opinion Mining from Web Documents: Extraction and Structurization

The task of opinion extraction and structurization is the key component of opinion mining, which allow Web users to retrieve and summarize people's opinions scattered over the Internet. Our aim is to develop a method for extracting opinions that represent evaluation of concumer products in a structured form. To achieve the goal, we need to consider some issues that are relevant to the extraction task: How the task of opinion extraction and structurization should be designed, and how to extract the opinions which we defined. We define an opinion unit consisting of a quadruple, that is, the opinion holder, the subject being evaluated, the part or the attribute in which it is evaluated, and the evaluation that expresses positive or negative assessment. In this task, we focus on two subtasks (a) extracting subject/aspect-evaluation relations, and (b) extracting subject/aspect-aspect relations, we approach each extraction task using a machine learning-based method. In this paper, we discuss how customer reviews in web documents can be best structured. We also report on the results of our experiments and discuss future directions.

Opinion Mining from Web Documents: Extraction and Structurization

Opinion Mining from Web Documents: Extraction and Structurization

Evaluation of high-early strength PCC mixtures used in full depth repairs

The research described herein was conducted as a part of NCHRP Project 18-04B, Early Opening-to-Traffic Portland Cement Concrete for Pavement Rehabilitation [Van Dam TJ. Durability of ‘Early-Opening-to-Traffic’ portland cement concrete for pavement rehabilitation. Final Report, NCHRP Web Document 76, Transportation Research Board, National Research Council, Washington, DC; 2005.]. In the study fourteen different high-early strength portland cement concrete (PCC) full-depth repair mixtures (two replicates or batches were prepared for each mixture for a total of 28 batches) were designed for a 6–8 h opening to traffic window. The fresh and hardened concrete properties, freeze-thaw durability and microstructural properties of the concrete were assessed as part of the laboratory testing program. As a result of this assessment it was determined that high-early strength PCC mixtures of adequate strength could be produced, but that interactions between the various constituents could result in durability problems in some mixtures, particularly those made with Type III cement and high-range water reducers. It is recommended that durability-related testing be conducted on such mixtures to ensure longevity of the repair.

How Recent is a Web Document?

One of the most important aspects of a Web document is its up-to-dateness or recency. Up-to-dateness is particularly relevant to Web documents because they usually contain content origining from different sources and being refreshed at different dates. Whether a Web document is relevant for a reader depends on the history of its contents and so-called external factors, i.e., the up-to-dateness of semantically related documents.In this paper, we approach automatic management of up-to-dateness of Web documents that are managed by an XML-centric Web content management system. First, the freshness for a single document is computed, taking into account its change history. A document metric estimates the distance between different versions of a document. Second, up-to-dateness of a document is determined based on its own history and the historical evolutions of semantically related documents.

Checking Content Consistency of Integrated Web Documents

A conceptual framework for the specification and verification of constraints on the content and narrative structure of documents is proposed. As a specification formalism, CTLDL is defined, which is an extension of the temporal logic CTL by description logic concepts. In contrast to existing solutions this approach allows for the integration of ontologies to achieve interoperability and abstraction from implementation aspects of documents. This makes CTLDL specifically suitable for the integration of heterogeneous and distributed information resources in the semantic web.

A Language for Verification and Manipulation of Web Documents: (Extended Abstract)

In this paper we develop the language theory underpinning the logical framework PLF. This language features lambda abstraction with patterns and application via pattern-matching. Reductions are allowed in patterns. The framework is particularly suited as a metalanguage for encoding rewriting logics and logical systems where proof terms have a special syntactic constraints, as in term rewriting systems, and rule-based languages. PLF is a conservative extension of the well-known Edinburgh Logical Framework LF. Because of sophisticated pattern matching facilities PLF is suitable for verification and manipulation of HXML documents.

Modeling user interests by conceptual clustering

As more information becomes available on the Web, there has been a crescent interest in effective personalization techniques. Personal agents providing assistance based on the content of Web documents and the user interests emerged as a viable alternative to this problem. Provided that these agents rely on having knowledge about users contained into user profiles, i.e., models of user preferences and interests gathered by observation of user behavior, the capacity of acquiring and modeling user interest categories has become a critical component in personal agent design. User profiles have to summarize categories corresponding to diverse user information interests at different levels of abstraction in order to allow agents to decide on the relevance of new pieces of information. In accomplishing this goal, document clustering offers the advantage that an a priori knowledge of categories is not needed, therefore the categorization is completely unsupervised. In this paper we present a document clustering algorithm, named WebDCC (Web Document Conceptual Clustering), that carries out incremental, unsupervised concept learning over Web documents in order to acquire user profiles. Unlike most user profiling approaches, this algorithm offers comprehensible clustering solutions that can be easily interpreted and explored by both users and other agents. By extracting semantics from Web pages, this algorithm also produces intermediate results that can be finally integrated in a machine-understandable format such as an ontology. Empirical results of using this algorithm in the context of an intelligent Web search agent proved it can reach high levels of accuracy in suggesting Web pages.

