Superfast Pathfinding in DBpedia

Superfast Pathfinding in DBpedia

Everything is Connected is able to find a path between a Facebook user and any concept known on DBpedia, such as, persons, locations, things, etc. The found path is presented to the end-user as a short story, which explains the relation between the searched concept and the end-user.

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Presenting Linked Data as (Learning) Objects to increase Research Efficiency

ICT can increase the efficiency of research by embedding and coordinating the search for information among a group of colleagues and other researchers in a discipline. This leads to an enormous time saving and acceleration of the research process. ICT has the potential to expose new possible research tracks and interesting data via channels which the researcher should not be familiar with. One type of promising ICT environments uses Learning objects for this purpose. Read more of this post

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

User Analysis possibilities: first simple Demo!

I implemented a first very basic demonstration to show the possibilities for analysis of the annotated tweet data. This first demo makes no use of any domain knowledge or linked data. It is just uses the annotated user data and associated tags.

It matches two users based on similar hashtags. Of course this can be done on many ways. But the semantic profiling framework (in its current state) made an implementation for the logic possible in just half an hour with 3 lines of code on top of the framework.

The next and final improvement for this simple demo will be to identify scientific conferences in the list of common tags.

Try it yourself

If you are not in the database yet, you can do it by using this link:

http://linkeddata.semanticprofiling.net/interlinking/provider.php?user=your_name

http://linkeddata.semanticprofiling.net/test/usermatch_demo.php?q=laurens_d_v&q2=selvers

First analysis demo

First analysis demo


Extraction basically complete

 

Notation3 Logo

Notation3 Logo

All users who registered before this week on Grabeeter can get a N3/Turtle version of their profile and tweets on the following url:
http://linkeddata.semanticprofiling.net/extraction/twitter_user_n3.php?user=…

 


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