Structured ontology and information retrieval for email search and discovery

This paper discusses an document discovery tool based on formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Document re-ranking by generality in bio-medical information retrieval

Document ranking is an important process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. Traditional document ranking methods are mostly based on the similarity computations between documents and query. In this paper we argue that the similarity-based document ranking is insufficient in some cases. There are two reasons. Firstly it is about the increased information variety. There are far too many different types documents available now for user to search. The second is about the users variety. In many cases user may want to retrieve documents that are not only similar but also general or broad regarding a certain topic. This is particularly the case in some domains such as bio-medical IR. In this paper we propose a novel approach to re-rank the retrieved documents by incorporating the similarity with their generality. By an ontology-based analysis on the semantic cohesion of text, document generality can be quantified. The retrieved documents are then re-ranked by their combined scores of similarity and the closeness of documents’ generality to the query’s. Our experiments have shown an encouraging performance on a large bio-medical document collection, OHSUMED, containing 348,566 medical journal references and 101 test queries.

Concept-based document readability in domain specific information retrieval

Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of readability. Non-expert users may want to see documents with higher readability on the top of the list. Consequently the search results need to be re-ranked in a descending order of readability. It is often not practical for domain experts to manually label the readability of documents for large databases. Computational models of readability needs to be investigated. However, traditional readability formulas are designed for general purpose text and insufficient to deal with technical materials for domain specific information retrieval. More advanced algorithms such as textual coherence model are computationally expensive for re-ranking a large number of retrieved documents. In this paper, we propose an effective and computationally tractable concept-based model of text readability. In addition to textual genres of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document’s readability. Three major readability formulas are proposed and applied to health and medical information retrieval. Experimental results show that our proposed readability formulas lead to remarkable improvements in terms of correlation with users’ readability ratings over four traditional readability measures.

The downsides of downsizing: Communication processes and information needs in the aftermath of a workforce reduction strategy

This study explored the impact of downsizing on levels of uncertainty, coworker and management trust, and communicative effectiveness in a health care organization downsizing during a 2-year period from 660 staff to 350 staff members. Self-report data were obtained from employees who were staying (survivors), from employees were being laid off (victims), and from employees with and without managerial responsibilities. Results indicated that downsizing had a similar impact on the amount of trust that survivors and victims had for management. However, victims reported feeling lower levels of trust toward their colleagues compared with survivors. Contrary to expectations, survivors and victims reported similar perceptions of job and organizational uncertainty and similar levels of information received about changes. Employees with no management responsibilities and middle managers both reported lower scores than did senior managers on all aspects of information received. Implications for practice and the management of the communication process are discussed.

Women, careers and information technology: An introduction

This special issue revisits the relationship between women, work and technology, focusing specifically on gender equity in information technology (IT) employment. Along with theoretical contestation over the broader relationship between gender and technology, arguments about the prospects for women in IT employment have ranged from optimistic to pessimistic extremes. On the one hand, optimists have envisaged a more gender-egalitarian workforce based on new occupations lacking traditional gender markers, and 'young' firms offering positive flexibilities and equal employment opportunity protections. On the other hand, pessimists anticipate that ongoing male dominance over tedmology and competitive pressures in the IT sector will ensure that the most prestigious and highly rewarded jobs remain concentrated in male hands, even as teclmologies and jobs are themselves transformed.

How does your model grow? An empirical investigation of the effects of ontological clarity and application domain size on query performance

Semantic data models provide a map of the components of an information system. The characteristics of these models affect their usefulness for various tasks (e.g., information retrieval). The quality of information retrieval has obvious important consequences, both economic and otherwise. Traditionally, data base designers have produced parsimonious logical data models. In spite of their increased size, ontologically clearer conceptual models have been shown to facilitate better performance for both problem solving and information retrieval tasks in experimental settings. The experiments producing evidence of enhanced performance for ontologically clearer models have, however, used application domains of modest size. Data models in organizational settings are likely to be substantially larger than those used in these experiments. This research used an experiment to investigate whether the benefits of improved information retrieval performance associated with ontologically clearer models are robust as the size of the application domains increase. The experiment used an application domain of approximately twice the size as tested in prior experiments. The results indicate that, relative to the users of the parsimonious implementation, end users of the ontologically clearer implementation made significantly more semantic errors, took significantly more time to compose their queries, and were significantly less confident in the accuracy of their queries.