Using Ontologies for Semantic Data Integration

While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed

Qualitative Effects of Knowledge Rules in Probabilistic Data Integration

One of the problems in data integration is data overlap: the fact that different data sources have data on the same real world entities. Much development time in data integration projects is devoted to entity resolution. Often advanced similarity measurement techniques are used to remove semantic duplicates from the integration result or solve other semantic conflicts, but it proofs impossible to get rid of all semantic problems in data integration. An often-used rule of thumb states that about 90% of the development effort is devoted to solving the remaining 10% hard cases. In an attempt to significantly decrease human effort at data integration time, we have proposed an approach that stores any remaining semantic uncertainty and conflicts in a probabilistic database enabling it to already be meaningfully used. The main development effort in our approach is devoted to defining and tuning knowledge rules and thresholds. Rules and thresholds directly impact the size and quality of the integration result. We measure integration quality indirectly by measuring the quality of answers to queries on the integrated data set in an information retrieval-like way. The main contribution of this report is an experimental investigation of the effects and sensitivity of rule definition and threshold tuning on the integration quality. This proves that our approach indeed reduces development effort — and not merely shifts the effort to rule definition and threshold tuning — by showing that setting rough safe thresholds and defining only a few rules suffices to produce a ‘good enough’ integration that can be meaningfully used

Semantic Integration Portal

The Semantic Integration Portal is a demonstration of the potential capabilities of Semantic Web applications in a knowledge-rich context. Source data is taken from different online terrorist incident aggregators and marked up according to ontologies specific to those domains. Unlike other semantic web techniques, which scrape the internet for raw data and then mark-up against a standard ontology, the approach here is to allow each data source to have its own domain-specific ontology. This allows the data producers the opportunity to mark up their data in their own way, producing RDF data according to their own ontologies without the need to conform to a standard. A variety of semantic integration techniques can then be applied to these ontologies, both automatic and interactive, allowing data from both sets to be viewed in a suitable application, in this case the mspace browser. Future iterations of the semantic integration portal aim to introduce more automated ontology-mapping techniques, aligning data from a variety of diverse sources with less need for human intervention

Semantic Web Techniques to Support Interoperability in Distributed Networked Environments

We explore two Semantic Web techniques arising from ITA research into semantic alignment and interoperability in distributed networks. The first is POAF (Portable Ontology Aligned Fragments) which addresses issues relating to the portability and usage of ontology alignments. POAF uses an ontology fragmentation strategy to achieve portability, and enables subsequent usage through a form of automated ontology modularization. The second technique, SWEDER (Semantic Wrapping of Existing Data sources with Embedded Rules), is grounded in the creation of lightweight ontologies to semantically wrap existing data sources, to facilitate rapid semantic integration through representational homogeneity. The semantic integration is achieved through the creation of context ontologies which define the integrations and provide a portable definition of the integration rules in the form of embedded SPARQL construct clauses. These two Semantic Web techniques address important practical issues relevant to the potential future adoption of ontologies in distributed network environments

A universal ontology-based approach to data integration

One of the main problems in building data integration systems is that of semantic integration. It has been acknowledged that the problem would not exist if all systems were developed using the same global schema, but so far, such global schema has been considered unfeasible in practice. However, in our previous work, we have argued that given the current state-of-the-art, a global schema may be feasible now, and we have put forward a vision of a Universal Ontology (UO) that may be desirable, feasible, and viable. One of the reasons why the UO may be desirable is that it might solve the semantic integration problem. The objective of this paper is to show that indeed the UO could solve, or at least greatly alleviate, the semantic integration problem. We do so by presenting an approach to semantic integration based on the UO that requires much less effort than other approaches.Peer ReviewedPostprint (published version

A Shared Ontology Approach to Semantic Representation of BIM Data

Architecture, engineering, construction and facility management (AEC-FM) projects involve a large number of participants that must exchange information and combine their knowledge for successful completion of a project. Currently, most of the AEC-FM domains store their information about a project in text documents or use XML, relational, or object-oriented formats that make information integration difficult. The AEC-FM industry is not taking advantage of the full potential of the Semantic Web for streamlining sharing, connecting, and combining information from different domains. The Semantic Web is designed to solve the information integration problem by creating a web of structured and connected data that can be processed by machines. It allows combining information from different sources with different underlying schemas distributed over the Internet. In the Semantic Web, all data instances and data schema are stored in a graph data store, which makes it easy to merge data from different sources. This paper presents a shared ontology approach to semantic representation of building information. The semantic representation of building information facilitates finding and integrating building information distributed in several knowledge bases. A case study demonstrates the development of a semantic based building design knowledge base

A Data-Intensive Lightweight Semantic Wrapper Approach to Aid Information Integration

We argue for the flexible use of lightweight ontologies to aid information integration. Our proposed approach is grounded on the availability and exploitation of existing data sources in a networked environment such as the world wide web (instance data as it is commonly known in the description logic and ontology community). We have devised a mechanism using Semantic Web technologies that wraps each existing data source with semantic information, and we refer to this technique as SWEDER (Semantic Wrapping of Existing Data Sources with Embedded Rules). This technique provides representational homogeneity and a firm basis for information integration amongst these semantically enabled data sources. This technique also directly supports information integration though the use of context ontologies to align two or more semantically wrapped data sources and capture the rules that define these integrations. We have tested this proposed approach using a simple implementation in the domain of organisational and communication data and we speculate on the future directions for this lightweight approach to semantic enablement and contextual alignment of existing network-available data sources