Simple Classification into Large Topic Ontology of Web Documents

The paper presents an approach to classifying Web documents into large topic ontology. The main emphasis is on having a simple approach appropriate for handling a large ontology and providing it with enriched data by including additional information on the Web page context obtained from the link structure of the Web. The context is generated from the in-coming and out-going links of the Web document we want to classify (the target document), meaning that for representing a document we use, not only text of the document itself, but also the text from the documents pointing to the target document, as well as the text from the documents the target document is pointing to. The idea is that providing enriched data is compensating for the simplicity of the approach while keeping it efficient and capable of handling large topic ontology.

A Parallel Hybrid Web Document Clustering Algorithm and its Performance Study

Clustering web document is an important procedure in many web information retrieval systems. As the size of the Internet grows rapidly and the amount of information requests increases exponentially, the use of parallel computing techniques in large scale web document retrieval is unavoidable. We propose a parallel hybrid web document clustering algorithm, which combines the Principal Direction Divisive Partitioning (PDDP) algorithm with the K-means algorithm. Computational experiments were conducted to test the performance of the hybrid algorithm using three real life web document datasets, and the results were compared with that of the parallel PDDP algorithm and the parallel K-means algorithm. The experiments show that the quality of the clustering solutions obtained from the hybrid algorithm is better than that from the parallel PDDP or the parallel K-means. The parallel run time of the hybrid algorithm is similar to and sometimes less than that of the widely used K-means algorithm.

Using Web structure and summarisation techniques for Web content mining

The dynamic nature and size of the Internet can result in difficulty finding relevant information. Most users typically express their information need via short queries to search engines and they often have to physically sift through the search results based on relevance ranking set by the search engines, making the process of relevance judgement time-consuming. In this paper, we describe a novel representation technique which makes use of the Web structure together with summarisation techniques to better represent knowledge in actual Web Documents. We named the proposed technique as Semantic Virtual Document (SVD). We will discuss how the proposed SVD can be used together with a suitable clustering algorithm to achieve an automatic content-based categorization of similar Web Documents. The auto-categorization facility as well as a “Tree-like” Graphical User Interface (GUI) for post-retrieval document browsing enhances the relevance judgement process for Internet users. Furthermore, we will introduce how our cluster-biased automatic query expansion technique can be used to overcome the ambiguity of short queries typically given by users. We will outline our experimental design to evaluate the effectiveness of the proposed SVD for representation and present a prototype called iSEARCH (Intelligent SEarch And Review of Cluster Hierarchy) for Web content mining. Our results confirm, quantify and extend previous research using Web structure and summarisation techniques, introducing novel techniques for knowledge representation to enhance Web content mining.

Using Shape to Index and Query Web Document Contents

The mass of information now available on web sites has greatly increased the web's popularity and, consequently, the demand for its convenient access and use. The InternetIntranet phenomenon provides some 50 million people with access to multiple sources of information. However, the current techniques for retrieving and navigating do not make it easy for the user to satisfy his/her needs. This paper aims to show how user communities can be enabled to unlock the information stored in web documents. We start from the observation that authors shape their documents to clarify the intended meaning, and readers in turn exploit document shape to synthetically grasp this meaning. Thus, the web document can be seen as a structure composed of different types of information units (such as images, tables, movies, videos, sounds, titles and paragraphs). These units are shaped, represented and organized in the document so that it transmits its message according to the cultural formation of its author. From our investigation of collections of web documents we have derived some heuristics to use in shaping the document in order to emphasize its content. These heuristics can be exploited to manage and retrieve semantic information on the web. Since human computer interaction with the web is preponderantly visual, we propose a visual approach in customizing web documents, and in indexing and querying the Web through the browser. This approach is based on a method of annotating HTML documents so that their shape and contents can be reorganized to satisfy the requirements of different readers.

A Runtime System for Interactive Web Services

<p>Interactive web services are increasingly replacing traditional static<br />web pages. Producing web services seems to require a tremendous<br />amount of laborious low-level coding due to the primitive nature of<br />CGI programming. We present ideas for an improved runtime system<br />for interactive web services built on top of CGI running on virtually<br />every combination of browser and HTTP/CGI server. The runtime<br />system has been implemented and used extensively in <bigwig>, a<br />tool for producing interactive web services.</p><p>Keywords: CGI, Interactive Web Service, Web Document Management,<br />Runtime System, Session Model.</p>

Principles of Web Document Structure and Design

(1997). Principles of Web Document Structure and Design. Medical Reference Services Quarterly: Vol. 16, No. 1, pp. 47-52.