Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation

We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a ‘virtuous cycle’ between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, an evaluation is performed based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous ‘Cranfield’ evaluation of Semantic Web search.     

Construction of a learning environment supporting learners’ reflection: A case of information seeking on the Web

In this study, we design a learning environment that supports reflective activities for information seeking on the Web and evaluate its educational effects. The features of this design are: (1) to visualize the learners’ search processes as described, based on a cognitive schema, (2) to support two types of reflective activities, such as “reflection-in-action” and “reflection-on-action”, and (3) to facilitate reflective activities by comparing their own search processes to other learners’ search processes. We have conducted an experiment to investigate the effects of our design. The experimental results confirm that: (1) the participants’ search performance in the instructional group supported by our instructional design improved effectively than in the control group; (2) they changed their ideas about important activities when seeking information on the Web, and (3) they activated their search cycles more than the control group did.     

Searching the Web for conflicting topics: Page and user factors

Web users tend to search only the pages displayed at the top of the search engine results page (the ‘top link’ heuristic). Although it might be reasonable to use this heuristic to navigate simple and unambiguous facts, it might be risky when searching for conflicting socio-scientific topics, such as potential measures to reduce greenhouse gas emissions. In the present study, we explored the extent to which students consider other Web page characteristics, such as topic relevance and trustworthiness, when searching and bookmarking pages concerning a conflicting topic. We also examined the extent to which prior background knowledge moderates students’ behavior. The results revealed that while the study participants actually used a ‘top link’ heuristic to navigate the results, they engaged in more systematic processes to bookmark pages for further study. Furthermore, the students’ background knowledge was related to the assessment of Web page trustworthiness. We discuss these results from the perspective of a dual-processing model.     

Scaffolding strategies for supporting middle school students’ online inquiry processes

Online inquiry, use of the Web as an information resource to inquire into science, has become increasingly common in middle schools in recent years. However, prior research has found that middle school students tend to use the Web in a superficial manner. To address the challenges that students face in online inquiry, we designed the Digital IdeaKeeper, a scaffolded software tool to help students engage in online inquiry through support for inquiry planning, information search, analysis, and synthesis. This study examined the differences between regular and IdeaKeeper-supported online inquiry performed by 8 pairs of sixth graders in naturalistic classroom settings. Analysis of 80 screen videos of students’ computer activities and conversations found that IdeaKeeper-supported online inquiry was more integrated, efficient, continuous, metacognitive, and focused. This study has important implications for designing online learning environments for middle school students.     

Applying social bookmarking to collective information searching (CIS): An analysis of behavioral pattern and peer interaction for co-exploring quality online resources

With the advancement of Web 2.0 applications, this study aims to advocate that social bookmarking (SB) applications could support mutual exchange of finding information in a manner of collective information searching (CIS). A social bookmarking system, namely ‘WeShare,’ was developed, and conducted with 127 junior high school students for performing the given assignment in this study. The participants’ activities of collecting and reviewing relevant information were traced by log data for later analysis. To initially unveil the participants’ behaviors in the use of social bookmarking for co-exploring the Internet resources, this study proposed some quantitative indicators to represent students’ personal contributions (‘Bookmarks from the Internet,’ ‘Bookmarks from WeShare,’ ‘Annotations on personal bookmarks,’ ‘Comments on others’ bookmarks’) and peer feedback (‘The number of bookmarks collected by peers,’ ‘The number of bookmarks commented on by peers,’ ‘The number of comments from peers’). By the method of cluster analysis, some behavioral patterns regarding how participants collectively search the Internet by use of WeShare were identified. Furthermore, the findings suggest that personal contributions to citing and commenting on peers’ bookmarks are important to the advancement of collective information searching activities for finding quality information on the Internet.     

An overview of Web search evaluation methods

Web search evaluation is the process of measuring the effectiveness of a Web search system. Such an evaluation helps in identifying the most effective one and helps the users to find the required information with less effort. Web search systems have been evaluated in many different ways in the last 15 years. In this paper, we review some of the efforts made for the evaluation of Web search systems. We discuss these evaluation studies by classifying them into eight different categories. As the size and content of Web is changing rapidly, and hence, the Web search techniques, we mention the necessity of an automatic evaluation methodology. But, at the same time, we emphasize that the significance of user based evaluation can not be neglected. Finally, we conclude that an automatic evaluation method that models users’ feedback based evaluation is required for the effective and realistic evaluation of Web search systems.     

