International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
About: International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management is an academic conference. The conference publishes majorly in the area(s): Ontology (information science) & Ontology-based data integration. Over the lifetime, 1157 publication(s) have been published by the conference receiving 3655 citation(s).
Topics: Ontology (information science), Ontology-based data integration, Process ontology, Upper ontology, Context (language use)
01 Jan 2015
TL;DR: The findings suggest that universities generally encourage and facilitate the transfer of tacit knowledge; however there are some areas that require improvement.
Abstract: The purpose of this paper is to explore whether Australian universities encourage tacit knowledge transfer. In doing so, the paper also explores the role of managers (academics’ supervisor) in promoting or hampering tacit knowledge transfer and the value given to new ideas and innovation. This study collected data by conducting interviews of academics in four universities and a qualitative narrative analysis was carried out. The findings suggest that universities generally encourage and facilitate the transfer of tacit knowledge; however there are some areas that require improvement. Avenues for improving tacit knowledge transfer call for open communication, peer-trust and unrestricted sharing of knowledge by managers. The study was conducted in four universities, hence limits the generalisability of the findings. This paper will contribute to further research in the discipline of tacit knowledge, provide understanding and guide universities in their tacit knowledge transfer efforts and in particular, encourage the transfer of tacit knowledge.
12 Nov 2015
TL;DR: This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology Evaluation techniques, while discussing their advantages and drawbacks.
Abstract: Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.
12 Nov 2015
TL;DR: The SCUT hybrid sampling method is proposed, which is used to balance the number of training examples in a multi-class setting and, when the SCUT method is used for pre-processing the data before classification, it obtain highly accurate models that compare favourably to the state-of-the-art.
Abstract: Class imbalance is a crucial problem in machine learning and occurs in many domains. Specifically, the two-class problem has received interest from researchers in recent years, leading to solutions for oil spill detection, tumour discovery and fraudulent credit card detection, amongst others. However, handling class imbalance in datasets that contains multiple classes, with varying degree of imbalance, has received limited attention. In such a multi-class imbalanced dataset, the classification model tends to favour the majority classes and incorrectly classify instances from the minority classes as belonging to the majority classes, leading to poor predictive accuracies. Further, there is a need to handle both the imbalances between classes as well as address the selection of examples within a class (i.e. the so-called within class imbalance). In this paper, we propose the SCUT hybrid sampling method, which is used to balance the number of training examples in such a multi-class setting. Our SCUT approach oversamples minority class examples through the generation of synthetic examples and employs cluster analysis in order to undersample majority classes. In addition, it handles both within-class and between-class imbalance. Our experimental results against a number of multi-class problems show that, when the SCUT method is used for pre-processing the data before classification, we obtain highly accurate models that compare favourably to the state-of-the-art.
09 Nov 2016
TL;DR: This work proposes a classification approach to discover the layout of tables in spreadsheets by focusing on the cell level, considering a wide range of features not covered before by related work, and delivers very high accuracy.
Abstract: Spreadsheet applications are one of the most used tools for content generation and presentation in industry and the Web. In spite of this success, there does not exist a comprehensive approach to automatically extract and reuse the richness of data maintained in this format. The biggest obstacle is the lack of awareness about the structure of the data in spreadsheets, which otherwise could provide the means to automatically understand and extract knowledge from these files. In this paper, we propose a classification approach to discover the layout of tables in spreadsheets. Therefore, we focus on the cell level, considering a wide range of features not covered before by related work. We evaluated the performance of our classifiers on a large dataset covering three different corpora from various domains. Finally, our work includes a novel technique for detecting and repairing incorrectly classified cells in a post-processing step. The experimental results show that our approach delivers very high accuracy bringing us a crucial step closer towards automatic table extraction.
01 Jan 2010
TL;DR: A novel database encryption scheme called MV-POPES (Multivalued Partial Order Preserving Encryption Scheme), which allows privacy-preserving queries over encrypted databases with an improved security level and is robust against known plaintext attacks and statistical attacks.
Abstract: Encryption is a well-studied technique for protecting the confidentiality of sensitive data. However, encrypting relational databases affects the performance during query processing. Preserving the order of the encrypted values is a useful technique to perform queries over the encrypted database with a reasonable overhead. Unfortunately, the existing order preserving encryption schemes are not secure against known plaintext attacks and statistical attacks. In those attacks, it is assumed that the attacker has prior knowledge about plaintext values or statistical information on the plaintext domain. This paper presents a novel database encryption scheme called MV-POPES (Multivalued Partial Order Preserving Encryption Scheme), which allows privacy-preserving queries over encrypted databases with an improved security level. Our idea is to divide the plaintext domain into many partitions and randomize them in the encrypted domain. Then, one integer value is encrypted to different multiple values to prevent statistical attacks. At the same time, MV-POPES preserves the order of the integer values within the partitions to allow comparison operations to be directly applied on encrypted data. Our scheme is robust against known plaintext attacks and statistical attacks. MV-POPES experiments show that security for sensitive data can be achieved with reasonable overhead, establishing the practicability of the scheme.
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