A study and comparison of multimedia Web searching : 1997-2006

Searching for multimedia is an important activity for users of Web search engines. Studying user's interactions with Web search engine multimedia buttons, including image, audio, and video, is important for the development of multimedia Web search systems. This article provides results from a Weblog analysis study of multimedia Web searching by Dogpile users in 2006. The study analyzes the (a) duration, size, and structure of Web search queries and sessions; (b) user demographics; (c) most popular multimedia Web searching terms; and (d) use of advanced Web search techniques including Boolean and natural language. The current study findings are compared with results from previous multimedia Web searching studies. The key findings are: (a) Since 1997, image search consistently is the dominant media type searched followed by audio and video; (b) multimedia search duration is still short (>50% of searching episodes are <1 min), using few search terms; (c) many multimedia searches are for information about people, especially in audio search; and (d) multimedia search has begun to shift from entertainment to other categories such as medical, sports, and technology (based on the most repeated terms). Implications for design of Web multimedia search engines are discussed.

Personalised ontology learning and mining for web information gathering

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.

Multitasking, cognitive coordination and cognitive shifts during web searching

As Web searching becomes more prolific for information access worldwide, we need to better understand users’ Web searching behaviour and develop better models of their interaction with Web search systems. Web search modelling is a significant and important area of Web research. Searching on the Web is an integral element of information behaviour and human–computer interaction. Web searching includes multitasking processes, the allocation of cognitive resources among several tasks, and shifts in cognitive, problem and knowledge states. In addition to multitasking, cognitive coordination and cognitive shifts are also important, but are under-explored aspects of Web searching. During the Web searching process, beyond physical actions, users experience various cognitive activities. Interactive Web searching involves many users’ cognitive shifts at different information behaviour levels. Cognitive coordination allows users to trade off the dependences among multiple information tasks and the resources available. Much research has been conducted into Web searching. However, few studies have modelled the nature of and relationship between multitasking, cognitive coordination and cognitive shifts in the Web search context. Modelling how Web users interact with Web search systems is vital for the development of more effective Web IR systems. This study aims to model the relationship between multitasking, cognitive coordination and cognitive shifts during Web searching. A preliminary theoretical model is presented based on previous studies. The research is designed to validate the preliminary model. Forty-two study participants were involved in the empirical study. A combination of data collection instruments, including pre- and post-questionnaires, think-aloud protocols, search logs, observations and interviews were employed to obtain users’ comprehensive data during Web search interactions. Based on the grounded theory approach, qualitative analysis methods including content analysis and verbal protocol analysis were used to analyse the data. The findings were inferred through an analysis of questionnaires, a transcription of think-aloud protocols, the Web search logs, and notes on observations and interviews. Five key findings emerged. (1) Multitasking during Web searching was demonstrated as a two-dimensional behaviour. The first dimension was represented as multiple information problems searching by task switching. Users’ Web searching behaviour was a process of multiple tasks switching, that is, from searching on one information problem to searching another. The second dimension of multitasking behaviour was represented as an information problem searching within multiple Web search sessions. Users usually conducted Web searching on a complex information problem by submitting multiple queries, using several Web search systems and opening multiple windows/tabs. (2) Cognitive shifts were the brain’s internal response to external stimuli. Cognitive shifts were found as an essential element of searching interactions and users’ Web searching behaviour. The study revealed two kinds of cognitive shifts. The first kind, the holistic shift, included users’ perception on the information problem and overall information evaluation before and after Web searching. The second kind, the state shift, reflected users’ changes in focus between the different cognitive states during the course of Web searching. Cognitive states included users’ focus on the states of topic, strategy, evaluation, view and overview. (3) Three levels of cognitive coordination behaviour were identified: the information task coordination level, the coordination mechanism level, and the strategy coordination level. The three levels of cognitive coordination behaviour interplayed to support multiple information tasks switching. (4) An important relationship existed between multitasking, cognitive coordination and cognitive shifts during Web searching. Cognitive coordination as a management mechanism bound together other cognitive processes, including multitasking and cognitive shifts, in order to move through users’ Web searching process. (5) Web search interaction was shown to be a multitasking process which included information problems ordering, task switching and task and mental coordinating; also, at a deeper level, cognitive shifts took place. Cognitive coordination was the hinge behaviour linking multitasking and cognitive shifts. Without cognitive coordination, neither multitasking Web searching behaviour nor the complicated mental process of cognitive shifting could occur. The preliminary model was revisited with these empirical findings. A revised theoretical model (MCC Model) was built to illustrate the relationship between multitasking, cognitive coordination and cognitive shifts during Web searching. Implications and limitations of the study are also discussed, along with future research work.

