This paper presents an approach for the disambiguation of individuals using the semantics of identity and social circles. Identity information is extracted and integrated to provide a presentation of existing identity information currently on the web relating to a given individual. Communities are used to discover identity resources, share them socially, and critique the resources based on the accuracy and volume of their content. As motivation for this research issues concerning identity theft, online fraud and cyber stalking are considered, where the growth of the social web has contributed to the rise in such practices. Monitoring identity on the web would go some way to addressing these issues.
The motivations behind my research have been the growth of the social web over the
past 2 years, and the rise in online identity theft and cyber stalking [
The semantics of identity are used to perform the extraction process by recognising identity features within a web resource and extracting the content relating to these features. Social networks are mined and pruned to derive the social circle that an individual belongs to using social content such as socially tagged images and conversation data. The social circle is then used to recognise identity information on the web, by parsing text content from web pages to derive the names of individuals, and comparing them against the social circle.
Disambiguating between information describing different individuals using features of identity and the pruned social circles is used to aid with the integration of identity information. Social circles are used to provide a useful technique to disambiguate individuals by their acquaintances. Resource discovery is supported using a community of users by sharing resources containing identity information; the community is responsible for rating, and critiquing the resources, and discovering more identity resources upon which they are shared with the community.
Semantic technologies provide useful techniques and methods to identify individuals. An individual’s identity will be formalised to encapsulate uniquely identifiable properties, reasoning is performed to derive additional information relating to the individual. The extraction of information will be carried out using a populated ontology containing personal details belonging to an individual. Identity information from various resources will be integrated together, and disambiguated according to their semantics. Involving a community to aid with extraction, by sharing vital resources, will also use social technologies. Social feedback methods will also be used for feature selection by allowing an individual to select the properties of their identity they believe to be the most prevalent.
The work presented within this abstract employs a both a combination of existing methodologies such as social networking mining, and original techniques for disambiguation of individuals. The motivation of this work places emphasis on monitoring online information, and providing risk assessments to concerned users, those who wish to discover what information exists relating to them. This extended abstract is structured as follows: Section 2 discusses the state of the art divided into the three previously mentioned areas. Section 3 is similarly divided into three areas outlining the work plan by explaining the various investigations being conducted. Section 4 explains the evaluation methods to be used, and section 5 presents concluding remarks.
The state of art on information extraction distributed throughout various textual
sources includes standard information extraction mechanisms and approaches that can
be applied to identity data such as classic wrapper induction [
The state of the art within the area of social network mining commonly uses
techniques such as entity co-occurrence [
Social networks from two separate sources are integrated together in [
Work in [
Regarding the discovery of identity information I have focussed on the semantics of identity. I am currently defining a manually created ontology encapsulating the properties of identity, and able to capture an individual’s identity properties. Future work includes the designing of a methodology to efficiently discover identity information from the wider web. In order to extract identity information I have investigated the use of support vector models for community supported identity extraction from semi-structured web resources. Following work will investigate focussed crawling using specialised web queries, indexing a subset of the web, and community supported blocking mechanisms.
To mine social networks I have created several mechanisms to extract social network data from social networking sites that will be used to seed sets for a wider mining process. A working prototype of this approach is available for use1. The next stage of work will investigate the pruning of social network data to derive social cliques and circles, and the investigation of the effects and application of identity discovery through the use of social cliques and clusters.
The work I have done to date has investigated the integration of object data from heterogeneous web resources, and the disambiguation of objects. The disambiguation of objects can then be applied to identity information. Further work will investigate 1 http://apps.facebook.com/socialcircular the use of pairwise decision models, and community supervision of integration where I anticipate that the use of feature selection using decision models will be an important aspect of disambiguating identity information.
To date I have researched approaches to share resources through social bookmarking tools and similar web applications. Future work will investigate the adoption of collective intelligence approaches when sharing identity resources, and the use of feedback mechanisms to rate and prioritise identity resources.
Identity extraction will be evaluated for precision, recall and error rate. Evaluators of the approach will be required to find all occurrences of their identity manually to create a gold standard, detailing what identity details are present. Extracted identity information will then be evaluated against the gold standard. Precision and error rate will be used to evaluate extracted social networks through comparison against real life social networks for each individual performing the evaluation. Evaluators will also be required to validate relationship strengths within their social circle, and the members of their social circle.
Evaluation will be performed using exhaustive user testing to derive the precision, recall and error rate. Each individual will verify the all information items relating or not relating to them, and the integrated information to identify incorrectly integrated information and incorrectly excluded data.
The evaluation of the sharing mechanism will be performed using social studies of users when using the approach, testing for user satisfaction through questionnaires. The approach should provide a useful means for sharing identity resources through an easy to use, yet effective methodology.
This paper presents an overview of the research that I am currently conducting. The research when broken down into the three areas can be summarised further to include information extraction, information integration and sharing mechanisms. The first areas being largely concerned with existing semantic web technologies and their adaptation to these areas of work. The third area is largely centred around the social web, and current sharing mechanisms being employed by social web sites and services. A combination of both semantic web and social web technologies would incorporate the user at a more intrinsic level by supervising the information extraction and integration stages.
The state of the art will be contributed to mainly in the area of social network mining, and the use of the derived social circles to disambiguate individuals. The work that I have carried out so far has been largely concerning research within each separate area of work, and I have reached a position to begin implementation of an approach to disambiguate individuals using social circles. Information retrieval metrics have been chosen to evaluate the extraction and integration of information because of their widespread usage in similar applications. Evaluating for user satisfaction was selected when evaluating the discovery of resources in order analyse the effectiveness of the sharing mechanism.
In relation to the addressing of issues such as identity theft, online fraud, and cyber stalking, the presented approach provides a methodology to monitor the occurrence of identity information, and using semantic technologies reasoning can be performed to asses the risk of an individual being a victim of such practices. The approach must have sufficient flexibility to allow assessments to be made based on alternative requirements, such as different identity features. Disambiguating identity information performs a crucial role when assessing the risk of identity theft, any information that is wrongly classified could contribute to providing a false analysis.