Protecting children from sex offenders online: when strangers become 'virtual friends'

This article describes findings from research funded by the Metropolitan Police and Crimestoppers which aimed to explore children's online experiences. A non-random, stratified sample of 200 London school children aged 10- 13 participated in focus groups. Preliminary findings are also presented from unpublished ongoing PhD research, which seeks to explore sex offender behaviour online and the policing of the internet (Martellozzo, 2005 ongoing). The findings are discussed in the context of sex offender's use of the internet. This research indicates that children do have some basic knowledge about 'stranger danger' but are not necessarily applying these lessons to cyberspace. The children in this study had sufficient awareness to not give personal details to strangers on the internet, and would not arrange to meet them. However, they made a distinction between 'strangers' and 'virtual friends' and this is an important point. Preliminary findings also highlight the difficulty of policing the internet and serve to illustrate the manner in which the Sexual Offences Act 2003 is applied to internet sexual offending in practice.

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.