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Case adaptation continues to be one of the more difficult aspects of case-based reasoning to automate. This paper looks at several techniques for utilising the implicit knowledge contained in a case base for case adaptation in case-based reasoning systems. The most significant of the techniques proposed are a moderately successful data mining technique and a highly successful artificial neural network technique. Their effectiveness was evaluated on a footwear design problem. [ABSTRACT FROM AUTHOR]

EXtensible Markup Language (XML) has rapidly gained importance as a mechanism for the exchange of information amongst heterogeneous sources over the web. In order to deal with the challenging task of managing the large volumes of data appearing, encoded in XML transactional databases, the need to explore the XML Document Warehouse (XDW) approach is initiated. The applications of the Requirement Engineering (RE) process and Object-Oriented conceptual models have proven their usefulness in building successful software models and solutions. In this paper we introduce an integration methodology for the development of XDWs. Initially we provide a formal notation of the structural design for the XDW conceptual model. Secondly we focus on deriving requirements for the XDWs by exploring the Goal-Question Metric (GQM) approach. We adapt and extend this concept to XDWs and introduce a method for developing warehouse requirements considering user viewpoints and organizational objectives. The implementation of our proposed warehouse requirement derivation model is demonstrated using a case study example extracted from a simplified real-world scenario. [ABSTRACT FROM AUTHOR]

Tree mining has recently attracted a lot of interest in areas such as Bioinformatics, XML mining, Web mining, etc. We are mainly concerned with mining frequent induced and embedded subtrees. While more interesting patterns can be obtained when mining embedded subtrees, unfortunately mining such embedding relationships can be very costly. In this paper, we propose an efficient approach to tackle the complexity of mining embedded subtrees by utilizing a novel Embedding List representation, Tree Model Guided enumeration, and introducing the Level of Embedding constraint. Thus, when it is too costly to mine all frequent embedded subtrees, one can decrease the level of embedding constraint gradually up to 1, from which all the obtained frequent subtrees are induced subtrees. Our experiments with both synthetic and real datasets against two known algorithms for mining induced and embedded subtrees, FREQT and TreeMiner, demonstrate the effectiveness and the efficiency of the technique. [ABSTRACT FROM AUTHOR]

Due to the increasing dependence on semi-structured data, there exists a requirement to model, design, and manipulate self-describing, schema-based, semi-structured data models (e.g. XML) and the associated semantics at a higher level of abstraction than at the instance level. In this paper, we propose to model dynamic properties of a layered XML view model, at the conceptual level, using eXtensible Semantic (XSemantic) nets. [ABSTRACT FROM AUTHOR]

Presently, organizations make significant investments in biomedical data and information sources. These investments are expected to produce reduction of errors and quality improvements in data management and analysis. To sustain achievements in quality and efficiency, healthcare organizations need to be vigilant in monitoring the state of competitiveness of their platforms. In the technology post-adoption period, healthcare organizations use multiple data sources to search for technology-related information to maintain technology parity with, or dominance over their competitors. Firstly this study seeks to answer the following research question: what approaches do healthcare organizations employ with regard to managing diverse sources of data and information in order to sustain their technology competitiveness. Then as an initial step in this direction, in this paper we discuss the conceptual foundation for the phenomenon of data and information sources management capability for the proteomics domain. This is done by discussing the case of Protein Data Source Integration by Protein Ontology. Keywords: Protein Ontology, Biomedical Ontologies, Knowledge Mana-gement, Information Retrieval, Data Integration, Data Semantics. [ABSTRACT FROM AUTHOR]

Several approaches for data interoperation identified by Karp have been implemented for biological databases. We extend Karp's approach for interoperation not only to protein databases but also to knowledge bases and other information sources. This paper outlines algebra for protein data source composition based on our existing work of Protein Ontology (PO). In this paper we consider the case of establishing correspondence between various protein data sources using semantic relationships over the conceptual framework of PO. Here we provide specific set of relationships over PO framework to cover data semantics for integrating data information from diverse protein data sources. These relationships help in defining semantic query algebra for PO to efficiently reason and query the instance store. [ABSTRACT FROM AUTHOR]

Ontologies are the foundation of the Semantic Web (SW) and one of the keys necessary to its success. Conversely, ontology views hold the promise of; (a) provide a manageable portion of a larger ontology for the localized applications and users, (b) enable precise extraction of sub-onlogies of a larger ontology that commits to the main ontology, (c) enable localized customization and usage of the portion of a larger ontology and (d) enable interoperability between large ontology bases and applications. Therefore, it is interesting to look at ontology views and their desired properties, as there exists no agreed upon standard, methodology or formalism to specify, define and materialize ontology views. Thus, in this paper, we elaborate on our own research direction towards proposing a meaningful ontology view formalism and its associated semantics. Keywords: Semantic Web (SW), ontology, ontology views. [ABSTRACT FROM AUTHOR]

