Conjunctive Query Answering for the Description Logic SHIQ

Ontologies and Data Management: A Brief Survey

Information systems have to deal with an increasing amount of data that is heterogeneous, unstructured, or incomplete. In order to align and complete data, systems may rely on taxonomies and background knowledge that are provided in the form of an ontology. This survey gives an overview of research work on the use of ontologies for accessing incomplete and/or heterogeneous data.

A DNA-based semantic fusion model for remote sensing data

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

Expressive OWL Queries

Web Ontology Language ontologies become more and more popular in complex domain modeling for their high expressiveness, flexibility and well defined semantics. Although query languages adequate in expressiveness to OWL reasoning capabilities were introduced before, their implementations are rather limited. In this paper, the authors study SPARQL-DLNOT, an extension of one of these query languages, SPARQL-DL, and present novel evaluation and optimization techniques for efficient SPARQL-DLNOT execution. As queries become complex easily, they also present a novel graph-based visualization that simplifies query construction and maintenance. Presented techniques and algorithms were implemented in the Pellet reasoner and in their novel Protégé plug-in OWL2Query.