CWS: a comparative web search system
In this paper, we define and study a novel search problem: Comparative Web Search (CWS). The task of CWS is to seek relevant and comparative information from the Web to help users conduct comparisons among a set of topics. A system called CWS is developed to effectively facilitate Web users' comparison needs. Given a set of queries, which represent the topics that a user wants to compare, the system is characterized by: (1) automatic retrieval and ranking of Web pages by incorporating both their relevance to the queries and the comparative contents they contain; (2) automatic clustering of the comparative contents into semantically meaningful themes; (3) extraction of representative keyphrases to summarize the commonness and differences of the comparative contents in each theme. We developed a novel interface which supports two types of view modes: a pair-view which displays the result in the page level, and a cluster-view which organizes the comparative pages into the themes and displays the extracted phrases to facilitate users' comparison. Experiment results show the CWS system is effective and efficient.

title: A practical system of keyphrase extraction for web pages
abstract: Keyphrases can be used to facilitate Web users grasping the main topic(s) of a Web page. We present a practical system of automatic keyphrase extraction for Web pages. In this system, a regression model was first trained based on a set of human-labeled documents. Then it was used to extract keyphrases from new pages automatically. This paper makes three contributions. First, the structure information in a Web page was investigated for keyphrase extraction task. Second, the query log data associated with a Web page collected by a search engine server were used to help keyphrase extraction. Third, a method was put forward in this paper in order to evaluate the similarity of phrases.

title: Latent semantic analysis for multiple-type interrelated data objects
abstract: Co-occurrence data is quite common in many real applications. Latent Semantic Analysis (LSA) has been successfully used to identify semantic relations in such data. However, LSA can only handle a single co-occurrence relationship between two types of objects. In practical applications, there are many cases where multiple types of objects exist and any pair of these objects could have a pairwise co-occurrence relation. All these co-occurrence relations can be exploited to alleviate data sparseness or to represent objects more meaningfully. In this paper, we propose a novel algorithm, M-LSA, which conducts latent semantic analysis by incorporating all pairwise co-occurrences among multiple types of objects. Based on the mutual reinforcement principle, M-LSA identifies the most salient concepts among the co-occurrence data and represents all the objects in a unified semantic space. M-LSA is general and we show that several variants of LSA are special cases of our algorithm. Experiment results show that M-LSA outperforms LSA on multiple applications, including collaborative filtering, text clustering, and text categorization.

title: CubeSVD: a novel approach to personalized Web search
abstract: As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users' information needs. This paper focuses on utilizing clickthrough data to improve Web search. Since millions of searches are conducted everyday, a search engine accumulates a large volume of clickthrough data, which records who submits queries and which pages he/she clicks on. The clickthrough data is highly sparse and contains different types of objects (user, query and Web page), and the relationships among these objects are also very complicated. By performing analysis on these data, we attempt to discover Web users' interests and the patterns that users locate information. In this paper, a novel approach CubeSVD is proposed to improve Web search. The clickthrough data is represented by a 3-order tensor, on which we perform 3-mode analysis using the higher-order singular value decomposition technique to automatically capture the latent factors that govern the relations among these multi-type objects: users, queries and Web pages. A tensor reconstructed based on the CubeSVD analysis reflects both the observed interactions among these objects and the implicit associations among them. Therefore, Web search activities can be carried out based on CubeSVD analysis. Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods.

title: Document Summarization Using Conditional Random Fields
abstract: Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a two-class classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences into a summary directly, which are hard to generalize. In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. What is more, the proposed framework can take the outcomes of previous methods as features and seamlessly integrate them. The key idea of our approach is to treat the summarization task as a sequence labeling problem. In this view, each document is a sequence of sentences and the summarization procedure labels the sentences by 1 and 0. The label of a sentence depends on the assignment of labels of others. We compared our proposed approach with eight existing methods on an open benchmark data set. The results show that our approach can improve the performance by more than 7.1% and 12.1% over the best supervised baseline and unsupervised baseline respectively in terms of two popular metrics F1 and ROUGE-2. Detailed analysis of the improvement is presented as well.

title: Supervised latent semantic indexing for document categorization
abstract: Latent semantic indexing (LSI) is a successful technology in information retrieval (IR) which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. However, LSI is not optimal for document categorization tasks because it aims to find the most representative features for document representation rather than the most discriminative ones. In this paper, we propose supervised LSI (SLSI) which selects the most discriminative basis vectors using the training data iteratively. The extracted vectors are then used to project the documents into a reduced dimensional space for better classification. Experimental evaluations show that the SLSI approach leads to dramatic dimension reduction while achieving good classification results.

title: Reasoning about semantic Web in Isabelle/HOL
abstract: Semantic Web is regarded as the next generation of the World Wide Web. It provides not only the structure of the Web but also meaningful semantics for the information presented. To make semantic Web services understandable for distributed agents, formal definitions of the ontologies and their consistencies are essential. However, the existing tools for reasoning about semantic Web ontologies are still primitive. We believe that mature software engineering tools, such as theorem provers, can contribute to the reasoning phase. In this paper, we present an approach of encoding the semantic Web ontology (DAML+OIL) into the generic theorem prover Isabelle/HOL for automatic reasoning. Furthermore, a translation tool was developed to transform semantic Web ontologies into their extended Isabelle theories. With additional intermediate lemmas, Isabelle can be used to perform both subsumption (class) level and instantiation (instance) level reasoning of the semantic Web ontologies.

title: Specifying and reasoning about generic architecture in TCOZ
abstract: Formal modeling techniques can be used to define and verify software architectures precisely. The paper applies the integrated formal specification technique, Timed Communicating Object Z (TCOZ), to generic software architecture modeling and verification.

title: A Compound Intrusion Detection Model
abstract: Intrusion detection systems (IDSs) have become a critical part of security systems. The goal of an intrusion detection system is to identify intrusion effectively and accurately. However, the performance of misuse intrusion detection system (MIDS) or anomaly intrusion detection system (AIDS) is not satisfying. In this paper, we study the issue of building a compound intrusion detection model, which has the merits of MIDS and AIDS. To build this compound model, we propose an improved Bayesian decision theorem. The improved Bayesian decision theorem brings some profits to this model: to eliminate the flaws of a narrow definition for intrusion patterns, to extend the known intrusions patterns to novel intrusions patterns, to reduce risks that detecting intrusion brings to system and to offer a method to build a compound intrusion detection model that integrates MIDS with AIDS.

title: Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
abstract: Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, tensor streams, and an effective algorithmic framework, window-based tensor analysis (WTA). Two variations of WTA, independent- window tensor analysis (IW) and moving-window tensor analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, multi-aspect correlation analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.

title: Z Approach to Semantic Web
abstract: The Semantic Web (SW) service is a web application using Semantic Web techniques which usually involve cooperation between several intelligent agents. The design of SW systems requires precise modelling techniques to capture ontology domain properties and application functionalities. We believe that Z as a specification technique can contribute to the Semantic Web-based system development in many ways. In this paper, we firstly conduct a case study of applying Z to the design of a SW service system (online talk discovery), and then present translation techniques and tools which can extract the SW ontology from the Z model automatically. Furthermore, we discuss how existing Z tools, i.e. Z/EVES, can be used to improve the quality of SW ontology design.