Development of an Ontology Based Forensic Search Mechanism: Proof of Concept

This paper examines the problems faced by Law Enforcement in searching large quantities of electronic evidence. It examines the use of ontologies as the basis for new forensic software filters and provides a proof of concept tool based on an ontological design. It demonstrates that efficient searching is produced through the use of such a design and points to further work that might be carried out to extend this concept

Extracting information from heterogeneous information sources using ontologically specified target views

Being deluged by exploding volumes of structured and unstructured data contained in databases, data warehouses, and the global Internet, people have an increasing need for critical information that is expertly extracted and integrated in personalized views. Allowing for the collective efforts of many data and knowledge workers, we offer in this paper a framework for addressing the issues involved. In our proposed framework we assume that a target view is specified ontologically and independently of any of the sources, and we model both the target and all the sources in the same modeling language. Then, for a given target and source we generate a target-to-source mapping, that has the necessary properties to enable us to load target facts from source facts. The mapping generator raises specific issues for a user’s consideration, but is endowed with defaults to allow it to run to completion with or without user input. The framework is based on a formal foundation, and we are able to prove that when a source has a valid interpretation, the generated mapping produces a valid interpretation for the part of the target loaded from the source.

Extracting information from heterogeneous information sources using ontologically specified target views

Abstract — Being deluged by exploding volumes of structured and unstructured data contained in databases, data warehouses, and the global Internet, people have an increasing need for critical information that is expertly extracted and integrated in personalized views. Allowing for the collective efforts of many data and knowledge workers, we offer in this paper a framework for addressing the issues involved. In our proposed framework we assume that a target view is specified ontologically and independently of any of the sources, and we model both the target and all the sources in the same modeling language. Then, for a given target and source we generate a target-to-source mapping, that has the necessary properties to enable us to load target facts from source facts. The mapping generator raises specific issues for a user’s consideration, but is endowed with defaults to allow it to run to completion with or without user input. The framework is based on a formal foundation, and we are able to prove that when a source has a valid interpretation, the generated mapping produces a valid interpretation for the part of the target loaded from the source. 1

UvA-DARE (Digital Academic Repository) Uncertain Data Integration Using Functional Dependencies

Abstract. Data integration systems are crucial for applications that need to provide a uniform interface to a set of autonomous and heterogeneous data sources. However, setting up a full data integration system for many application contexts, e.g. web and scientific data management, requires significant human effort which prevents it from being really scalable. In this paper, we propose IFD (Integration based on Functional Dependencies), a pay-as-you-go data integration system that allows integrating a given set of data sources, as well as incrementally integrating additional sources. IFD takes advantage of the background knowledge implied within functional dependencies for matching the source schemas. Our system is built on a probabilistic data model that allows capturing the uncertainty in data integration systems. Our performance evaluation results show significant performance gains of our approach in terms of recall and precision compared to the baseline approaches. They confirm the importance of functional dependencies and also the contribution of using a probabilistic data model in improving the quality of schema matching. The analytical study and experiments show that IFD scales well

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives