After having introduced the motivation and context, the remainder of this paper is divided into the following sections. Section 2 introduces ontologies and domain knowledge representations. Section 3 reviews the state of the art in ontology-based database information retrieval. Section 4 discusses our findings in relation to ontology-based information retrieval. Section 5 reviews the state of the art database schema to ontology schema transformation and ontology-to-database mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. Section 6 provides a discussion and highlights a possibility of combining database-to-ontology transformation and ontology-to-database mappings approaches for relational query formulation. Finally, Section 7 outlines the future challenges and possible research directions towards using ontologies for information retrieval from information and Big data management systems. (p. 2)

Munir, K., Anjum M.S (2017, August 7). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics . Retrieved November 12, 2017 from https://doi.org/10.1016/j.aci.2017.07.003Get rights and content

Compare and Contrast

A comparison between RDF(s), OWL-1 and OWL-2 showing the possible uses of knowledge representation concepts to formulate ontology based relational database queries is presented in Table 1. In summary, both OWL and RDF have many common features, but OWL is a stronger language with greater machine interpretability than RDF. Moreover, OWL comes with a larger vocabulary and a stronger syntax than RDF, which can be used to define complex ontology concept restrictions and subsequently to formulate ontology based relational database queries. (p. 3)

Munir, K., Anjum M.S (2017, August 7). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics . Retrieved November 12, 2017 from https://doi.org/10.1016/j.aci.2017.07.003Get rights and content

Definition or Description

Ontology-based Visual or Interactive query formulation systems are query systems for databases that use visual representations to express related data requests. These systems adapt ontologies for database query generation in order to improve the effectiveness of the human-computer communication. In recent years, many such systems have been reported in the literature (e.g., TAMBIS [20], GRQL [21], SEWASIE [22], Ontogator [23], OntoViews [24], OntoQF [25], VISAGE [26], Smartch [27], Semantic-based [28] and many others). In most of these ontology based visual query formulation systems, the search queries are performed using an ontology browser that visualises the ontology as a tree. The actual search is done via concept selection through a visual tree or through keywords annotated by the visual ontology concepts. (p.5)

Munir, K., Anjum M.S (2017, August 7). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics . Retrieved November 12, 2017 from https://doi.org/10.1016/j.aci.2017.07.003Get rights and content

Cause and Effect

Database information retrieval is the search for information in databases. The need for effective methods to automate information retrieval has grown in importance because of the significant increase in the amount of both structured and unstructured information embodied in information sources. Over the years, many visual information retrieval approaches came into existence, which aim to reduce the end users effort while interacting with databases. These approaches intend to extract information from databases using visual tools. Such approaches include form-based [17], query by example (QBE) [18] or query by template (QBT) [19] etc. These approaches work for basic relational database queries, primarily because tabular structure of the database fits well with the tabular skeletons used in query interfaces. (p. 4)

Munir, K., Anjum M.S (2017, August 7). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics . Retrieved November 12, 2017 from https://doi.org/10.1016/j.aci.2017.07.003Get rights and content