Towards Databases for Knowledge Representation

Fundamental notions of relative information capacity between database structures are studied in the context of the relational model. Four progressively less restrictive formal definitions of "dominance" between pairs of relational database schemata are given. Each of these is shown to capture intuitively appealing, semantically meaningful properties which are natural for measures of relative information capacity between schemata. Relational schemata, both with and without key dependencies, are studied using these notions. A significant intuitive conclusion concerns the informal notion of relative information capacity often suggested in the conceptual database literature, which is based on accessability of data via queries. Results here indicate that this notion is too general to accurately measure whether an underlying semantic connection exists between database schemata. Another important result of the paper shows that under any natural notion of information capacity equivalence, two relational schemata (with no dependencies) are equivalent if and only if they are identical (up to re-ordering of the attributes and relations). The approach and definitions used here can form part of the foundation for a rigorous investigation of a variety of important database problems involving data relativism, including those of schema integration and schema translation.

Ontologies represents main building material for creation of Semantic Web. Without ontologies it could not be possible to enable machine processability of data on the Semantic Web. Data is usually stored inside relational databases that represent the backbone for dynamical web sites. That data represents the invisible Web, ignored by search engines and Web crawlers. This paper presents a way to extract semantics from relational database schema and create OWL ontology automatically. Because relational database schema can capture very limited semantics, ontologies are enriched with additional WordNet semantics.

Domain descriptions should represent more than the characteristics of data and the operations on it. They should be "semantic" in the sense that they may represent information such as the meanings of special terms used in the business, as well as goals and rules. ER models are often described as "semantic data models". However, the correspondence between ER and natural language is through syntactic rather than through semantic constructs. Conceptual modeling languages and knowledge representation techniques are more appropriate for representing domain meaning. Modern research in linguistics, semantics, and artificial intelligence provides valuable insight into basic issues regarding such representations. Domain descriptions must use languages based on generally-accepted linguistic and knowledge representation principles.

In this paper, a new Distribution Management System (DMS) framework based on security region is proposed. First, the concept of Distribution System Security Region (DSSR) is introduced. DSSR is capable to describe the N − 1 security boundary of the whole distribution network, including the secure output range of DGs. This new theoretic tool provides a chance for the implementation of real-time security analysis and active controls in DMS. Second, this paper proposes and describes five security states for distribution system. Third, an upgraded DMS enhanced with DSSR is proposed, which consists of advanced security functions such as preventive and predictive control of the trajectory of operating points. Finally, a practical case is presented to simulate the proposed DSSR-enhanced DMS, in which both the security region of network and the output range of DGs are calculated. Typical security functions are also demonstrated. In conclusion, the new DMS framework aims to help operate the system closed to its security boundary in order to improve the efficiency significantly within same security standard. This work is beneficial for future low carbon distribution systems with high penetration rate of DGs.

This paper proposes a novel open-architecture approach to the design of digital controller hardware for power electronics systems. The paper discusses the benefits of such an approach and compares it to the more conventional centralized controller approach. Prototypes of the three key open-architecture functional blocks: high-speed serial communication link, hardware manager and application manager were built in order to test their performance on a representative three-phase 100 kVA converter. Experimental results verify the feasibility of the proposed approach.

This is a brief overview of terminology and issues related to Knowledge Representation (here-after KR) research, intended primarily for researchers working on Semantic Data Models within Database Management and Program Specifications within Programming Languages/Software Engineering. Knowledge Representation is a central problem in Artificial Intelligence (AI) today. Its importance stems from the fact that the current design paradigm for “intelligent” systems stresses the need for expert knowledge in the system along with associated knowledge-handling facilities. This paradigm is in sharp contrast to earlier ones which might be termed “power-oriented” [Goldstein and Papert 77] in that they placed an emphasis on general purpose heuristic search techniques [Nilsson 71].

Mapping relational database schema to ontology is a critical task for ontology-based heterogeneous database integration. This paper proposes a hybrid approach combining element-level and structure-level techniques to discover the correspondences between relational database schema and ontology. In the process of element-level matching, string-based and Word Net sense-based techniques are combined to calculate the name similarity between elements. This paper analyzes different characteristics exhibited by matching element pairs of various types and describes how to use these characteristics to calculate the structure similarity. A series of experiments indicate that our approach performs better than the previous ones.

