Refining search results using a mining framework

Today’s major search engines return ranked search results that match the keywords the user specifies. There have been many proposals to rank the search results such that they match the user’s intentions and needs more closely. Despite good advances during the past decade, this problem still requires considerable research, as the number of search results has become ever larger. We define the collection of each search result and all the Web pages that are linked to the result as a search-result drilldown. We hypothesize that by mining and analyzing the top terms in the search-result drilldown of search results, it may be possible to make each search result more meaningful to the user, so that the user may select the desired search results with higher confidence. In this paper, we describe this technique, and show the results of preliminary validation work that we have done.     

MIRRE approach: nonlinear and multimodal exploration of MIR aggregated search results

Multimedia Tools and Applications - Nowadays, web users frequently explore multimedia contents to satisfy their information needs. The exploration approaches usually provide linear interaction...     

A conceptual framework mapping the application of information search strategies to well and ill-structured problem solving

Problem-based learning (PBL) is an instructional approach that is organized around the investigation and resolution of problems. Problems are neither uniform nor similar. 11 and 12 in his design theory of problem solving has categorized problems into two broad types – well-structured and ill-structured. He has also described a host of mediating skills that impact problem solving outcomes. The thrust of this paper rests in the argument that the basis of these mediating skills is information search literacy and particularly, in view of the utility of the Internet as an informational repository, effective information searching skills. This study was an investigation of how different Internet information seeking strategies can be used to engage in problem solving. A conceptual framework that explains how different Internet information searching strategies can be employed in successfully solving well and ill-structured problems was devised and empirically tested. The research site was a newly established polytechnic in Singapore that employs problem-based learning to support its curricular implementation. The sample population of students came from a class of 25 first-year students. The research findings of this study inform that information searching skills indeed play an important role in problem solving. The findings affirm the need for students to be systematically instructed in the skills of information searching to be able to accomplish problem solving. The information searching necessary for solving well-structured problems is constrained and readily manageable. Thus, students only have to be acquainted with fundamental information searching skills to solve well-structured problems. On the other hand, the information needs of ill-structured problems are usually complex, multi-disciplinary and expansive. Hence, students have to be trained to apply a more advanced set of information searching skills in resolving ill-structured problems.     

A framework for search interfaces

Searching textual databases can be confusing for users. Popular search systems for the World Wide Web and stand alone systems typically provide a simple interface: users type in keywords and receive a relevance ranked list of 10 results. This is appealing in its simplicity, but users are often frustrated because search results are confusing or aspects of the search are out of their control. If we are to improve user performance, reduce mistaken assumptions, and increase successful searches, we need more predictable design. To coordinate design practice, we suggest a four-phase framework that would satisfy first time, intermittent, and frequent users accessing a variety of textual and multimedia libraries     

An Activity Theory framework to explore distributed communities

Technology‐mediated communication is having an influence on the working, learning and leisure activities of everyone. This paper looks at work and learning which have many features in common and yet remain different in many ways as the intentions are distinct. Research into computer support for working and computer support for learning emphasise the differences through rather traditional perspectives but it may be possible that each research domain can offer insights into the other. This paper attempts to draw on both research cultures and makes tentative steps in creating a framework for the new insights which are required if there is to be a better understanding of the ways that communications technology may be able to add new opportunities for distributed communities of learners.     

A framework to uncover multiple alternative clusterings

Machine Learning - Clustering is often referred to as unsupervised learning which aims at uncovering hidden structures from data. Unfortunately, though widely being used as one of the principal...     

Combining information from multiple search engines—Preliminary comparison

The total number of popular search engines has decreased over time from its peak of the late 1990s. However, when combining the three remaining major ones (Yahoo!, Google, MS Live) with large repositories of information (e.g. BBC.com, NYT.com, Gazeta.pl, etc.), the total number of important information sources can be seen as slowly increasing. Focusing on utilization of search engines only, it is easy to observe that the same query issued to each one of them results in different “suggestions.” The question thus arrives, is it possible (and worthy) to combine responses obtained from each one of them into a single answer set. In this paper, we look into three approaches of achieving this goal, which are based on: Game theory, Auction and Consensus methods, while our focus is to study (and compare) their “performance.”     

Agent based intelligent search framework for product information using ontology mapping

The Semantic Web and Web services provide many opportunities in various applications such as product search and comparison in electronic commerce. We implemented an intelligent meta-search and recommendation system for products through consideration of multiple attributes by using ontology mapping and Web services. Under the assumption that each shopping site offers product ontology and product search service with Web services, we proposed a meta-search framework to configure a customer’s search intent, make and dispatch proper queries to each shopping site, evaluate search results from shopping sites, and show the customer the relevant product list with associated rankings. Ontology mapping is used for generating proper queries for shopping sites that have different product categories. We also implemented our framework and performed empirical evaluation of our approach with two leading shopping sites in the world.     

A simple analytical framework to analyze search strategies in large-scale peer-to-peer networks

This paper presents an analytical framework to study search strategies in large-scale decentralized unstructured peer-to-peer (P2P) networks. The peers comprising the P2P network and their application-level connections are modeled as generalized random graphs (GRGs) whose simple and efficient analysis is accomplished using the generating function of the graph’s degree distribution. The framework we defined allows the computation of several interesting performance indexes to be used to compare different search strategies: in particular, the average number of messages sent throughout the P2P network and the probability that a query is successful are used as examples. Furthermore, assuming that the cumulative distribution function (CDF) of the time required by a peer to positively reply to a query is known, we show how to derive the CDF of the time it takes for a randomly chosen peer to obtain at least one positive reply from other peers. The approach is validated through simulation showing that the accuracy of the proposed model improves as the size of the P2P network increases making it a suitable tool for the analysis of search strategies in large-scale systems.     

EXEHDA-HM: A compositional approach to explore contextual information on hybrid models

The current proposals of hybrid context modeling bring new challenges, an important one is how applications can access and process data stored on these models. Thinking about that, this paper proposes a solution to deal with this challenge through a compositional approach that explores the context information on hybrid models, called EXEHDA-HM. The proposed approach stands out by the design of a repository that supports three database models and by the compositional processing strategy based on rules. In our proposal, the applications can combine data stored on different bases in a single rule, which could enhance the identification of contextual situations. For the evaluation we designed and implemented some case studies on information security area, exploring the hybrid repository composed of relational, non-relational, and triple storage models. Our results demonstrate that was possible to identify richer situations with the data composition across more than one model and there are situations that can only be found through this composition.     

A spatial relation-based framework to perform visual information extraction

The Spatial Relation Query (SRQ) tool is a graphical software environment, supported by a SQL-like language, which enables users to perform information extraction driven by the visual appearance and the spatial arrangement of the information. The tool has been initially customised to work on specific application domains, like web pages and geospatial data. In this paper, we present the theoretical formalisation of the visual information extraction (VIE) task and accordingly the redesign of the SRQ tool, which is now a full-featured, general-purpose information extraction system. Moreover, we show a new application of the VIE framework to the analysis and visual information extraction from PDF files.     

A conceptual framework for developing solutions that organise social media information for emergency response teams

ABSTRACT

Social media have great power to spread information, and this is particularly noticeable when an emergency occurs. The extraction of accurate information from social media can offer an important resource for emergency management, both in terms of decision-making and increasing situational awareness. This paper describes a conceptual framework for the development of applications to treat messages from social media. It is designed to select, classify and prioritise, using parameters, messages containing information that is relevant to the emergency context. It allows a team to act on this information and to generate rescue actions that contribute to the emergency solution. It has a collaborative bias, providing perceptual, coordination and communication mechanisms. We also present an instantiation and the simulation of its use in the treatment of tweets (Twitter messages) about two emergencies: an earthquake in Mexico City (19/09/2017) and a California fire (December, 2017). The volume of messages is enormous, but most of them do not present significant value to the emergency response. We categorised those that contained relevant information. With only 2% of the tweets, it was possible to identify and prioritise messages with potential to aid in response and rescue operations.     

