Researcher Profiling based on Semantic Analysis in Social Networks

Last week I defended my work in front of the jury of the KULeuven. This included a presentation and a demo. I want to share those with you. You can read the full thesis text here: Researcher Profiling based on Semantic Analysis in Social Networks

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Final Thesis text almost finished…

Only the abstract remains to be written, as an inspiration the 30 most frequent words of my work could probably serve as a basis. Would they be great tags to give a reliable representation of my work were tag representations play a very important role? We’ll see… the irony!

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Explore your Social Circle, improve your Research

If you have a Twitter account and want to register in Grabeeter (or already have), you can now try out and take part in the evaluation of the “Researcher Affinity Browser“. Grabeeter is a tool to archive and search your tweets. The Researcher Affinity Browser is one of the first to expose affinities between Twitter users and is the first web application built on top of a semantic profiling framework. It is intended for researchers who are using Twitter for microblogging and are tweeting about their research, their interests or the conferences they are attending or tracking.

Researcher Affinity Browser

Researcher Affinity Browser

The semantic profiling framework grew throughout this year as my thesis project and information about the evolutions is presented here on this blog.

So if you are a researcher and are using Twitter, you are most welcome to explore people who have already registered themselves in Grabeeter. You can register from inside the application or here. After registration you have to wait a few hours before your data is analysed and suggestions can be made for you. Once you are registered you can start exploring people by selecting your username.
Important Notes

1. You can evaluate the application by filling out the form that can be accessed by clicking on the “Evaluate”-button inside the application or by using this form.

2. You can also evaluate the application with a default user if you feel uncomfortable or don’t want to wait for your data to be analyzed. Just click the Load Persons button without selecting a user in the list. The default user is selected because it has the most conferences.

Progress Report 4

This report is a presentation. I presented it today for members of the research group and fellow thesis students at the KULeuven.

Scientific Profiling Presentation

It explains the evaluation approach for the framework. The term “affinity” is being introduced for the first time. Before terms like shared resources or common entitities and interests were used.

“Affinities” expresses much better the user-centric perspective and the fact that it is a subjective notion. It also means that it is not only linked to a certain person but is also time-sensitive, something that could be called a “user context”.


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Constructing Experts Profiles from Linked Open Data

Inspired from Linked Open Data (LOD) initiative, Latif et al. have developed a tool which can establish links between authors of digital journals with relevant semantic resources available in LOD [1]. The proposed system is able to disambiguate authors and can: 1) locate, 2) retrieve, and 3) structure the relevant semantic resources. Furthermore, the system constructs comprehensive aspect oriented authors’ profiles from heterogeneous datasets of LOD on the fly. They investigated the potentials of such an approach on a digital journal known as Journal of Universal Computer Science (J.UCS). It is their strong belief that this kind of applications can motivate researchers and developers to investigate different application areas where Linked Open Data can contribute, bring added value, and can take the idea of open access further.

Because of the strong resemblance to our application, some interesting aspects of their approach will be used in this project. Read more of this post

Open Innovation Problem Solver Search

This project presents a case in which technical scientists are linked to each other. The intention is to connect scientists that have similar interests. In “Open Innovation” experts of different institutions and companies try to collaborate and increase the rate of technological innovation. M. Stankovic wrote a paper [1] to check if linked data contributes in these efforts. Read more of this post

Can linked data assist in expert profiling?

Scientific profiling in social networks involves the determination of a canditate’s (user) generated content. To determine if this content (in this case the microblogs) have scientific relevance, thus if a twitter user is an expert in a certain domain, we link hashtags to the linked data cloud. Specifically we try to discover scientific conferences, locations, people and events. In the literature we found an important validation for this idea. The general conclusion is that there are sources available to build such a system. But they are not properly interlinked. This thesis project is an effort to provide the interlinking between several LOD sources (most importantly Colinda, GeoNames and DBPedia). Other resources can definitely enhance the possibilities of the framework. But to prove the case we strictly limit the effort to technical scientific people and we use the hypothesis that if people are attending similar scientific conferences they are a good match.

Stankovic et al. studied expert search and profiling systems. Such systems aim to identify candidate experts and rank them with respect to their estimated expertise on a given topic, using available evidence. The authors found that traditional expert search and profiling systems exploit structured data from closed systems (e.g. email program) or unstructured data from open systems (e.g. the Web). However, on today’s Web, there is a growing number of data sets published according to the Linked Data principals, the majority of them being part of the Linked Open Data (LOD) cloud. As LOD connects data and people across different platforms in a meaningful way, one can assume that expert search and profiling systems would benefit from harnessing LOD. Read more of this post