International Journal of Knowledge Engineering and Data Mining 

About: International Journal of Knowledge Engineering and Data Mining is an academic journal published by Inderscience Enterprises Ltd.. The journal publishes majorly in the area(s): Computer science & Association rule learning. It has an ISSN identifier of 1755-2087. It is also open access. Over the lifetime, 90 publications have been published receiving 378 citations.

Motivation mechanisms for participation in human-driven semantic content creation

Abstract: In the last few years, semantic technologies are continuously maturing and many applications are adopted in various field. To take a step towards overcoming the knowledge acquisition bottleneck, the challenge of generating semantic content persists. It usually requires the involvement of humans, thus motivations and incentives mechanisms that might foster human participation in the semantic content creation should be analysed. We review motivation structures of different successful communities (online communities, social web communities, open source software communities), analyse motivation mechanisms for incentivising semantic content creation, and provide some useful insights for the design of semantic annotation tools which would embed incentives mechanisms.

Machine learning: the new 'big thing' for competitive advantage

Abstract: While machine learning (ML) has existed for a long time, the business world's focus on it may seem like an overnight development. The technology has been steadily growing since the early days of big data. However, the ability to give machines access to big data and allow them to apply complex mathematical calculations repetitively, while learning for themselves, is a new development. The key objective of this article is to propose a conceptual model for successful implementation of ML in organisations. This article also covers some of the potential benefits of ML, and provides a guide to some of the opportunities that are available for using ML in various businesses. Furthermore, this study reviews key attributes of successful ML platforms and illustrates how to overcome some of the key implementation challenges. Finally, this study highlights some of the successful implementations of ML solutions used in the manufacturing and service industry.

An associative rule-based classifier for Arabic medical text

Abstract: Text classification is one of the methods used for managing, organising and retrieving the needed data among the huge available text. Several methods have been proposed to manipulate the text classification problem. In recent years, some studies proposed the use of Associative Classification AC approach. This paper examines an associative classification approach for the categorisation of text typed in Arabic language and related to medical domain. The approach discovers a set of association rules to build a classification model where three steps were applied to build the model: generating association rules, rule ordering and pruning, and then validation. The results of the experiments showed that the ordered decision list approach outperforms other approaches with accuracy reaching 90.6%. In general, the results of the experiments showed that association rule mining is a suitable method for building good classification models to categorise Arabic medical text.

Clustering news articles using efficient similarity measure and N-grams

Abstract: The rapid progress of information technology and web makes it easier to store huge amount of collected textual information, e.g., blogs, news articles, e-mail messages, reviews and forum postings. The growing size of textual dataset with high-dimensions and natural language pose a big challenge making it hard for such information to be categorised efficiently. Document clustering is an automatic unsupervised machine learning technique that aimed at grouping related set of items into clusters or subsets. The target is creating clusters with high internal coherence, but different from each other substantially. This paper presents a new document clustering technique using N-grams and efficient similarity measure known as 'improved sqrt-cosine similarity measure'. Comprehensive experiments are conducted to evaluate our proposed clustering technique and compared with an existing method. The results of the experiments show that our proposed clustering technique outperforms the existing techniques.

A conceptual graph approach to semantic similarity computation method for e-service discovery

Abstract: E-services are services that make themselves available on the web and can be invoked over the internet. These services can be composed together to form new services. With the great growth of the number of services on the web it is indispensable to adopt an effective and efficient way to discover and compose them to meet the users' requirements. However, current web services description languages such as WSDL and OWL-S do not provide semantic support for automatic search and composition of services. In this paper, based on the Conceptual Graph theory, we propose a contextual based conceptual model for automatic search and match for services using two kinds of contextual information – service characteristics and inter-service semantic relations. We explore the theoretical models for inter-service and service characteristics semantic similarity computation to match users' service queries and based on the conceptual model we present an effective service matching algorithm.