Decision making is a tough process. It involves dealing with a lot of uncertainty and projecting what the final outcome might be. Depending on the projection of the uncertain outcome, a decision has to be taken. In a peer-to-peer financial interaction, the trusting agent in order to analyze the Risk has to consider the possible likelihood of failure of the interaction and the possible consequences of failure to its resources involved in the interaction before concluding whether to interact with the probable trusted agent or not. Further it might also have to choose and decide on an agent to interact with from a set of probable trusted agents. In this paper we propose a Fuzzy Risk based decision making system that would assist the trusting agent to ease its decision making process. [ABSTRACT FROM AUTHOR]

Advances in technology and the growth of life sciences are generating ever increasing amounts of data. High-throughput techniques are regularly used to capture thousands of data points in an experiment. The results of these experiments normally end up in scientific databases and publications. Although there have been concerted efforts to capture more scientific data in specialist databases, it is generally acknowledged that only 20 per cent of biological knowledge and data is available in a structured format. The remaining 80 per cent of biological information is hidden in the unstructured scientific results and texts. Protein Ontology (PO) discussed in this paper provides a common structured vocabulary for this structured and unstructured information and provides researchers a medium to share knowledge in proteomics domain. It consists of concepts, which are data descriptors for proteomics data and the relations among these concepts. Protein Ontology provides description for protein domains that can be used to describe proteins in any organism. [ABSTRACT FROM AUTHOR]

Resolving heterogeneity among various protein data sources is a crucial problem if we want to gain more information about proteomics process. Information from multiple protein databases like PDB, SCOP, and UniProt need to integrated to answer user queries. Issues of Semantic Heterogeneity haven't been addressed so far in Protein Informatics. This paper outlines protein data source composition approach based on our existing work of Protein Ontology (PO). The proposed approach enables semi-automatic interoperation among heterogeneous protein data sources. The establishment of semantic interoperation over conceptual framework of PO enables us to get a better insight on how information can be integrated systematically and how queries can be composed. The semantic interoperation between protein data sources is based on semantic relationships between concepts of PO. No other such generalized semantic protein data interoperation framework has been considered so far. [ABSTRACT FROM AUTHOR]

In this paper we propose an approach to integrate protein information from various data sources by defining a Protein Ontology. Protein Ontology provides the technical and scientific infrastructure and knowledge to allow description and analysis of relationships between various proteins. Protein Ontology uses relevant protein data sources of information like PDB, SCOP, and OMIM. Protein Ontology describes: Protein Sequence and Structure Information, Protein Folding Process, Cellular Functions of Proteins, Molecular Bindings internal and external to Proteins, and Constraints affecting the Final Protein Conformation. Details about Protein Ontology are available online at http://www.proteinontology.info/. Keywords: Protein Ontology, Biomedical Ontologies, Knowledge Representation, Information Retrieval, Data Integration. [ABSTRACT FROM AUTHOR]

The emergence of Semantic Web (SW) and the related technologies promise to make the web a meaningful experience. Yet, high level modeling, design and querying techniques proves to be a challenging task for organizations that are hoping utilize the SW paradigm for their industrial applications, which are still using traditional database techniques. To address such an issue, in this paper, we propose a view model for the SW (SW-View), to SW-enable traditional solutions. First we outline the view model, its properties and some modeling issues, followed by some discussions on modeling such views (at the conceptual level). We also provide a brief discussion on how this view model is utilized in the design and construction of materialized ontology views to support extraction of sub-ontologies. [ABSTRACT FROM AUTHOR]

In this paper we proposed a Protein Ontology to integrate protein data and information from various Protein Data Sources. Protein Ontology provides the technical and scientific infrastructure and knowledge to allow description and analysis of relationships between various proteins. Protein Ontology uses relevant protein data sources of information like PDB, SCOP, and OMIM. Protein Ontology describes: Protein Sequence and Structure Information, Protein Folding Process, Cellular Functions of Proteins, Molecular Bindings internal and external to Proteins, and Constraints affecting the Final Protein Conformation. We also created a database of 10 Major Prion Proteins available in various Protein data sources, based on the vocabulary provided by Protein Ontology. Details about Protein Ontology are available online at http://www.proteinontology.info/. [ABSTRACT FROM AUTHOR]