User-web interactions : how wholistic/analytic web users search the web?

User-Web interactions have emerged as an important research in the field of information science. In this study, we examine extensively the Web searching performed by general users. Our goal is to investigate the effects of users’ cognitive styles on their Web search behavior in relation to two broad components: Information Searching and Information Processing Approaches. We use questionnaires, a measure of cognitive style, Web session logs and think-aloud as the data collection instruments. Our study findings show wholistic Web users tend to adopt a top-down approach to Web searching, where the users searched for a generic topic, and then reformulate their queries to search for specific information. They tend to prefer reading to process information. Analytic users tend to prefer a bottom-up approach to information searching and they process information by scanning search result pages.

Fast and accurate phonetic spoken term detection

For the first time in human history, large volumes of spoken audio are being broadcast, made available on the internet, archived, and monitored for surveillance every day. New technologies are urgently required to unlock these vast and powerful stores of information. Spoken Term Detection (STD) systems provide access to speech collections by detecting individual occurrences of specified search terms. The aim of this work is to develop improved STD solutions based on phonetic indexing. In particular, this work aims to develop phonetic STD systems for applications that require open-vocabulary search, fast indexing and search speeds, and accurate term detection. Within this scope, novel contributions are made within two research themes, that is, accommodating phone recognition errors and, secondly, modelling uncertainty with probabilistic scores. A state-of-the-art Dynamic Match Lattice Spotting (DMLS) system is used to address the problem of accommodating phone recognition errors with approximate phone sequence matching. Extensive experimentation on the use of DMLS is carried out and a number of novel enhancements are developed that provide for faster indexing, faster search, and improved accuracy. Firstly, a novel comparison of methods for deriving a phone error cost model is presented to improve STD accuracy, resulting in up to a 33% improvement in the Figure of Merit. A method is also presented for drastically increasing the speed of DMLS search by at least an order of magnitude with no loss in search accuracy. An investigation is then presented of the effects of increasing indexing speed for DMLS, by using simpler modelling during phone decoding, with results highlighting the trade-off between indexing speed, search speed and search accuracy. The Figure of Merit is further improved by up to 25% using a novel proposal to utilise word-level language modelling during DMLS indexing. Analysis shows that this use of language modelling can, however, be unhelpful or even disadvantageous for terms with a very low language model probability. The DMLS approach to STD involves generating an index of phone sequences using phone recognition. An alternative approach to phonetic STD is also investigated that instead indexes probabilistic acoustic scores in the form of a posterior-feature matrix. A state-of-the-art system is described and its use for STD is explored through several experiments on spontaneous conversational telephone speech. A novel technique and framework is proposed for discriminatively training such a system to directly maximise the Figure of Merit. This results in a 13% improvement in the Figure of Merit on held-out data. The framework is also found to be particularly useful for index compression in conjunction with the proposed optimisation technique, providing for a substantial index compression factor in addition to an overall gain in the Figure of Merit. These contributions significantly advance the state-of-the-art in phonetic STD, by improving the utility of such systems in a wide range of applications.

Automated gathering of Web information: An in-depth examination of agents interacting with search engines

The Web has become a worldwide repository of information which individuals, companies, and organizations utilize to solve or address various information problems. Many of these Web users utilize automated agents to gather this information for them. Some assume that this approach represents a more sophisticated method of searching. However, there is little research investigating how Web agents search for online information. In this research, we first provide a classification for information agent using stages of information gathering, gathering approaches, and agent architecture. We then examine an implementation of one of the resulting classifications in detail, investigating how agents search for information on Web search engines, including the session, query, term, duration and frequency of interactions. For this temporal study, we analyzed three data sets of queries and page views from agents interacting with the Excite and AltaVista search engines from 1997 to 2002, examining approximately 900,000 queries submitted by over 3,000 agents. Findings include: (1) agent sessions are extremely interactive, with sometimes hundreds of interactions per second (2) agent queries are comparable to human searchers, with little use of query operators, (3) Web agents are searching for a relatively limited variety of information, wherein only 18% of the terms used are unique, and (4) the duration of agent-Web search engine interaction typically spans several hours. We discuss the implications for Web information agents and search engines.

Introduction : creative industries and cities

The 2000s were marked by a resurgence of interest in creativity and cities. If the rapid global proliferation of the Internet and digital media technologies in the 1990s had set off enthusiasm for a post-industrial ‘new economy’, where the significance of location would be in decline, the 2000s saw an energetic search by artists, entrepreneurs, investors, policy-makers, journalists and many others to uncover the well-springs of creativity and its relationship to place (Flew 2012a). This chapter begins with a discussion of the discourses or ‘scripts’ that have emerged to try and conceptualise the relationship between creativity and cities, notably theories of creative clusters, creative cities and creative class theories. Such work can be seen as representing a growth in the field of cultural economic geography although – as is noted in the chapter – it possesses some significant gaps. Among the issues that are drawn out in this book, and discussed in this chapter, are: the need to move beyond ‘imagined geographies’ of creative inner cities and come to terms with empirical evidence that suggests significant concentrations of the creative workforce in suburbs and regional cities; the relevance of urban cultural policy as a variable in the rise of cities as creative hubs or, in a different model, media capitals; and the challenges of bringing together cultural research with economic discourses in ways that get beyond caricatured representations of the ‘other’, as found, for instance, in some of the most influential framings of the concept of neo-liberalism.

Supporting research at QUT : a tale of three librarians and a Creative Industries super-faculty

In 2012 the existing eight disciplines of Creative Industries Faculty, QUT combined with the School of Design (formerly a component of the Faculty of Built Environment and Engineering) to create a super faculty that includes the following disciplines: Architecture, Creative Writing & Literary Studies, Dance, Drama, Fashion, Film & Television, Industrial Design, Interior Design, Journalism, Media & Communication, Landscape Architecture, Music & Sound and Urban Design. The university’s research training unit AIRS (Advanced Information Retrieval Skills) is a systematic introduction to research level information literacies. It is currently being redesigned to reflect today’s new data intensive research environment and facilitate the capacity for life-long learning. Upon completion participants are expected to be able to: 1. Demonstrate an understanding of the theory of advanced search and evaluative strategies to efficiently yield appropriate resources to create original research. 2. Apply appropriate data management strategies to organise and utilize your information proficiently, ethically and legally. 3. Identify strategies to ensure best practice in the use of information sources, information technologies, information access tools and investigative methods. All Creative Industries Faculty research students must complete this unit into which CI Librarians teach discipline specific material. The library employs a team of research specific experts as well as Liaison Librarians for each faculty. Together they develop and deliver a generic research training program that provides researcher training in the following areas: Managing Research Data, QUT ePrints: New features for tracking your research impact, Tracking Research Impact, Research Students and the Library: Overview of Library Research Support Services, Technologies for Research Collaboration, Open Access Publishing, Greater Impact via Creative Commons Licence, CAMBIA - Navigating the patent literature, Uploading Publications to QUT ePrints Workshop, AIRS for supervisors, Finding Existing Research Data, Keeping up to date:Discovering and managing current awareness information and Getting Published. In 2011 Creative Industries initiated a new faculty specific research training program to promote capacity building for research within their Faculty, with workshops designed and developed with Faculty Research Leaders, The Office of Research and Liaison Librarians. “Show me the money” which assists staff to pursue alternative funding sources was one such session that was well attended and generated much discussion and interest. Drop in support sessions for ePrints, EndNote referencing software and Tracking Research Impact for the Creative Industries were also popular options on the menu. Liaison Librarians continue to provide one-on-one consultations with individual researchers as requested. This service assists Librarians greatly with getting to know and monitoring their researchers’ changing needs. The CI Faculty has enlisted two Research Leaders, one for each of the two Schools (Design and Media, Entertainment & Creative Arts) whose role it is to mentor newer research staff. Similarly within the CI library liaison team one librarian is assigned the role of Research Coordinator, whose responsibility it is to be the primary liaison with the Assistant Dean, Research and other key Faculty research managers and is the one most likely to attend Faculty committees and meetings relating to research support.