Learning and inferencing in user ontology for personalized Semantic Web search

User modeling is aimed at capturing the users’ interests in a working domain, which forms the basis of providing personalized information services. In this paper, we present an ontology based user model, called user ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling, the proposed user ontology model utilizes concepts, taxonomic relations, and non-taxonomic relations in a given domain ontology to capture the users’ interests. As a customized view of the domain ontology, a user ontology provides a richer and more precise representation of the user’s interests in the target domain. Specifically, we present a set of statistical methods to learn a user ontology from a given domain ontology and a spreading activation procedure for inferencing in the user ontology. The proposed user ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine, called OntoSearch, to provide personalized document retrieval services. The experimental results, based on the ACM digital library and the Google Directory, support the efficacy of the user ontology approach to providing personalized information services.     

Learning by foraging: The impact of individual knowledge and social tags on web navigation processes

The World Wide Web provides a tremendously large quantity of information. When users search for information or products on the Web, they will presumably be inclined to choose their path of navigation on the basis of their prior knowledge. In those cases in which the prior knowledge of users is incorrect, however, this navigation process is assumed to lead to suboptimal search results. In an experimental study with 180 participants, we examined to what extent both the users’ prior knowledge and social tags - which capture the collective knowledge of a Web community in tag clouds - influenced the navigation of users and triggered incidental learning processes during the Web search. The results supported the assumption that the users’ prior knowledge is indeed crucial for navigation, as users followed those tags which corresponded to their internal associations. Moreover, we found that social tags also affected the navigation behavior of users, as a strong collective association of social tags led to a high selection rate for these tags. Finally, the results showed that social tags triggered incidental learning processes, as those internal associations which corresponded to tags with a strong collective association were strengthened during navigation. The implications of these findings for further research are discussed.     

Priority scheduling of requests to web portals

Web portals work as a point of access to a large volume of information on the web. This paper focuses on the performance of Web portals in an E-commerce environment which involves the processing of a large number of users’ requests. It proposes a class-based priority scheme which classifies users’ requests into high and low priorities. In E-commerce, some requests (e.g. buy) are generally considered more important than others (e.g. search or browse). We contend that the requests received from a Web portal should generally get higher priority as such requests are more likely to lead to purchases. We believe that assigning such priorities at multiple service levels can improve the performance of Web portals’ requests of higher priority. The proposed scheme is formally specified and implemented, and performance results are obtained and compared to a server that does not prioritise requests. The results show significant performance improvements in the processing of high priority requests.     

THESUS: Organizing Web document collections based on link semantics

The requirements for effective search and management of the WWW are stronger than ever. Currently Web documents are classified based on their content not taking into account the fact that these documents are connected to each other by links. We claim that a pages classification is enriched by the detection of its incoming links semantics. This would enable effective browsing and enhance the validity of search results in the WWW context. Another aspect that is underaddressed and strictly related to the tasks of browsing and searching is the similarity of documents at the semantic level. The above observations lead us to the adoption of a hierarchy of concepts (ontology) and a thesaurus to exploit links and provide a better characterization of Web documents. The enhancement of document characterization makes operations such as clustering and labeling very interesting. To this end, we devised a system called THESUS. The system deals with an initial sets of Web documents, extracts keywords from all pages incoming links, and converts them to semantics by mapping them to a domains ontology. Then a clustering algorithm is applied to discover groups of Web documents. The effectiveness of the clustering process is based on the use of a novel similarity measure between documents characterized by sets of terms. Web documents are organized into thematic subsets based on their semantics. The subsets are then labeled, thereby enabling easier management (browsing, searching, querying) of the Web. In this article, we detail the process of this system and give an experimental analysis of its results.Received: 16 December 2002, Accepted: 16 April 2003, Published online: 17 September 2003     

Students’ performance and satisfaction with Web vs. paper-based practice quizzes and lecture notes

The use of computers to deliver course-related materials is rapidly expanding in most universities. Yet the effects of computer vs. printed delivery modes on students’ performance and motivation are not yet fully known. We compared the impacts of Web vs. paper to deliver practice quizzes that require information search in lecture notes. Hundred and twenty two undergraduate students used either a web site or printed documents to answer 18 mathematics questions during a tutored session. A revised Web site was designed based on ergonomic criteria, to test the hypothesis that improved usability would decrease time spent on the task, the number of pages consulted, and students’ perceived cognitive load. The group working with printed documents had the highest performance. Furthermore, students perceived the paper materials as less effortful to read, and expressed preference for printing lecture notes and questions. However, students appreciated having a Web site available. No differences were found between the two sites. We conclude that Web delivery imposed higher perceived cognitive load due to the need to read lengthy documents. We suggest possible ways to improve Web-based practice materials, such as simultaneous display of questions and lecture notes.     