In this paper, we introduce a XML modeling methodology to design W3C XML schema using UML and we propose a unified design methodology for relational database schema to store XML data efficiently in relational databases. In the second place, in order to verify objectivity of the unified design methodology. Using Ronald Bourret’s method, first we introduce the method of the transformation from XML schema to the object model and second we translate the object model into the relational database schema. Therefore we show the mutual consistency between those consequences, and so can verify the unified design methodology, we proposed in this paper.KeywordsClass DiagramCyclic GraphClass TableMutual ConsistencyDefault ClauseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The rapid growth of available data arises the need for more sophisticated techniques for semantic access to information. It has been proved that using conceptual model or ontology over relational data sources is necessary to overcome many problems related with accessing the structured data. However, the task of wrapping the data residing in a database by means of an ontology is mainly done manually.The research we are carrying out studies the reuse of relational sources in the context of semantics-based access to information. This problem is tackled in two phases: (i) extracting semantics hidden in the relational sources by wrapping them by means of an ontology, (ii) understanding the methodology for semantic extension of such ontologies. In this paper we focus on the first sub-problem and present an automatic procedure for extracting from a relational database schema its conceptual view. The semantic mapping between the database and its conceptualization is captured by associating views over the data source to elements of the extracted ontology. To represent the extracted conceptual model we use an ontology language, rather that a graphical notation, in order to provide precise formal semantics. Our approach uses heuristics based on ideas of standard relational schema design and normalization. Under this we formally prove that our technique preserves the semantics of constraints in the database. Therefore, there is no data loss, and the extracted model constitutes a faithful wrapper of the relational database.

Intelligent systems and ambient-assisted living (AAL) technologies actually represent an important research area, mainly due to the rapidly aging society, the increasing cost of healthcare and the growing importance that individuals place on living independently. The general goal is to create intelligence systems able to support people with specific demands to live longer in their preferred environment thanks to intelligent, sensitive and responsive devices. The research describes the design and development of a service-oriented system architecture where different smart objects are combined to offer assistive functionalities to elderly people. The design is driven by a user-centred approach and human-oriented principles. A prototypal system has been realised in the context of an Italian research project promoted by National Institute on Health and Science of Aging (INRCA). The result is an interoperable and flexible platform that allows creating user-centred services for independent living.

Based upon a robust Bidirectional Scatter Distribution Function (BSDF) data reporting format, a full featured BSDF database has been developed. The database uses a relational database schema and provides a rich query ability through the support of Structured Query Language (SQL) statements. The database can be searched and reports generated for such items as sample type, BSDF range, measurement and laboratory environment. The database was developed to provide support functionality for a light scatter modeling program. It represents a step toward convenient use of existing BSDF data and opens the door to a BSDF data library that could be accessed by many facilities.

Automatically converting text into formal models such as Enhanced Entity Relationship Diagrams, Class Diagrams and Relational Database Schemas is a time and effort saving process, but it is not a trivial process. There are a good number of rules required to convert Natural Language text into formal models. The purpose of this paper is to review existing rules, explain their limitations, and derive new rules for tackling those limitations and thereby helping to obtain an accurate conversion. The rules are divided into three groups: rules to define entities/classes/tables; rules to define relationship between entities/classes/tables and rules to define attributes of entities/classes/tables.

The use of rewriting systems to transform a given expression into a simpler one has promoted the use of rewriting logic in several areas and, particularly, in Software Engineering. Unfortunately, this application has not reached the treatment of Functional Dependencies contained in a given relational database schema. The reason is that the different sound and complete axiomatic systems defined up to now to manage Functional Dependencies are based on the transitivity inference rule. In the literature, several authors illustrate different ways of mapping inference systems into rewriting logics. Nevertheless, the explosive behavior of these inference systems avoids the use of rewriting logics for classical FD logics. In a previous work, we presented a novel logic named SL FD whose axiomatic system did not include the transitivity rule as a primitive rule.In this work we consider a new complexity criterion which allows us to introduce a new minimality property for FD sets named atomic-minimality. The SL FD logic has allowed us to develop the heart of this work, which is the use of Rewriting Logic and Maude 2 as a logical framework to search for atomic-minimality.KeywordsKnowledge RepresentationReasoningRewriting LogicRedundancy Removal

Knowledge of the dependencies and correlations which exist between data units in a relational database is of paramount importance when designing a distributed database since discovering such correlations would be crucial for reaching an optimal data distribution. The knowledge may also be used to improve the performance of a centralized database through a reorganization process. In this paper, we propose a data mining approach for identifying groups of correlated tables in a relational database schema by considering the work profile of the database. We analyze the query history to find sets of tables that are frequently accessed together. The sets of tables that are found overlap in general and we therefore turn each set of tables into a fuzzy set and determine the degree of membership of each table in each of the fuzzy sets. Our approach offers a high degree of flexibility and can be easily customized to produce result with desired amount of detail. The outcome will be highly valuable for guiding a database administrator in producing a better allocation plan. It also guides the database designer in deriving a well optimized fragmentation plan. Our experiment shows the viability and power of our approach. The proposed approach can also be extended to find correlations between smaller data units such as fragments, rows or columns.

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better. Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.