Mining Web Data for Competency Management

Zhu, Jianhan; Gonçalves, Alexandre Leopoldo; Uren, Victoria; Motta, Enrico; Pacheco, Roberto Carlos dos Santos

doi:10.1109/wi.2005.99

Cited by 15 publications

(8 citation statements)

References 12 publications

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“…A body of literature indicates that quite substantial depth of research has been done on this topic. In particularly, a quickly growing number of research contributions have been paid to the application of data mining techniques in supporting the various HRM activities and processes, such as employee selection [4][5], staffing analysis for turnover [6][7][8] and evaluating employee performance in the function of performance management [9][10]. On the stream of techniques used to support the above HRM works, decision trees [11][12][13] (Sivaram & Ramar, 2010), support vector machines [14] and neural nets [15] have been widely employed.…”

Section: Introductionmentioning

confidence: 99%

Assessing Personal Performance with M-SVMs

Gui

Zhang

et al. 2014

2014 Seventh International Joint Conference on Computational Sciences and Optimization

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In view of the significance of personal performance assessment in the working metrics of human resource management, this study proposes to set up a multi-class Support Vector Machine based model to elaborate how to evaluate the staffs' performance effectively and efficiently. Data samples are collected from a construction company in China, and used to justify the proposed model against the selected well-established classifiers. The experimental results suggest it to be a promising alternative for assessing personal performance.

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Section: Introductionmentioning

confidence: 99%

Assessing Personal Performance with M-SVMs

Gui

Zhang

et al. 2014

2014 Seventh International Joint Conference on Computational Sciences and Optimization

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“…Given an entity, we can use either standard statistical measures such as mutual information [12] or our own CORDER method [11] to find related entities in a textual corpus. Given a document, suppose there are a number of entities originally occurring in the document, however, entities which are related to these original entities may not necessarily also occur in the document, e.g., Thomas and Jack both work on X but one document only mentions Thomas works on X.…”

Section: Introductionmentioning

confidence: 99%

“…LRD is based on our own CORDER algorithm [11]. LRD can be viewed as an unsupervised machine learning method, i.e., the method does not need either richly annotated corpora required by supervised learning methods or instances of relations as initial seeds for weakly supervised learning methods.…”

Section: Introductionmentioning

confidence: 99%

LRD: Latent Relation Discovery for Vector Space Expansion and Information Retrieval

Gonçalves¹,

Zhu

Song

et al. 2006

Advances in Web-Age Information Management

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Abstract. In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships between them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.

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“…Experiments on our departmental web site [4] show that CORDER can discover relations with high precision, recall, and ranking accuracy. We intend to use CORDER for ontology maintenance, aiming to overcome the disconnection that we see between static organizational ontologies, as designed by managers, and the real dynamic situation, as experienced by communities of practice.…”

mentioning

confidence: 99%