Использование онтологий для построения семантических запросов в реляционных базах данных

На сьогодні всесвітня павутина є найбільшим сховищем інформації. Проте для використання цієї інформації потрібна людина. Мета Семантичного Вебу — представити інформацію у вигляді, придатному для машинної обробки. Він забезпечує можливість спільного доступу до даних, а також їх повторного використання. Велика частина інформації у всесвітній павутині зберігається в реляційних базах даних. Семантичний Веб не може їх використовувати безпосередньо, але реляційні бази даних можуть бути використані для побудови онтологій. Ця ідея привернула увагу багатьох дослідників, які запропонували алгоритми та відповідні програмні рішення для автоматичного або напівавтоматичного вилучення структурованої синтаксичної інформації. У цій роботі досліджено існуючі рішення, показано різні підходи до формалізації логічної моделі реляційної бази даних і перетворення цієї моделі в OWL (мова Семантичного Вебу). Відзначено проблеми розглянутих рішень, а також виділено аспекти, які необхідно враховувати в майбутньому.Nowadays, the Web is the biggest existing information repository. However, to operate with its information human action is required, but the Semantic Web aims to change this. It provides a common framework that allows data to be shared and reused across application, allowing more uses than the traditional Web. Most of the information on the Web is stored in relational databases and the Semantic Web cannot use such databases. Relational databases can be used to construct ontology as the core of the Semantic Web. This task has attracted the interest of many researches, which have made algorithms (wrappers) able to extract structured syntactic information in an automatic or semi-automatic way. At our work we drew experience from those works. We showed different approaches of formalization of a logic model of relational databases, and a transformation of that model into OWL, a Semantic Web language. We closed this paper by mentioning some problems that have only been lightly touched by database to ontology mapping solutions as well as some aspects that need to be considered by future approaches.На сегодняшний день всемирная паутина является крупнейшим хранилищем информации. Тем не менее для использования этой информации необходим человек. Цель Семантического Веба — представить информацию в виде пригодном для машинной обработки. Он обеспечивает возможность совместного доступа к данным, а также их повторного использования. Большая часть информации во всемирной паутине хранится в реляционных базах данных. Семантический Веб не может их использовать непосредственно, но реляционные базы данных могут быть применены для построения онтологий. Эта идея привлекла интерес многих исследователей, которые предложили алгоритмы и соответствующие программные решения для автоматического или полуавтоматического извлечения структурированной синтаксической информации. В этой работе исследованы существующие решения, показаны различные подходы к формализации логической модели реляционной базы данных и преобразования этой модели в OWL (язык Семантического Веба). Отмечены проблемы рассмотренных решений, а также выделены аспекты, которые необходимо учитывать в будущем

Automatic Transformation of Relational Database Schema into OWL Ontologies

Ontology alignment, or ontology matching, is a technique to map different concepts between ontologies. For this purpose at least two ontologies are required. In certain scenarios, such as data integration, heterogeneous database integration and data model compatibility evaluation, a need to transform a relational database schema to an ontology can arise. To conduct a successful transformation it is necessary to identify the differences between relational database schema and ontology information representation methods, and then to define transformation rules. The most straight forward but time consuming way to carry out transformation is to do it manually. Often this is not an option due to the size of data to be transformed. For this reason there is a need for an automated solution.The automatic transformation of OWL ontology from relational database schema is presented in this paper; the data representation differences between relational database schema and OWL ontologies are described; the transformation rules are defined and the transformation tool’s prototype is developed to perform the described transformation

A framework for integrating and transforming between ontologies and relational databases