Searching and browsing Linked Data with SWSE: The Semantic Web Search Engine

In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data – loosely also known as Linked Data – which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web – in terms of scale, unreliability, inconsistency and noise – are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a best-effort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.     

Incremental and interactive mining of web traversal patterns

Web mining involves the application of data mining techniques to large amounts of web-related data in order to improve web services. Web traversal pattern mining involves discovering users’ access patterns from web server access logs. This information can provide navigation suggestions for web users indicating appropriate actions that can be taken. However, web logs keep growing continuously, and some web logs may become out of date over time. The users’ behaviors may change as web logs are updated, or when the web site structure is changed. Additionally, it can be difficult to determine a perfect minimum support threshold during the data mining process to find interesting rules. Accordingly, we must constantly adjust the minimum support threshold until satisfactory data mining results can be found.The essence of incremental data mining and interactive data mining is the ability to use previous mining results in order to reduce unnecessary processes when web logs or web site structures are updated, or when the minimum support is changed. In this paper, we propose efficient incremental and interactive data mining algorithms to discover web traversal patterns that match users’ requirements. The experimental results show that our algorithms are more efficient than other comparable approaches.     

Developing Web literacy in collaborative inquiry activities

Although many children are technically skilled in using the Web, their competences to use it in a critical and meaningful way are usually less well developed. In this article, we report on a multiple case study focusing on the possibilities and limitations of collaborative inquiry activities as an appropriate context to acquire Web literacy skills in primary education. Four 5th grade school teachers and their students worked with collaborative inquiry activities on the subject of ‘healthy food’. The project was aimed at both the development of Web literacy skills and content knowledge building. Data from a variety of sources were collected: videotaped and written lesson observations, interviews with teachers and students, teacher diaries, student questionnaires, and student assignments. The teachers appeared to be able to carry out the program to varying degrees. Contextual factors that influenced the realization of the project’s goals and results were the adequacy of the research questions formulated by students, students’ inquiry skills, and the teachers’ teaching styles. Students’ learning results show that it is possible to teach Web literacy skills in the context of collaborative inquiry activities. All classes show knowledge gain with regard to the subject healthy food and all classes but one show knowledge gain with regard to Web literacy skills. Although many students show adequate use of particular Web searching, reading and evaluating skills after the project, inconsistency, impulsiveness and impatience are also typical of their Web behaviour. In the context of collaborative inquiry activities teachers are challenged to deal with the paradox that they want their students to be active knowledge builders with help of the Web, whereas the Web seems to invite students to be more or less passive searchers.     

Semantic Web Technologies for supporting learning assessment

The use of Semantic Web Technologies in eLearning has become more significant in recent years. A topic that has received less attention to date is the use of such technologies for supporting assessment processes. In this paper, an approach that makes use of Semantic Web Technologies to support the assessment of open questions in eLearning courses is described. This methodology combines domain ontologies, semantic annotations and semantic similarity measurements. It also incorporates an algorithm for extracting knowledge from students’ answers. It has been applied in different courses and the results are also reported and discussed. In our approach, the behaviour of the algorithm for calculating the referred semantic similarity is defined by the teacher.     

Virtual agents in retail web sites: Benefits of simulated social interaction for older users

This study investigates the benefits of simulated social interaction (social presence) through virtual agents for older users’ experience in retail Web sites, particularly with respect to age-related barriers to the adoption of online retailing. In Study 1, through four focus group interviews, we identified six social–psychological barriers to the adoption of online shopping among older users (mean age of 73 years). These included barriers relating to perceived risks, trust, social support, familiarity, experience, and search. In Study 2, a laboratory experiment with older users (mean age of 69 years) demonstrated that embedding a virtual agent that serves search and navigational/procedural support functions in the online store leads to increased perceived social support, trust, and patronage intention for the online store. Mediational analyses further revealed that the effect of virtual agents: (1) on trust is mediated by perceived social support; and (2) on patronage intentions is mediated in part by trust and perceived risks. The study provides important implications on the design of virtual agents for older users of e-commerce applications, and on building online trust and e-service patronage through virtual agents.     

Semantic Web search based on ontological conjunctive queries

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontology-based Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.     

基于深度优先序列模式挖掘的预取模型

序列模式挖掘能够发现隐含在Web日志中的用户的访问规律,可以被用来在Web预取模型中预测即将访问的Web对象。目前大多数序列模式挖掘是基于Apriori的宽度优先算法。提出了基于位图深度优先挖掘算法,采用基于字典树数据结构的深度优先策略,同时采用位图保存和计算各序列的支持度,能够较迅速地挖掘出频繁序列。将该序列模式挖掘算法应用于Web预取模型中,在预取缓存一体化的条件下实验表明具有较好的性能。