Ontology of core data mining entities

In this article, we present OntoDM-core, an ontology of core data mining entities. OntoDM-core defines themost essential datamining entities in a three-layered ontological structure comprising of a specification, an implementation and an application layer. It provides a representational framework for the description of mining structured data, and in addition provides taxonomies of datasets, data mining tasks, generalizations, data mining algorithms and constraints, based on the type of data. OntoDM-core is designed to support a wide range of applications/use cases, such as semantic annotation of data mining algorithms, datasets and results; annotation of QSAR studies in the context of drug discovery investigations; and disambiguation of terms in text mining. The ontology has been thoroughly assessed following the practices in ontology engineering, is fully interoperable with many domain resources and is easy to extend

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

A Design Theory for Secure Semantic E-Business Processes (SSEBP)

This dissertation develops and evaluates a Design theory. We follow the design science approach (Hevener, et al., 2004) to answer the following research question: "How can we formulate a design theory to guide the analysis and design of Secure Semantic eBusiness processes (SSeBP)?" Goals of SSeBP design theory include (i) unambiguously represent information and knowledge resources involved in eBusiness processes to solve semantic conflicts and integrate heterogeneous information systems; (ii) analyze and model business processes that include access control mechanisms to prevent unauthorized access to resources; and (iii) facilitate the coordination of eBusiness process activities-resources by modeling their dependencies. Business processes modeling techniques such as Business Process Modeling Notation (BPMN) (BPMI, 2004) and UML Activity Diagrams (OMG, 2003) lack theoretical foundations and are difficult to verify for correctness and completeness (Soffer and Wand, 2007). Current literature on secure information systems design methods are theoretically underdeveloped and consider security as a non-functional requirement and as an afterthought (Siponen et al. 2006, Mouratidis et al., 2005). SSeBP design theory is one of the first attempts at providing theoretically grounded guidance to design richer secure eBusiness processes for secure and coordinated seamless knowledge exchange among business partners in a value chain. SSeBP design theory allows for the inclusion of non-repudiation mechanisms into the analysis and design of eBusiness processes which lays the foundations for auditing and compliance with regulations such as Sarbanes-Oxley. SSeBP design theory is evaluated through a rigorous multi-method evaluation approach including descriptive, observational, and experimental evaluation. First, SSeBP design theory is validated by modeling business processes of an industry standard named Collaborative Planning, Forecasting, and Replenishment (CPFR) approach. Our model enhances CPFR by incorporating security requirements in the process model, which is critically lacking in the current CPFR technical guidelines. Secondly, we model the demand forecasting and capacity planning business processes for two large organizations to evaluate the efficacy and utility of SSeBP design theory to capture the realistic requirements and complex nuances of real inter-organizational business processes. Finally, we empirically evaluate SSeBP, against enhanced Use Cases (Siponen et al., 2006) and UML activity diagrams, for informational equivalence (Larkin and Simon, 1987) and its utility in generating situational awareness (Endsley, 1995) of the security and coordination requirements of a business process. Specific contributions of this dissertation are to develop a design theory (SSeBP) that presents a novel and holistic approach that contributes to the IS knowledge base by filling an existing research gap in the area of design of information systems to support secure and coordinated business processes. The proposed design theory provides practitioners with the meta-design and the design process, including the system components and principles to guide the analysis and design of secure eBusiness processes that are secure and coordinated

Ontological Engineering and Mapping in Multiagent Systems Development

Multiagent systems have received much attention in recent years due to their advantages in complex, distributed environments. Previous work at the Air Force Institute of Technology has developed a methodology for analyzing, designing, and developing multiagent systems, called Multiagent Systems Engineering (MaSE). MaSE currently does not address the information domain of the system, which is an integral part of designing proper system execution. This research extends the MaSE methodology to include the use of ontologies for information domain specification. The extensions allow the designer to specify information flow by using objects from the ontology as parameters in agent conversations. The developer can then ensure system functionality by verifying that each agent has the information required to accomplish the system goals. To fully describe the system design, the developer must describe the relationships between the system ontology and any agent component ontologies. This research also developed a ranking model to assist the user with creating such mappings, to show the relationships between the objects in the ontologies

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org)

Semantic and pragmatic characterization of learning objects

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

An Application Ontology for Reproducibility of Machine Learning Solutions

With Artificial Intelligence and Machine Learning (ML) on the rise, organisations of different scales and nature are looking to utilise ML systems to support their day-to-day operations. Many enterprises find it difficult to adapt existing ML solutions to their organisations without huge investments in solution understanding, customisation, infrastructure enablement and workforce training. Some organisations utilise external service providers to provision their standard analytics services, and this often leads to solutions that either do not fit well with their organisation goals or may lead to the loss of expert knowledge behind the establishment of the AI system. This paper aims to address some of these challenges by proposing an ontology for ensuring the reproducibility of ML models in research as well as their integration within application environments. Our work will ensure that the knowledge about a developed ML system or process is accumulated and recorded within an organisation and can be used in the future, either by new employees or other teams within the organisation. This approach can also be utilised by researchers and developers of ML systems to record and publish metadata of their studies, ensuring that future researchers can reuse their work with minimal effort