Bridging the gap between ontologies, expressed in the Web Ontology Language (OWL), and relational databases is a necessity for realising the Semantic Web vision. Relational databases are considered a good solution for storing and processing ontologies with a large amount of data. Moreover, the vast majority of current websites store data in relational databases, and therefore being able to generate ontologies from such databases is important to support the development of the Semantic Web. Most of the work concerning this topic has either (1) extracted an OWL ontology from an existing relational database that represents as exactly as possible the relational schema, using a limited range of OWL modelling constructs, or (2) extracted a relational database from an existing OWL ontology, that represents as much as possible the OWL ontology. By way of contrast, this thesis proposes a general framework for transforming and mapping between ontologies and databases, via an intermediate low-level Hyper-graph Data Model. The transformation between relational and OWL schemas is expressed using directional Both-As-View mappings, allowing a precise definition of the equivalence between the two schemas, hence data can be mapped back and forth between them. In particular, for a given OWL ontology, we interpret the expressive axioms either as triggers, conforming to the Open-World Assumption, that performs a forward-chaining materialisation of inferred data, or as constraints, conforming to the Closed-World Assumption, that performs a consistency checking. With regards to extracting ontologies from relational databases, we transform a relational database into an exact OWL ontology, then enhance it with rich OWL 2 axioms, using a combination of schema and data analysis. We then apply machine learning algorithms to rank the suggested axioms based on past users’ relevance. A proof-of-concept tool, OWLRel, has been implemented, and a number of well-known ontologies and databases have been used to evaluate the approach and the OWLRel tool.Open Acces

Overcoming database heterogeneity to facilitate social networks: the Colombian displaced population as a case study

In this paper we describe a two-step approach for the publication of data about displaced people in Colombia, whose lack of homogeneity represents a major barrier for the application of adequate policies. This data is available in heterogeneous data sources, mainly relational, and is not connected to social networking sites. Our approach consists in a first step where ontologies are automatically derived from existing relational databases, exploiting the semantics underlying the SQL-DDL schema description, and a second step where these ontologies are aligned with existing ontologies (FOAF in our example), facilitating a better integration of data coming from multiple sources

The use of ontologies for effective knowledge modelling and information retrieval

© 2017 The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems

A survey of RDB to RDF translation approaches and tools

ISRN I3S/RR 2013-04-FR 24 pagesRelational databases scattered over the web are generally opaque to regular web crawling tools. To address this concern, many RDB-to-RDF approaches have been proposed over the last years. In this paper, we propose a detailed review of seventeen RDB-to-RDF initiatives, considering end-to-end projects that delivered operational tools. The different tools are classified along three major axes: mapping description language, mapping implementation and data retrieval method. We analyse the motivations, commonalities and differences between existing approaches. The expressiveness of existing mapping languages is not always sufficient to produce semantically rich data and make it usable, interoperable and linkable. We therefore briefly present various strategies investigated in the literature to produce additional knowledge. Finally, we show that R2RML, the W3C recommendation for describing RDB to RDF mappings, may not apply to all needs in the wide scope of RDB to RDF translation applications, leaving space for future extensions

Fund Finder: A case study of database-to-ontology mapping

The mapping between databases and ontologies is a basic problem when trying to "upgrade" deep web content to the semantic web. Our approach suggests the declarative definition of mappings as a way to achieve domain independency and reusability. A specific language (expressive enough to cover some real world mapping situations like lightly structured databases or not 1st normal form ones) is defined for this purpose. Along with this mapping description language, the ODEMapster processor is in charge of carrying out the effective instance data migration. We illustrate this by testing both the mappings definition and processor on a case study

Storage of Ontology as Database and Representation of Existing Database as Ontology

Semantic Web, the next generation Web, stands out from the traditional Web by incorporating meaning to the information that is accessible to the users. Hence in effect a Web of Data is formed, represented through Ontologies. Most of the data in the traditional Web is being stored in the form of relational databases. Hence for the common man to start with ontologies, this paper tries to propose a mechanism that efficiently stores an entire ontology as a database. To move along with this transition from the traditional Web to Semantic Web, all data must be converted to a form that complies to the Semantic Web concepts. Hence this paper also proposes a mechanism to represent databases as ontology by determining the relationship between the various database components. The system proposed also tries to integrate knowledge of various databases and existing ontologies leading to a global ontology that can be used in various contexts