Many paths lead to discovery : analogical retrieval of cancer therapies

This paper addresses the issue of analogical inference, and its potential role as the mediator of new therapeutic discoveries, by using disjunction operators based on quantum connectives to combine many potential reasoning pathways into a single search expression. In it, we extend our previous work in which we developed an approach to analogical retrieval using the Predication-based Semantic Indexing (PSI) model, which encodes both concepts and the relationships between them in high-dimensional vector space. As in our previous work, we leverage the ability of PSI to infer predicate pathways connecting two example concepts, in this case comprising of known therapeutic relationships. For example, given that drug x TREATS disease z, we might infer the predicate pathway drug x INTERACTS WITH gene y ASSOCIATED WITH disease z, and use this pathway to search for drugs related to another disease in similar ways. As biological systems tend to be characterized by networks of relationships, we evaluate the ability of quantum-inspired operators to mediate inference and retrieval across multiple relations, by testing the ability of different approaches to recover known therapeutic relationships. In addition, we introduce a novel complex vector based implementation of PSI, based on Plate’s Circular Holographic Reduced Representations, which we utilize for all experiments in addition to the binary vector based approach we have applied in our previous research.

Sense-making across space and time : implications for the organization and findability of information

This paper presents the results from a study of information behaviors, with specific focus on information organisation-related behaviours conducted as part of a larger daily diary study with 34 participants. The findings indicate that organization of information in everyday life is a problematic area due to various factors. The self-evident one is the inter-subjectivity between the person who may have organized the information and the person looking for that same information (Berlin et. al., 1993). Increasingly though, we are not just looking for information within collections that have been designed by someone else, but within our own personal collections of information, which frequently include books, electronic files, photos, records, documents, desktops, web bookmarks, and portable devices. The passage of time between when we categorized or classified the information, and the time when we look for the same information, poses several problems of intra-subjectivity, or the difference between our own past and present perceptions of the same information. Information searching, and hence the retrieval of information from one's own collection of information in everyday life involved a spatial and temporal coordination with one's own past selves in a sort of cognitive and affective time travel, just as organizing information is a form of anticipatory coordination with one's future information needs. This has implications for finding information and also on personal information management.

Reliability and validity of query intent assessments

In most intent recognition studies, annotations of query intent are created post hoc by external assessors who are not the searchers themselves. It is important for the field to get a better understanding of the quality of this process as an approximation for determining the searcher's actual intent. Some studies have investigated the reliability of the query intent annotation process by measuring the interassessor agreement. However, these studies did not measure the validity of the judgments, that is, to what extent the annotations match the searcher's actual intent. In this study, we asked both the searchers themselves and external assessors to classify queries using the same intent classification scheme. We show that of the seven dimensions in our intent classification scheme, four can reliably be used for query annotation. Of these four, only the annotations on the topic and spatial sensitivity dimension are valid when compared with the searcher's annotations. The difference between the interassessor agreement and the assessor-searcher agreement was significant on all dimensions, showing that the agreement between external assessors is not a good estimator of the validity of the intent classifications. Therefore, we encourage the research community to consider using query intent classifications by the searchers themselves as test data.

