Most common database management systems represent information in a simple record-based format. Semantic modeling provides richer data structuring capabilities for database applications. In particular, research in this area has articulated a number of constructs that provide mechanisms for representing structurally complex interrelations among data typically arising in commercial applications. In general terms, semantic modeling complements work on knowledge representation (in artificial intelligence) and on the new generation of database models based on the object-oriented paradigm of programming languages. This paper presents an in-depth discussion of semantic data modeling. It reviews the philosophical motivations of semantic models, including the need for high-level modeling abstractions and the reduction of semantic overloading of data type constructors. It then provides a tutorial introduction to the primary components of semantic models, which are the explicit representation of objects, attributes of and relationships among objects, type constructors for building complex types, ISA relationships, and derived schema components. Next, a survey of the prominent semantic models in the literature is presented. Further, since a broad area of research has developed around semantic modeling, a number of related topics based on these models are discussed, including data languages, graphical interfaces, theoretical investigations, and physical implementation strategies.

Inamultidatabase system, schematic con icts between two objects are usually of interest only when the objects have some semantic similarity. We use the concept of semantic proximity, which is essentially an abstraction/mapping between the domains of the two objects associated with the context of comparison. An explicit though partial context representation is proposed and the speci city relationship between contexts is de ned. The contexts are organized as a meet semi-lattice and associated operations like the greatest lower bound (glb) are de ned. The context of comparison and the type of abstractions used to relate the two objects form the basis of a semantic taxonomy. Atthesemantic level, the intensional description of database objects provided by the context is expressed in a description logic language. Schema correspondences are used to store mappings from the semantic level to the data level and are associated with the respective contexts. Inferences about database content at the federation level are modeled as changes in the context and the associated schema correspondences. We try to reconcile the dual (schematic and semantic) perspecitves by: enumerating possible semantic similarities between objects having schema and data conicts, and modeling schema correspondences as the projection of semantic proximity wrt context. 1

A graph-oriented object database model (GOOD) is introduced as a theoretical basis for database systems in which manipulation as well as conceptual representation of data is transparently graph-based. In the GOOD model, the scheme as well as the instance of an object database is represented by a graph, and the data manipulation is expressed by graph transformations. These graph transformations are described using five basic operations and a method construct, all with a natural semantics. The basic operations add and delete objects and edges in function of the matchings of a pattern. The expressiveness of the model in terms of object-oriented modeling and data manipulation power is investigated. Index terms: Database models, query languages, graph transformations, objectoriented databases, user interfaces. Preliminary versions of this paper were presented at the 9th ACM Symposium on Principles of Database Systems [16] and the 1990 ACM SIGMOD International Conference on Management of D...

Graph database models can be characterized as those where data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models flourished in the eighties and early nineties in parallel to object oriented models and their influence gradually faded with the emergence of other database models, particularly the geographical, spatial, semistructured and XML. Recently, the need to manage information with inherent graph-like nature has brought back the relevance of the area. In fact, a whole new wave of applications for graph databases emerged with the development of huge networks (e.g. Web, geographical systems, transportation, telephones), and families of networks generated due to the automation of the process of data gathering (e.g. social and biological networks). The main objective of this survey is to present in a single place the work that has been done in the area of graph database modeling, concentrating in data structures, query languages and integrity constraints.

Abstract not found

According to the logical model of Information Retrieval (IR), the task of IR can be described as the extraction, from a given document base, of those documents d that, given a query q, make the formula d → q valid, where d and q are formulae of the chosen logic and “→ ” denotes the brand of logical implication formalized by the logic in question. In this paper, although essentially subscribing to this view, we propose that the logic to be chosen for this endeavour be a Terminological Logic (TL): accordingly, the IR task becomes that of singling out those documents d such that d � q, where d and q are terms of the chosen TL and “�” denotes subsumption between terms. We call this the terminological model of IR. TLs are particularly suitable for modelling IR; in fact, they can be employed: 1) in representing documents under a variety of aspects (e.g. structural, layout, semantic content); 2) in representing queries; 3) in representing lexical, “thesaural ” knowledge. The fact that a single logical language can be used for all these representational endeavours ensures that all these sources of knowledge will participate in the retrieval process in a uniform and principled way. In this paper we introduce Mirtl, a TL for modelling IR according to the above guidelines; its syntax, formal semantics and inferential algorithm are described. 1

This paper studies heterogeneous database systems from the multiple (poly) source @rrt) perspective. It aims at addressing issues such as “where is the data from ” and “which intermediate data sources were used to arrive at that data ”- issues which are critical to many users in utilizing information composed from multiple sources. Specifically, it presents a polygen model for resolving the Data Source Tagging and Intermediate Source Tagging problems. Secondly, it presents a data-driven query translation mechanism for mapping a polygen query into a set of local queries dynamically. A concrete example is also provided to exemplify polygen query processing. The significance of this paper lies not only in a precise characterization of a practical problem and a solution per se, but also in the establishment of a foundation for resolving many other critical research issues such as domain mismatch, semantic reconciliation, and data conflict amongst data retrieved from different sources. In a federated database environment with hundreds of databases, all of these issues are critical to their effective USt!. I.

Abstract. This paper studies the RDF model from a database perspective. From this point of view it is compared with other database models, particularly with graph database models, which are very close in motivations and use cases to RDF. We concentrate on query languages, analyze current RDF trends, and propose the incorporation to RDF query languages of primitives which are not present today, based on the experience and techniques of graph database research. 1

In this paper, we motivate and present a data model for conceptual design of structural and behavioural aspects of databases. We follow an object centered design paradigm in the spirit of semantic data models. The specification of structural aspects is divided into modelling of object structures and modelling of data types used for describing object properties. The specification of object structures is based on an Extended Entity--Relationship (EER) model. The specification of behavioural aspects is divided into the modelling of admissible database state evolutions by means of temporal integrity constraints and the formulation of database (trans)actions. The central link for integrating these design components is a descriptive logic-- based query language for the EER model. The logic part of this language is the basis for static constraints and descriptive action specifications by means of pre- and postconditions. A temporal extension of this logic is the specification language for tem...

Distributed systems are characterised by complexity, large numbers of diverse components and multiple autonomous managements. There is a need for multiple coexisting management views and boundaries of responsibility. A means of grouping components into units to which a common management policy can apply is required. Management of communication systems and distributed processing is a subject of major concern in standardisation working groups, computer manufacturers and enterprises running large computer networks. This paper defines a framework for managing large distributed systems in terms of managed objects, a structure for implementing manager objects and the use of domains as a means of grouping objects for management purposes. Access rules specify what operations managers can perform on managed objects. The paper develops and refines the concept of a domain. It discusses the domain relationships needed to partition and structure the management of large distributed systems to delega...