Design issues for agent-based resource locator systems

While knowledge is viewed by many as an asset, it is often difficult to locate particularitems within a large electronic corpus. This paper presents an agent based framework for the location of resources to resolve a specific query, and considers the associated design issue. Aspects of the work presented complements current research into both expertise finders and recommender systems. The essential issues for the proposed design are scalability, together ith the ability to learn and adapt to changing resources. As knowledge is often implicit within electronic resources, and therefore difficult to locate, we have proposed the use of ontologies, to extract the semantics and infer meaning to obtain the results required. We explore the use of communities of practice, applying ontology-based networks, and e-mail message exchanges to aid the resource discovery process

The right expert at the right time and place: From expertise identification to expertise selection

We propose a unified and complete solution for expert finding in organizations, including not only expertise identification, but also expertise selection functionality. The latter two include the use of implicit and explicit preferences of users on meeting each other, as well as localization and planning as important auxiliary processes. We also propose a solution for privacy protection, which is urgently required in view of the huge amount of privacy sensitive data involved. Various parts are elaborated elsewhere, and we look forward to a realization and usage of the proposed system as a whole

Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding

Modern expert nding algorithms are developed under the assumption that all possible expertise evidence for a person is concentrated in a company that currently employs the person. The evidence that can be acquired outside of an enterprise is traditionally unnoticed. At the same time, the Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only

Pengembangan Sistem Pencari Pakar Dengan Menggunakan Metode Association Rules

Suatu organisasi membutuhkan informasi tentang kondisi kepakaran. Salah satu aspek yang dapat digunakan dalam menentukan kepakaran di institusi pendidikan tinggi adalah publikasi ilmiah yang pernah dibuat. Penelitian ini bertujuan untuk mendapatkan informasi tentang kepakaran berdasarkan publikasi yang pernah dibuat. Penelitian ini menggunakan metode association rules untuk mendapatkan informasi tentang kepakaran berdasarkan publikasi yang ada. Di sini, dilihat tingkat kemunculan nama seseorang pada koleksi publikasi pada bidang yang sejenis. Hasil penelitian ini sebuah model pencarian informasi yang bermanfaat bagi manajemen puncak untuk mengetahui kepakaran seseorang. Dengan demikian, pengambilan keputusan oleh manajemen puncak dapat terlaksana dengan bantuan pakar yang tepat

Exploiting synergy between ontologies and recommender systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations.Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

Exploiting Synergy Between Ontologies and Recommender Systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

Expert Finding in Disparate Environments

Providing knowledge workers with access to experts and communities-of-practice is central to expertise sharing, and crucial to effective organizational performance, adaptation, and even survival. However, in complex work environments, it is difficult to know who knows what across heterogeneous groups, disparate locations, and asynchronous work. As such, where expert finding has traditionally been a manual operation there is increasing interest in policy and technical infrastructure that makes work visible and supports automated tools for locating expertise. Expert finding, is a multidisciplinary problem that cross-cuts knowledge management, organizational analysis, and information retrieval. Recently, a number of expert finders have emerged; however, many tools are limited in that they are extensions of traditional information retrieval systems and exploit artifact information primarily. This thesis explores a new class of expert finders that use organizational context as a basis for assessing expertise and for conferring trust in the system. The hypothesis here is that expertise can be inferred through assessments of work behavior and work derivatives (e.g., artifacts). The Expert Locator, developed within a live organizational environment, is a model-based prototype that exploits organizational work context. The system associates expertise ratings with expert’s signaling behavior and is extensible so that signaling behavior from multiple activity space contexts can be fused into aggregate retrieval scores. Post-retrieval analysis supports evidence review and personal network browsing, aiding users in both detection and selection. During operational evaluation, the prototype generated high-precision searches across a range of topics, and was sensitive to organizational role; ranking true experts (i.e., authorities) higher than brokers providing referrals. Precision increased with the number of activity spaces used in the model, but varied across queries. The highest performing queries are characterized by high specificity terms, and low organizational diffusion amongst retrieved experts; essentially, the highest rated experts are situated within organizational niches

El factor humano: Instrumentos de medida competencial y estimación.

La importancia del “factor humano” en la gestión de los proyectos de desarrollo de software es vital. Para contribuir a la mejora de la capacidad de las organizaciones en el proceso software, se ha desarrollado un modelo, complementario a CMM, para el diagnóstico de la madurez de los procesos relacionados con el personal, People-CMM. Por otra parte, los modelos de estimación existentes en la Ingeniería de Software integran aspectos relativos a la competencia técnica y general del personal, pero, sin embargo, no establecen correspondencias con los instrumentos de medida competencial y del rendimiento en el establecimiento de los valores de los distintos factores que se utilizan para la estimación. Este artículo, tras realizar un estudio sobre las recomendaciones e iniciativas implantadas para la medición competencial en la industria, y los métodos de estimación sobre factores de personal en los proyectos de desarrollo de software más relevantes, realiza una recomendación para la integración de cada uno de los factores relacionados con el “factor humano” que se recogen en COCOMO II con los instrumentos de gestión que recomienda People-CMM. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Human Factor is a key factor in the software project management. People-CMM has been developed, inside the family of CMM, to contribute to the diagnosis of the maturity of processes related with human resources. By the other side, estimation models in Software Engineering, although they integrate issues of technical and general competencies, do not establish competencies measurement instruments for the factors used in the estimation methods. This paper suggests initiatives to measure human factors taken in COCOMO II with management instruments recommended by People-CMM

The Effects of IT, Task, Workgroup, and Knowledge Factors on Workgroup Outcomes: A Longitudinal Investigation

In order to successfully manage the knowledge-related processes occurring in their workgroups, organizations need to understand how different contingency factors affect the knowledge-related processes of a workgroup, ultimately affecting the workgroup\u27s knowledge outcomes and performance. To obtain a deeper understanding of the longitudinal effects of different contingency factors on knowledge outcomes and performance of workgroups, this dissertation was guided by the research question: Which factors, from the five categories of factors (a) characteristics of the workgroup; (b) characteristics of the tasks assigned to the workgroup; (c) the interface between the workgroup and the tasks; (d) characteristics of the knowledge required to complete the tasks; and (e) characteristics of the information technologies, affect workgroup outcomes, including (i) average consensus among a workgroup\u27s members about each other\u27s areas of knowledge; (ii) average accuracy of knowledge; and (iii) performance of the workgroup, over time, and in what way? Workgroup processes considered were categorized into three groups: processes related to scheduling of tasks, processes related to completion of tasks and processes accompanying those related to completion of tasks. Results indicate that only a subset of contingency factors from each category affect each of the workgroup outcomes. Specifically, average task priority, average knowledge-intensity of subtasks, average propensity to share, time in training phase, probability of non-specific exchange, number of agents, number of locations and average project intensity were found to have a positive effect on average consensus, while average task intensity, average self-knowledge and average number of tasks per agent had negative effect on average consensus. In the case of average accuracy of knowledge, average knowledge level and number of agents were found to have a positive significant effect. Finally, in the case of percentage of project completed, average propensity to share, average knowledge level, average self-knowledge, and time in training phase were found to have a positive significant effect, while average knowledge intensity of subtasks, richness of email, and average direction time were found to have a negative significant effect. Average number of tasks per agent was found to have a significant negative effect between workgroups and positive significant effect within workgroups