Towards modelling web search behaviour : integrating users’ cognitive styles

With the rapid growth of information on the Web, the study of information searching has let to an increased interest. Information behaviour (IB) researchers and information systems (IS) developers are continuously exploring user - Web search interactions to understand and to help users to provide assistance with their information searching. In attempting to develop models of IB, several studies have identified various factors that govern user's information searching and information retrieval (IR), such as age, gender, prior knowledge and task complexity. However, how users' contextual factors, such as cognitive styles, affect Web search interactions has not been clearly explained by the current models of Web Searching and IR. This study explores the influence of users' cognitive styles on their Web search behaviour. The main goal of the study is to enhance Web search models with a better understanding of how these cognitive styles affect Web searching. Modelling Web search behaviour with a greater understanding of user's cognitive styles can help information science researchers and IS designers to bridge the semantic gap between the user and the IS. To achieve the aims of the study, a user study with 50 participants was conducted. The study adopted a mixed method approach incorporating several data collection strategies to gather a range of qualitative and quantitative data. The study utilised pre-search and post-search questionnaires to collect the participants' demographic information and their level of satisfaction about the search interactions. Riding's (1991) Cognitive Style Analysis (CSA) test was used to assess the participants' cognitive styles. Participants completed three predesigned search tasks and the whole user - web search interactions, including thinkaloud, were captured using a monitoring program. Data analysis involved several qualitative and quantitative techniques: the quantitative data gave raise to detailed findings about users' Web searching and cognitive styles, the qualitative data enriched the findings with illustrative examples. The study results provide valuable insights into Web searching behaviour among different cognitive style users. The findings of the study extend our understanding of Web search behaviour and how users search information on the Web. Three key study findings emerged: • Users' Web search behaviour was demonstrated through information searching strategies, Web navigation styles, query reformulation behaviour and information processing approaches while performing Web searches. The manner in which these Web search patterns were demonstrated varied among the users with different cognitive style groups. • Users' cognitive styles influenced their information searching strategies, query reformulation behaviour, Web navigational styles and information processing approaches. Users with particular cognitive styles followed certain Web search patterns. • Fundamental relationships were evident between users' cognitive styles and their Web search behaviours; and these relationships can be illustrated through modelling Web search behaviour. Two models that depict the associations between Web search interactions, user characteristics and users' cognitive styles were developed. These models provide a greater understanding of Web search behaviour from the user perspective, particularly how users' cognitive styles influence their Web search behaviour. The significance of this research is twofold: it will provide insights for information science researchers, information system designers, academics, educators, trainers and librarians who want to better understand how users with different cognitive styles perform information searching on the Web; at the same time, it will provide assistance and support to the users. The major outcomes of this study are 1) a comprehensive analysis of how users search the Web; 2) extensive discussion on the implications of the models developed in this study for future work; and 3) a theoretical framework to bridge high-level search models and cognitive models.

Combining word semantics within complex Hilbert space for information retrieval

Complex numbers are a fundamental aspect of the mathematical formalism of quantum physics. Quantum-like models developed outside physics often overlooked the role of complex numbers. Specifically, previous models in Information Retrieval (IR) ignored complex numbers. We argue that to advance the use of quantum models of IR, one has to lift the constraint of real-valued representations of the information space, and package more information within the representation by means of complex numbers. As a first attempt, we propose a complex-valued representation for IR, which explicitly uses complex valued Hilbert spaces, and thus where terms, documents and queries are represented as complex-valued vectors. The proposal consists of integrating distributional semantics evidence within the real component of a term vector; whereas, ontological information is encoded in the imaginary component. Our proposal has the merit of lifting the role of complex numbers from a computational byproduct of the model to the very mathematical texture that unifies different levels of semantic information. An empirical instantiation of our proposal is tested in the TREC Medical Record task of retrieving cohorts for clinical studies.