A curriculum framework for implementing information technology in school education to foster information literacy

A literature review of the development of the information technology (IT) curriculum in recent decades in Hong Kong reveals that the aim of the curriculum has shifted from Computer Studies to the development of information literacy (IL). Based on a survey of all schools in Hong Kong and in response to the demand for IL in society, a curriculum framework is proposed that fosters the IL of students. The curriculum framework is based on the rationale of significant learning and designed on the principles of providing authenticity and creating reflection. It consists of three parts: a core IT curriculum; use of knowledge about IT and information processing across curricula; and a school culture that fosters IL. Four issues that are critical for the successful implementation of the proposed curriculum framework are discussed: school-based implementation models; the provision of service learning activities; support from parents; and reduction of the digital divide between students.     

A theory of information with special application to search problems

Classical information theory concerns itself with communication through a noisy channel and how much one can infer about the channel input from a knowledge of the channel output. Because the channel is noisy the input and output are only related statistically and the rate of information transmission is a statistical concept with little meaning for the individual symbol used in transmission. Here we develop a more intuitive notion of information that is concerned with asking the right questions--that is, with finding those questions whose answer conveys the most information. We call this confirmatory information. In the first part of the paper we develop the general theory, show how it relates to classical information theory, and how in the special case of search problems it allows us to quantify the efficacy of information transmission regarding individual events. That is, confirmatory information measures how well a search for items having certain observable properties retrieves items having some unobserved property of interest. Thus confirmatory information facilitates a useful analysis of search problems and contrasts with classical information theory, which quantifies the efficiency of information transmission but is indifferent to the nature of the particular information being transmitted. The last part of the paper presents several examples where confirmatory information is used to quantify protein structural properties in a search setting.     

A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

A framework for interval-valued information system

Interval-valued information system is used to transform the conventional dataset into the interval-valued form. To conduct the interval-valued data mining, we conduct two investigations: (1) construct the interval-valued information system, and (2) conduct the interval-valued knowledge discovery. In constructing the interval-valued information system, we first make the paired attributes in the database discovered, and then, make them stored in the neighbour locations in a common database and regard them as ‘one’ new field. In conducting the interval-valued knowledge discovery, we utilise some related priori knowledge and regard the priori knowledge as the control objectives; and design an approximate closed-loop control mining system. On the implemented experimental platform (prototype), we conduct the corresponding experiments and compare the proposed algorithms with several typical algorithms, such as the Apriori algorithm, the FP-growth algorithm and the CLOSE+ algorithm. The experimental results show that the interval-valued information system method is more effective than the conventional algorithms in discovering interval-valued patterns.     

A framework to analyze information systems as knowledge flow facilitators

This paper presents a framework which can be used to analyze information systems as knowledge flow facilitators in organizational processes. This framework may be useful, particularly to small organizations, for two main reasons: it can help them to start seeing the implications of KM in their current technical infrastructure, and as a result, they should be in a better position to know how to include their current working tools in part of a KM strategy, thus facilitating the alignment of such a strategy to the daily work of the organization. Second, identifying the role that their current tools play in the flow of knowledge should help such organizations to identify means by which to improve such tools as KM enablers, before becoming engaged in costly KM efforts that could require the acquisition of new tools and often also big changes in their current work processes. The applicability of the framework is illustrated with a case study conducted in a software development environment in which it was successfully applied.     

A knowledge-based search framework for designing composite structures

In this paper, a search framework is presented that makes a genetic algorithm (GA) a practical engineering tool for searching discrete and nonconvex design spaces. It is shown that near optimal designs can be relatively quickly found if three problems are addressed: (i) the dimensionality of a design space is reasonably reduced, (ii) fast function evaluations are available, and (iii) genetic operators are tailored to the application at hand. In our work, the number of active design variables is reduced by using a rule-based fuzzy logic inference engine to process knowledge about the structure's behaviour. The use of a parallel computing environment for structural analysis in conjunction with data retrieval from binary-like trees effectively reduces the time required to perform genetic searching. Three different selection operators along with a permutation-like operator for the reduced problem are evaluated for their effectiveness in locating the global optimum. As an example, the stacking sequence for maximizing a buckling load for a stiffened square panel with a circular cut-out and subject to thermal and shear loading is designed.