Multidimensional Integrated Ontologies: A Framework for Designing Semantic Data Warehouses

Abstract

The Semantic Web enables companies and organizations to gather huge amounts of valuable semantically annotated data concerning their subjects of interest. Nowadays, many applications attach metadata and semantic annotations taken from domain and application ontologies to the information they generate. From our point of view, the concepts in these ontologies could describe the facts, dimensions, categories and values implied in the analysis subjects of a data warehouse. In this paper we propose the Semantic Data Warehouse to be a repository of ontologies and semantically annotated data resources. We also propose an ontology-driven framework to design multidimensional analysis models for Semantic Data Warehouses. This framework provides means for building an integrated ontology, called the Multidimensional Integrated Ontology (MIO), including the classes, relationships and instances that represent interesting analysis dimensions and measures. The reasoning capabilities of a MIO can be used to check the properties required by current multidimensional databases (e.g., dimension orthogonality, category satisfiability, etc.). In this paper we also sketch how the instance data of a MIO can be translated into OLAP cubes for analysis purposes. Finally, some implementation issues of the overall framework are discussed.

Designing multidimensional cubes from warehoused data and linked open data

“…2 the qb:dataset named Climate Changes includes two instances of qb:observation identified by eg:ob1 and 2 http://www.w3.org/TR/vocab-data-cube 3 Details about the prefixes are available on http://prefix.cc/ eg:ob2 (cf. lines [15][16][17][18][19][20][21][22][23][24]. Each instance of qb:observation relates an instance of eg:M_TEMPERATURE with an instance of associated dimensions, namely eg:GEOGRAPHY and eg:TIME.…”

“…operational databases) into the warehouse format. With the arrival of web published data in business analyses, the DW community intuitively treated LOD as external data sources that should be centralized in a DW through an ETL process [16], [17]. The shortcoming of this approach was found soon afterwards due to the poor freshness of warehoused LOD and the high cost of non-automatic ETL process [18].…”

Building data warehouses with semantic web data

“…By combining IE techniques with logical reasoning, in [18] they propose a MD model specially devised to select, group and aggregate the instances of an ontology. In our previous work [31] we define the semantic data warehouse as a new semi-structured repository consisting of semantic annotations along with their associated set of ontologies. Moreover, we introduce the multidimensional integrated ontology (MIO) as a method for designing, validating and building OLAP-based cubes for analyzing the stored annotations.…”

DC Proposal: Online Analytical Processing of Statistical Linked Data

“…There is recent work on creating data warehouses using general ontologies [15,7]; however, they do not deal with the problem of integrating datasets described by heterogeneous ontologies. Nebot et al [6] do so, however, they limit their work to static datasources, which is not realistic with Linked Data. Also, they require the user to manually control the building of a conceptual model; our work focuses on automatically retrieving a valid conceptual model from SLD.…”