C-SAW---contextual semantic alignment of ontologies: using negative semantic reinforcement

Understanding the meaning of each term in an ontology is essential for successfully integrating and aligning ontologies. Much ontology integration research to date is focused on syntactic, structural and semantic matching where the actual meaning of the concepts is disregarded. The C-SAW approach to ontology alignment is based on the Contextualizing the concepts by using a set of Semantic Alignment Words (C-SAW). The C-SAW approach is enhanced by Negative Semantic Reinforcement (NSR), where additional semantic meaning can be added to the set of Semantic Alignment Words, by considering words which are unrelated to the concept

Development of an Enhanced Generic Data Mining Life Cycle (DMLC)

Data mining projects are complex and have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for large scale data mining projects. The paper provides a detailed view of the design and development of a generic data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve project success rates and strategic decision support. An extensive analysis of eight existing life cycles leads to a list of advantages, disadvantages, and characteristics of the life cycles. This is extended and generates a conglomerate of several guidelines which serve as the foundation for the development of a new generic data mining life cycle. The new life cycle is further developed to incorporate process, people and data aspects. A detailed study of the human resources involved in a data mining project enhances the DMLC

An Enhanced Data Mining Life Cycle

Data mining projects are complex and can have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for data mining projects, its team members and their role. The paper provides a detailed view of the design and development of the data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve project success rates and strategic decision support. An extensive analysis of eight life cycles leads to a list of advantages, disadvantages, and characteristics of the life cycles. This is extended and generates a conglomerate of several guidelines which serve as the foundation for the development of a new generic data mining

Contextual Semantic Integration For Ontologies

Information integration in organisations has been hindered by differences in the software applications used and by the structure and semantic differences of the different data sources (de Bruijn, 2003). This is a common problem in the area of Enterprise Application Integration (EAI) where numerous ah-hoc programs have typically been created to perform the integration process. More recently ontologies have been introduced into this area as a possible solution to these problems, but most of the current approaches to ontology integration only address platform, syntactic and structural differences and do not address the semantic differences between the data sources (de Bruijn, 2003). For ontology semantic integration the underlying meaning of each element is needed. An approach based on introducing the contextualisation of the terms used in an ontology is proposed. This approach is called Contextual Semantic Integration for Ontologies

Bi-directional Ontology Versioning BOV

his paper defines a new type of ontology versioning: Bi-directional Ontology Versioning: BOV. BOV provides bi-directional mappings and transformations between concepts in two ontology versions. BOV is identified by two levels mapping processes: linguistic mapping and structural mapping. BOV can satisfy the requirement of mapping in distributed environment

Development of an Enhanced Generic Data Mining Life Cycle (DMLC)

Data mining projects are complex and have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for large scale data mining projects. The paper provides a detailed view of the design and development of a generic data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve project success rates and strategic decision support. An extensive analysis of eight existing life cycles leads to a list of advantages, disadvantages, and characteristics of the life cycles. This is extended and generates a conglomerate of several guidelines which serve as the foundation for the development of a new generic data mining life cycle. The new life cycle is further developed to incorporate process, people and data aspects. A detailed study of the human resources involved in a data mining project enhances the DMLC

An Enhanced Data Mining Life Cycle

Data mining projects are complex and can have a high failure rate. In order to improve project management and success rates of such projects a life cycle is vital to the overall success of the project. This paper reports on a research project that was concerned with the life cycle development for data mining projects, its team members and their role. The paper provides a detailed view of the design and development of the data mining life cycle called DMLC. The life cycle aims to support all members of data mining project teams as well as IT managers and academic researchers and may improve project success rates and strategic decision support. An extensive analysis of eight life cycles leads to a list of advantages, disadvantages, and characteristics of the life cycles. This is extended and generates a conglomerate of several guidelines which serve as the foundation for the development of a new generic data mining life cycle. A detailed study of the human resources involved in a data mining project enhances the DMLC

The IP Multimedia Subsystem (IMS) & the Mobile Internet: Opportunities for the Mobile OperatorTHE IP MULTIMEDIA SUBSYSTEM (IMS) & THE MOBILE INTERNET: Opportunities for the Mobile Operator

The IP Multimedia Subsystem (IMS) is a new mobile communications architecture, which enables many new and innovative services, and can extend the possibilities of mobile Internet application development. These mobile Internet applications and the IMS, are considered in terms of the impact that they can have on the critical success factors (CSFs) of mobile operators. The CSFs identified are particular to mobile operators that are competing in a highly saturated (in terms of mobile penetration) marketplace, and that are facing the threat of increasing competition

Feature Engineering vs Feature Selection vs Hyperparameter Optimization in the Spotify Song Popularity Dataset

Research in Featuring Engineering has been part of the data pre-processing phase of machine learning projects for many years. It can be challenging for new people working with machine learning to understand its importance along with various approaches to find an optimized model. This work uses the Spotify Song Popularity dataset to compare and evaluate Feature Engineering, Feature Selection and Hyperparameter Optimization. The result of this work will demonstrate Feature Engineering has a greater effect on model efficiency when compared to the alternative approaches

Sentiment Classification Using Negation as a Proxy for Negative Sentiment

We explore the relationship between negated text and neg- ative sentiment in the task of sentiment classification. We propose a novel adjustment factor based on negation occur- rences as a proxy for negative sentiment that can be applied to lexicon-based classifiers equipped with a negation detec- tion pre-processing step. We performed an experiment on a multi-domain customer reviews dataset obtaining accuracy improvements over a baseline, and we further improved our results using out-of-domain data to calibrate the adjustment factor. We see future work possibilities in exploring nega- tion detection refinements, and expanding the experiment to a broader spectrum of opinionated discourse, beyond that of customer reviews