Transformation From Semantic Data Model to Rdf

There have been several efforts to use relational model and database to store and manipulate Resource Description Framework (RDF). They have one general disadvantage, i.e. one is forced to map the model of semantics of RDF into relational model, which will end up in constraints and additional properties, such as, validating each assertion against the RDF schema which also stored as a triplets table. In this paper, we introduce Semantic Data Model as a proposed data model language to store and manipulate Resource Description Framework. This study also tries to prescribe the procedure on transforming a semantic data model into a RDF data model. Keyworsd: Semantic Data Model, Resource Description Framework

A SQL front-end semantic data model

SQLSDM is a front end semantic data model to a SQL relational database management system (RDBMS). SQLSDM provides a more semantically complete RDBMS through the implementation of a Domain and Relational Integrity scheme. SQLSDM provides integrity definition functions and a sub-system to interpret SQL commands . Integrity system tables are created through the use of SQLSDM \u27 s domain definition command and SQL \u27 s CREATE TABLE command. As SQL database update commands are interpreted, SQLSDM uses these integrity tables to enforce domain and referential integrity. SQLSDM operates virtually transparent to the user and provides for greater database consistency and semantic control. Furthermore, SQLSDM is designed and engineered to be a portable front-end that may be implemented on any SQL relational database management system

Extending fuzzy semantic model by advanced decision rules

This paper extends FSM, a recently proposed semantic data model that supports fuzziness, imprecision and uncertainty of real-world. More precisely, the paper proposes four new concepts, decisional grouping, inhibition, multiplicity and selection, which allows enhancing the modeling of real-world applications. It integrates these concepts in FSM by the definition of new decision rules

Constructing a Semantic Data Model for the Field of Material Science through Ontology

This research presents a method for constructing a semantic data model for the field of material science based on ontology. The method involves designing the semantic data model and establishing mapping rules between the data model and the ontology. Data semantic analysis and annotation are carried out based on the semantic data model, supporting the integration and efficient retrieval of heterogeneous data in the material science field. Furthermore, the method utilizes the OWL-S technique to describe, issue, and acquire relevant data services, enabling higher-level service semantic synergy in the material science domain. The proposed method effectively enables semantic integration, intelligent inquiry, and personalized services for data in the material science field

Implementing a semantic data model

OSIRIS is an integrated information architecture which was developed at Oregon State University. SIDUR is the data model upon which the semantic level is based. The semantic level is the mediating level between user's information needs and the stored data. The advantages of providing a semantic database environment include flexibility and independence in data access and schema changes and increased representational power. The purpose of this paper is to describe the prototype implementation of SIDUR in Franz Lisp on a VAX®-11/750. This implementation maps semantic-level database operations into a descriptive formalism, called the BAGAL query language, based solely on present data which is amenable to optimization

A Semantic Data Model to Represent Building Material Data in AEC Collaborative Workflows

The specification of building material is required in multiple phases of engineering and construction projects towards holistic BIM implementations. Building material information plays a vital role in design decisions by enabling different simulation processes, such as energy, acoustic, lighting, etc. Utilization and sharing of building material information between stakeholders are some of the major influencing factors on the practical implementation of the BIM process. Different meta-data schemas (e.g. IFC) are usually available to represent and share material information amongst partners involved in a construction project. However, these schemas have their own constraints to enable efficient data sharing amongst stakeholders. This paper explains these constraints and proposes a methodological approach for the representation of material data using semantic web concepts aiming to support the sharing of BIM data and interoperability enhancements in collaboration workflows. As a result, the DICBM (https://w3id.org/digitalconstruction/BuildingMaterials) ontology was developed which improves the management of building material information in the BIM-based collaboration process.:Abstract 1. Introduction and Background 1.1 Building Information Modeling for collaboration 1.2 Information management in AEC using semantic web technologies 2 DICBM: Digital Construction Building Material Ontology 2.1 Building Material Data in IFC 2.2 Overview of the building material ontology 2.3 Integration of external ontology concepts and roles 2.4 Material Definition 2.5 Material, Material Type, and Material Property 2.6 Data Properties in DICBM 3 Conclusions Acknowledgments Reference

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database