Modeling Documents as Mixtures of Persons for Expert Finding

In this paper we address the problem of searching for knowledgeable persons within the enterprise, known as the expert finding (or expert search) task. We present a probabilistic algorithm using the assumption that terms in documents are produced by people who are mentioned in them.We represent documents retrieved to a query as mixtures of candidate experts language models. Two methods of personal language models extraction are proposed, as well as the way of combining them with other evidences of expertise. Experiments conducted with the TREC Enterprise collection demonstrate the superiority of our approach in comparison with the best one among existing solutions

A Technology Proposal for a Management Information System for the Director’s Office, NAL.

This technology proposal attempts in giving a viable solution for a Management Information System (MIS) for the Director's Office. In today's IT scenario, an Organization's success greatly depends on its ability to get accurate and timely data on its operations of varied nature and to manage this data effectively to guide its activities and meet its goals. To cater to the information needs of an Organization or an Office like the Director's Office, information systems are developed and deployed to gather and process data in ways that produce a variety of information to the end-user. MIS can therefore can be defined as an integrated user-machine system for providing information to support operations, management and decision-making functions in an Organization. The system in a nutshell, utilizes computer hardware and software, manual procedures, models for analysis planning, control and decision-making and a database. Using state-of-the-art front-end and back-end web based tools, this technology proposal attempts to provide a single-point Information Management, Information Storage, Information Querying and Information Retrieval interface to the Director and his office for handling all information traffic flow in and out of the Director's Office

Third International Workshop on Gamification for Information Retrieval (GamifIR'16)

Stronger engagement and greater participation is often crucial to reach a goal or to solve an issue. Issues like the emerging employee engagement crisis, insufficient knowledge sharing, and chronic procrastination. In many cases we need and search for tools to beat procrastination or to change people’s habits. Gamification is the approach to learn from often fun, creative and engaging games. In principle, it is about understanding games and applying game design elements in a non-gaming environments. This offers possibilities for wide area improvements. For example more accurate work, better retention rates and more cost effective solutions by relating motivations for participating as more intrinsic than conventional methods. In the context of Information Retrieval (IR) it is not hard to imagine that many tasks could benefit from gamification techniques. Besides several manual annotation tasks of data sets for IR research, user participation is important in order to gather implicit or even explicit feedback to feed the algorithms. Gamification, however, comes with its own challenges and its adoption in IR is still in its infancy. Given the enormous response to the first and second GamifIR workshops that were both co-located with ECIR, and the broad range of topics discussed, we now organized the third workshop at SIGIR 2016 to address a range of emerging challenges and opportunities

Special Libraries, December 1975

Volume 66, Issue 12https://scholarworks.sjsu.edu/sla_sl_1975/1009/thumbnail.jp

Finding Relevant Answers in Software Forums

Abstract—Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containin

Myths and Realities about Online Forums in Open Source Software Development: An Empirical Study

The use of free and open source software (OSS) is gaining momentum due to the ever increasing availability and use of the Internet. Organizations are also now adopting open source software, despite some reservations, in particular regarding the provision and availability of support. Some of the biggest concerns about free and open source software are post release software defects and their rectification, management of dynamic requirements and support to the users. A common belief is that there is no appropriate support available for this class of software. A contradictory argument is that due to the active involvement of Internet users in online forums, there is in fact a large resource available that communicates and manages the provision of support. The research model of this empirical investigation examines the evidence available to assess whether this commonly held belief is based on facts given the current developments in OSS or simply a myth, which has developed around OSS development. We analyzed a dataset consisting of 1880 open source software projects covering a broad range of categories in this investigation. The results show that online forums play a significant role in managing software defects, implementation of new requirements and providing support to the users in open source software and have become a major source of assistance in maintenance of the open source projects

Open data and the academy: an evaluation of CKAN for research data management

This paper offers a full and critical evaluation of the open source CKAN software (http://ckan.org) for use as a Research Data Management (RDM) tool within a university environment. It presents a case study of CKAN's implementation and use at the University of Lincoln, UK, and highlights its strengths and current weaknesses as an institutional Research Data Management tool. The author draws on his prior experience of implementing a mixed media Digital Asset Management system (DAM), Institutional Repository (IR) and institutional Web Content Management System (CMS), to offer an outline proposal for how CKAN can be used effectively for data analysis, storage and publishing in academia. This will be of interest to researchers, data librarians, and developers, who are responsible for the implementation of institutional RDM infrastructure. This paper is presented as part of the dissemination activities of the Jisc-funded Orbital project (http://orbital.blogs.lincoln.ac.uk

Collaborative yet independent: Information practices in the physical sciences

In many ways, the physical sciences are at the forefront of using digital tools and methods to work with information and data. However, the fields and disciplines that make up the physical sciences are by no means uniform, and physical scientists find, use, and disseminate information in a variety of ways. This report examines information practices in the physical sciences across seven cases, and demonstrates the richly varied ways in which physical scientists work, collaborate, and share information and data. This report details seven case studies in the physical sciences. For each case, qualitative interviews and focus groups were used to understand the domain. Quantitative data gathered from a survey of participants highlights different information strategies employed across the cases, and identifies important software used for research. Finally, conclusions from across the cases are drawn, and recommendations are made. This report is the third in a series commissioned by the Research Information Network (RIN), each looking at information practices in a specific domain (life sciences, humanities, and physical sciences). The aim is to understand how researchers within a range of disciplines find and use information, and in particular how that has changed with the introduction of new technologies

Automatic Identification of Assumptions from the Hibernate Developer Mailing List

During the software development life cycle, assumptions are an important type of software development knowledge that can be extracted from textual artifacts. Analyzing assumptions can help to, for example, comprehend software design and further facilitate software maintenance. Manual identification of assumptions by stakeholders is rather time-consuming, especially when analyzing a large dataset of textual artifacts. To address this problem, one promising way is to use automatic techniques for assumption identification. In this study, we conducted an experiment to evaluate the performance of existing machine learning classification algorithms for automatic assumption identification, through a dataset extracted from the Hibernate developer mailing list. The dataset is composed of 400 'Assumption' sentences and 400 'Non-Assumption' sentences. Seven classifiers using different machine learning algorithms were selected and evaluated. The experiment results show that the SVM algorithm achieved the best performance (with a precision of 0.829, a recall of 0.812, and an F1-score of 0.819). Additionally, according to the ROC curves and related AUC values, the SVM-based classifier comparatively performed better than other classifiers for the binary classification of assumptions.</p