Page 199 - DCAP312_WEB_TECHNOLOGIES_II

Unit 9: The Database Model

Notes
views component. Besides the mapping between relational DB and ontology, it is critical to
construct an appropriate RDF representation (ontology) for relational data.

The challenge in using domain knowledge in ontologies effectively is in crafting “integrating”
ontologies that tie the domain knowledge in ontologies with other ontologies that may be
used to model the relational data in the database. For example, in figure a crucial piece in
answering the query is the ontology in top right, which ties the data in the relational DB
with the domain ontologies describing regions and businesses. We would need to come up
with guidelines and best practices for developing these ontologies IBM’s Semantic Web Tools
and Systems Here, we briefly introduce some IBM’s ontology tools and systems related to
RDF Access to relational data. IODT is a toolkit for ontology-driven development, including
EMF Ontology Definition Metamodel (EODM) and an OWL Ontology Repository (named
SOR). EODM is derived from the OMG's Ontology Definition Metamodel (ODM) and
implemented in Eclipse Modelling Framework (EMF). It is the run-time library that allows
the application to put in and put out RDFS/OWL ontology in RDF/XML format; manipulate
an ontology using Java objects; call an inference engine and access inference results; and
transform between ontology and other models. SOR is an OWL ontology storage and query
system on the relational DBMS. It supports Description Logic Program (DLP), a subset of
OWL DL, and SPARQL query language. SHER reasoned uses a novel method that allows
for efficient querying of SHIN ontologies with large ABoxes stored in databases. Currently,
this method focuses on instance retrieval that queries all individuals of a given class in the
ABox. It is well known that all queries over DL ontologies can be reduced to consistency
check, which is usually checked by a tableau algorithm. SHER groups individuals which are
instances of the same class into a single individual to generate a summary ABox of a small
size. Then, consistency check can be done on the dramatically simplified summary ABox,
instead of the original ABox. It is reported in that SHER can process ABox queries with up
to 7.4 million assertions efficiently, whereas the state of art reasoners could not scale to this
size. As described in the example shown in figure to enable semantic queries over existing
data sources, we need to store and leverage ontologies representing domain knowledge. SOR
could be used to manage such ontologies. Similarly, in the CDI case, we need an ontology
repository to cache and materialize some inference results for performance improvement.
In general, an RDF store, such as SOR, could be used to store domain knowledge or part of
reasoning results for RDF access to relational databases. Obviously, SHER engine could be
used for scalable ontological reasoning for SPARQL queries over relational databases. The
system described in takes an ETL (Extract-Transform-Load) approach, where the relational
data in the database is extracted, transformed into RDF triples based on a set of domain
ontologies and mapping rules, and loaded into SOR. This system also provides mechanisms
to handle updates to the relational database as well as to the ontologies.
Question

1. Explain Universal Resources Identifiers (URIs).
2. What is Customer Data Integration (CDI)?
Self Assessment Questions

6. All authorized users in an organization can share the ……………. stored in its database.
(a) information (b) method
(c) processor (d) None of these

7. The ………… of data elements in a database are there for a specific purpose.
(a) single (b) only related
(c) collection (d) None of these

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