From database to semantic web ontology: an overview
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paper_title: Reverse engineering of relational databases: extraction of an EER model from a relational database

paper_content:

Abstract A methodology for extracting an extended Entity-Relationship (EER) model from a relational database is presented. Through a combination of data schema and data instance analysis, an EER model is derived which is semantically richer and more comprehensible for maintenance and design purposes than the original database. Classification schemes for relations and attributes necessary for the EER model extraction are derived and justified. These have been demonstrated to be implementable in a knowledge-based system; a working prototype system which does so is briefly discussed. In addition, cases in which human input is required are also clearly identified. This research also illustrates that the database reverse engineering process can be implemented at a high level of automation.
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paper_title: Ontologies and Databases: More than a Fleeting Resemblance

paper_content:

Formal ontologies can be seen as mathematical objects that form the range of a classical "Tarskian" semantics interpretation mapping of first-order language constructs that could represent situations, functions or procedures related to a given domain. Some design methods and techniques such as view integration that were originally developed for large databases, where the "data models" and their semantics typically are limited to a particular application, could be relevant for this purpose, and we analyze parallelisms and fundamental differences between databases and ontologies. In particular the ORM (Ob jectRole Modeling) method, or rather its precursor NIAM, with its rigorous distinction and handling of so-called lexical and non-lexical knowledge proved to be an interesting candidate to help identify and clarify a number of these issues, and a number of examples are given. We report on research within STARLab’s DOGMA Project that indicates how such methods may or must be adapted to be usable in the context of ontologies, and how they may then help to define ontology updates, the role of domain constraints, and future tools that assist in e.g. the alignment of ontologies.
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paper_title: A reverse engineering approach for migrating data-intensive web sites to the Semantic Web

paper_content:

The Semantic Web is intended to enable machine understandable web content and seems to be a solution for many drawbacks of the current Web. It is based on metadata that formally describe the semantics of Web contents. In this paper we present an integrated and semi-automatic approach for generating shared-understandable metadata for data-intensive Web applications. This approach is based on mapping a given relational schema into an already existing ontology structure using a reverse engineering process. As a case study we present this style of a schema- and data-migration for our Institute web portal. The presented approach can be applied to a broad range of today’s data-intensive Web sites.
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paper_title: Reverse Engineering of Relational Databases to Ontologies

paper_content:

We propose a novel approach to reverse engineering of relational databases to ontologies. Our approach is based on the idea that semantics of a relational database can be inferred, without an explicit analysis of relational schema, tuples and user queries. Rather, these semantics can be extracted by analyzing HTML forms, which are the most popular interface to communicate with relational databases for data entry and display on the Web. The semantics are supplemented with the relational schema and user “head knowledge” to build an ontology. Our approach can be applied to migrating data-intensive Web pages, which are usually based on relational databases, to the ontologybased Semantic Web.
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paper_title: Object-Oriented Conceptual Modeling

paper_content:

Drawing on the author's experience, this introduction to object-oriented databases examines concepts and implementations from a commercial/business viewpoint. It covers systems development and its relationship to conceptual modelling; object-oriented analysis; object-oriented expert/knowledge-based system; extensions of object-oriented concepts in a database; incorporation of exceptions in the object-oriented data model; and hybrid system development.
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paper_title: Reverse engineering of relational databases: extraction of an EER model from a relational database

paper_content:

Abstract A methodology for extracting an extended Entity-Relationship (EER) model from a relational database is presented. Through a combination of data schema and data instance analysis, an EER model is derived which is semantically richer and more comprehensible for maintenance and design purposes than the original database. Classification schemes for relations and attributes necessary for the EER model extraction are derived and justified. These have been demonstrated to be implementable in a knowledge-based system; a working prototype system which does so is briefly discussed. In addition, cases in which human input is required are also clearly identified. This research also illustrates that the database reverse engineering process can be implemented at a high level of automation.
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paper_title: Data reverse engineering: a historical survey

paper_content:

Data reverse engineering is a rapidly growing field which is sometimes misunderstood. In our effort to promote the realization that data reverse engineering is a valuable and essential part of reverse engineering and is now finding its place in software engineering, we present a summary of the history of data reverse engineering. A definition of data reverse engineering, a summary of the history of what has been none and published, a statement about the state-of-the art today, and a comment on the future of DRE is presented.
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paper_title: Ontologies and Databases: More than a Fleeting Resemblance

paper_content:

Formal ontologies can be seen as mathematical objects that form the range of a classical "Tarskian" semantics interpretation mapping of first-order language constructs that could represent situations, functions or procedures related to a given domain. Some design methods and techniques such as view integration that were originally developed for large databases, where the "data models" and their semantics typically are limited to a particular application, could be relevant for this purpose, and we analyze parallelisms and fundamental differences between databases and ontologies. In particular the ORM (Ob jectRole Modeling) method, or rather its precursor NIAM, with its rigorous distinction and handling of so-called lexical and non-lexical knowledge proved to be an interesting candidate to help identify and clarify a number of these issues, and a number of examples are given. We report on research within STARLab’s DOGMA Project that indicates how such methods may or must be adapted to be usable in the context of ontologies, and how they may then help to define ontology updates, the role of domain constraints, and future tools that assist in e.g. the alignment of ontologies.
---
paper_title: A reverse engineering approach for migrating data-intensive web sites to the Semantic Web

paper_content:

The Semantic Web is intended to enable machine understandable web content and seems to be a solution for many drawbacks of the current Web. It is based on metadata that formally describe the semantics of Web contents. In this paper we present an integrated and semi-automatic approach for generating shared-understandable metadata for data-intensive Web applications. This approach is based on mapping a given relational schema into an already existing ontology structure using a reverse engineering process. As a case study we present this style of a schema- and data-migration for our Institute web portal. The presented approach can be applied to a broad range of today’s data-intensive Web sites.
---
paper_title: Reverse Engineering of Relational Databases to Ontologies

paper_content:

We propose a novel approach to reverse engineering of relational databases to ontologies. Our approach is based on the idea that semantics of a relational database can be inferred, without an explicit analysis of relational schema, tuples and user queries. Rather, these semantics can be extracted by analyzing HTML forms, which are the most popular interface to communicate with relational databases for data entry and display on the Web. The semantics are supplemented with the relational schema and user “head knowledge” to build an ontology. Our approach can be applied to migrating data-intensive Web pages, which are usually based on relational databases, to the ontologybased Semantic Web.
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paper_title: From Data Mining to Knowledge Discovery in Databases

paper_content:

The data that most organizations possess is considered a valuable asset. To really benefit from it, organizations must be able to analyze these data efficiently and effectively. This activity is nowadays referred to as business intelligence and one class of algorithms used within this activity are called data mining methods. There are some famous success stories about knowledge discovered via data mining but there is also a lot of disappointment so far. The paper argues that one of the reasons for some failures to produce better results with data mining is the reliance on transactional and master data only. It points to other types of data that can enrich transactional data and help in this way to produce more interesting and more rewarding data patterns.
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paper_title: Data Mining: An Overview

paper_content:

Data mining is emerging as one of the key features of many homeland security initiatives. Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. This report discusses the data mining uses (i.e. Terrorism Information Awareness (TIA) Program) and issues (i.e. data quality, interoperability, privacy), as well as the limitations of data mining.
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paper_title: IEPAD: information extraction based on pattern discovery

paper_content:

research in information extraction (IE) regards the generation of wrappers that can extract particular information from semi- structured Web documents. Similar to compiler generation, the extractor is actually a driver program, which is accompanied with the generated extraction rule. Previous work in this field aims to learn extraction rules from users' training example. In this paper, we propose IEPAD, a system that automatically discovers extraction rules from Web pages. The system can automatically identify record boundary by repeated pattern mining and multiple sequence alignment. The discovery of repeated patterns are realized through a data structure call PAT trees. Additionally, repeated patterns are further extended by pattern alignment to comprehend all record instances. This new track to IE involves no human effort and content-dependent heuristics. Experimental results show that the constructed extraction rules can achieve 97 percent extraction over fourteen popular search engines.
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paper_title: Extracting structured data from Web pages

paper_content:

Many web sites contain large sets of pages generated using a common template or layout. For example, Amazon lays out the author, title, comments, etc. in the same way in all its book pages. The values used to generate the pages (e.g., the author, title,...) typically come from a database. In this paper, we study the problem of automatically extracting the database values from such template-generated web pages without any learning examples or other similar human input. We formally define a template, and propose a model that describes how values are encoded into pages using a template. We present an algorithm that takes, as input, a set of template-generated pages, deduces the unknown template used to generate the pages, and extracts, as output, the values encoded in the pages. Experimental evaluation on a large number of real input page collections indicates that our algorithm correctly extracts data in most cases.
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paper_title: Towards an effective approach for reverse engineering

paper_content:

Currently, the demand for the reverse engineering has been growing significantly. The need of different business sectors to adapt their systems to Web or to use other technologies is stimulating the research for methods, tools and infrastructures that support the evolution of existing applications. We present the main research trends on reverse engineering, and discuss how should be an efficient reverse engineering approach, aiming at higher reuse levels.
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paper_title: An HTML-Form-Driven Approach to Reverse Engineering of Relational Databases to Ontologies

paper_content:

The migration of the Deep Web to the Semantic Web calls for reverse engineering of relational databases to ontologies. However, there exist few approaches that consider an ontology as the target for reverse engineering. The existing approaches are faced with two main problems. First, they can require more information about a relational database than it is possible to provide in practice. Second, they can make unrealistic assumptions about the relational database; e.g. the relational database is in 3NF. As an attempt to resolve these problems, we propose a novel approach. Rather than analyzing a relational schema, tuples, or user queries to build an ontology, our approach takes advantage of HTML forms - popular user-friendly interface to access relational databases on the Web today. By analyzing the forms, our approach can deliver benefits that could not otherwise be achieved with the existing approaches. These benefits are twofold. First, only minimal information about the relational database is required. Second, the relational database can be bad-designed, optimized and denormalized.
---
paper_title: A reverse engineering approach for migrating data-intensive web sites to the Semantic Web

paper_content:

The Semantic Web is intended to enable machine understandable web content and seems to be a solution for many drawbacks of the current Web. It is based on metadata that formally describe the semantics of Web contents. In this paper we present an integrated and semi-automatic approach for generating shared-understandable metadata for data-intensive Web applications. This approach is based on mapping a given relational schema into an already existing ontology structure using a reverse engineering process. As a case study we present this style of a schema- and data-migration for our Institute web portal. The presented approach can be applied to a broad range of today’s data-intensive Web sites.
---
paper_title: Reverse Engineering of Relational Databases to Ontologies

paper_content:

We propose a novel approach to reverse engineering of relational databases to ontologies. Our approach is based on the idea that semantics of a relational database can be inferred, without an explicit analysis of relational schema, tuples and user queries. Rather, these semantics can be extracted by analyzing HTML forms, which are the most popular interface to communicate with relational databases for data entry and display on the Web. The semantics are supplemented with the relational schema and user “head knowledge” to build an ontology. Our approach can be applied to migrating data-intensive Web pages, which are usually based on relational databases, to the ontologybased Semantic Web.
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paper_title: Learning ontology from relational database

paper_content:

Ontology provides a shared and reusable piece of knowledge about a specific domain, and has been applied in many fields, such as semantic Web, e-commerce and information retrieval, etc. However, building ontology by hand is a very hard and error-prone task. Learning ontology from existing resources is a good solution. Because relational database is widely used for storing data and OWL is the latest standard recommended by W3C, this paper proposes an approach of learning OWL ontology from data in relational database. Compared with existing methods, the approach can acquire ontology from relational database automatically by using a group of learning rules instead of using a middle model. In addition, it can obtain OWL ontology, including the classes, properties, properties characteristics, cardinality and instances, while none of existing methods can acquire all of them. The proposed learning rules have been proven to be correct by practice.
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paper_title: D2R MAP - A Database to RDF Mapping Language

paper_content:

The vision of the Semantic Web is to give data on the web a welldefined meaning by representing it in RDF and linking it to commonly accepted ontologies. Most formatted data today is stored in relational databases. To be able to use this data on the Semantic Web we need a flexible but easy to use mechanism to map relational data to RDF. The poster presents D2R MAP, a declarative language to describe mappings between relational database schemata and OWL ontologies.
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paper_title: R2O, an Extensible and Semantically based Database-to-Ontology Mapping Language

paper_content:

We present R2O, an extensible and declarative language to describe mappings between relational DB schemas and ontologies implemented in RDF(S) or OWL. R2O provides an extensible set of primitives with welldefined semantics. This language has been conceived expressive enough to cope with complex mapping cases arisen from situations of low similarity between the ontology and the DB models.
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Title: From Database to Semantic Web Ontology: An Overview
Section 1: Introduction and Motivation
Description 1: Write about the purpose and significance of transforming data from relational databases to Semantic Web ontologies, including the main challenges and advantages.

Section 2: Implicit Knowledge Embedded in Relational Databases
Description 2: Describe the types of knowledge that are embedded in relational databases which can be used for ontology development.

Section 3: The Semantic Web Ontology
Description 3: Define ontology in the context of the Semantic Web and the primary components of an ontology, with a focus on OWL constructs.

Section 4: Relational Database Schema
Description 4: Explain how relational database schema represents data structure and constraints, and how these elements can be mapped to ontology constructs.

Section 5: Database Instance
Description 5: Discuss how database instances provide concrete, timely data and how this information can be used to generate ontology instances and extract additional knowledge.

Section 6: Application Source Code
Description 6: Explain how application source code can be analyzed to verify and identify domain knowledge beyond what is represented in the database schema and instances.

Section 7: Techniques of Knowledge Acquisition from Relational Database
Description 7: Provide an overview of the various techniques used to extract knowledge from relational databases, including reverse engineering, mapping techniques, data mining, information extraction, and application reverse engineering.

Section 8: Challenges in Automated Database to Ontology
Description 8: Discuss the challenges of automating the knowledge acquisition process from relational databases and integrating this knowledge into Semantic Web ontologies.

Section 9: Classification of Previous Approaches on Database to Ontology
Description 9: Classify and compare different previous approaches to transforming database knowledge into ontologies, highlighting their methodologies and limitations.

Section 10: Conclusion and Future Work
Description 10: Conclude with a discussion on the importance of addressing the challenges mentioned and propose future directions for research and development in the field.