Integration with Ontologies

One of today’s hottest IT topics is integration, as bringing together information from different sources and structures is not completely solved. The approach outlined here wants to illustrate how ontologies [Gr93] could help to support the integration process

A lightweight web video model with content and context descriptions for integration with linked data

The rapid increase of video data on the Web has warranted an urgent need for effective representation, management and retrieval of web videos. Recently, many studies have been carried out for ontological representation of videos, either using domain dependent or generic schemas such as MPEG-7, MPEG-4, and COMM. In spite of their extensive coverage and sound theoretical grounding, they are yet to be widely used by users. Two main possible reasons are the complexities involved and a lack of tool support. We propose a lightweight video content model for content-context description and integration. The uniqueness of the model is that it tries to model the emerging social context to describe and interpret the video. Our approach is grounded on exploiting easily extractable evolving contextual metadata and on the availability of existing data on the Web. This enables representational homogeneity and a firm basis for information integration among semantically-enabled data sources. The model uses many existing schemas to describe various ontology classes and shows the scope of interlinking with the Linked Data cloud

Conceptual Modelling and The Quality of Ontologies: Endurantism Vs. Perdurantism

Ontologies are key enablers for sharing precise and machine-understandable semantics among different applications and parties. Yet, for ontologies to meet these expectations, their quality must be of a good standard. The quality of an ontology is strongly based on the design method employed. This paper addresses the design problems related to the modelling of ontologies, with specific concentration on the issues related to the quality of the conceptualisations produced. The paper aims to demonstrate the impact of the modelling paradigm adopted on the quality of ontological models and, consequently, the potential impact that such a decision can have in relation to the development of software applications. To this aim, an ontology that is conceptualised based on the Object-Role Modelling (ORM) approach (a representative of endurantism) is re-engineered into a one modelled on the basis of the Object Paradigm (OP) (a representative of perdurantism). Next, the two ontologies are analytically compared using the specified criteria. The conducted comparison highlights that using the OP for ontology conceptualisation can provide more expressive, reusable, objective and temporal ontologies than those conceptualised on the basis of the ORM approach

SODA: Generating SQL for Business Users

The purpose of data warehouses is to enable business analysts to make better decisions. Over the years the technology has matured and data warehouses have become extremely successful. As a consequence, more and more data has been added to the data warehouses and their schemas have become increasingly complex. These systems still work great in order to generate pre-canned reports. However, with their current complexity, they tend to be a poor match for non tech-savvy business analysts who need answers to ad-hoc queries that were not anticipated. This paper describes the design, implementation, and experience of the SODA system (Search over DAta Warehouse). SODA bridges the gap between the business needs of analysts and the technical complexity of current data warehouses. SODA enables a Google-like search experience for data warehouses by taking keyword queries of business users and automatically generating executable SQL. The key idea is to use a graph pattern matching algorithm that uses the metadata model of the data warehouse. Our results with real data from a global player in the financial services industry show that SODA produces queries with high precision and recall, and makes it much easier for business users to interactively explore highly-complex data warehouses.Comment: VLDB201

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Ontology-based composition and matching for dynamic cloud service coordination

Recent cross-organisational software service offerings, such as cloud computing, create higher integration needs. In particular, services are combined through brokers and mediators, solutions to allow individual services to collaborate and their interaction to be coordinated are required. The need to address dynamic management - caused by cloud and on-demand environments - can be addressed through service coordination based on ontology-based composition and matching techniques. Our solution to composition and matching utilises a service coordination space that acts as a passive infrastructure for collaboration where users submit requests that are then selected and taken on by providers. We discuss the information models and the coordination principles of such a collaboration environment in terms of an ontology and its underlying description logics. We provide ontology-based solutions for structural composition of descriptions and matching between requested and provided services

The ERA of FOLE: Foundation

This paper discusses the representation of ontologies in the first-order logical environment FOLE (Kent 2013). An ontology defines the primitives with which to model the knowledge resources for a community of discourse (Gruber 2009). These primitives, consisting of classes, relationships and properties, are represented by the entity-relationship-attribute ERA data model (Chen 1976). An ontology uses formal axioms to constrain the interpretation of these primitives. In short, an ontology specifies a logical theory. This paper is the first in a series of three papers that provide a rigorous mathematical representation for the ERA data model in particular, and ontologies in general, within the first-order logical environment FOLE. The first two papers show how FOLE represents the formalism and semantics of (many-sorted) first-order logic in a classification form corresponding to ideas discussed in the Information Flow Framework (IFF). In particular, this first paper provides a foundation that connects elements of the ERA data model with components of the first-order logical environment FOLE, and the second paper provides a superstructure that extends FOLE to the formalisms of first-order logic. The third paper defines an interpretation of FOLE in terms of the transformational passage, first described in (Kent 2013), from the classification form of first-order logic to an equivalent interpretation form, thereby defining the formalism and semantics of first-order logical/relational database systems (Kent 2011). The FOLE representation follows a conceptual structures approach, that is completely compatible with formal concept analysis (Ganter and Wille 1999) and information flow (Barwise and Seligman 1997)