The initiative exposes implicit invisible relations and allows people to connect based on entities identified as affinities that they share. Such entities are common real world things and can thus represent locations, people, events, document-snippets, charts, images etc.

Members can ask expertise analysis on excerpts of their personal reading library, social networks, search queries or browsing history and a use it as an intelligent collaboration tool.

The main hooks and hubs for the initiative operations are the people who contribute ideas. It puts the relations between the people central. You can see it as a huge peer-2-peer review network or a free open university where everybody is student, teacher, researcher and policy maker at the same time.

Support this new initiative by filling out their “Idea-to-Implementation tool” Questionnaire.

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A growing number of Web 2.0 Services such as Mendeley, Bibsonomy and citeulike are disclosing information from a huge amount of resources. However it is still a troubling task for users to check all those services one by one as they each provide access to a (small) part of the entire available information ‘cloud’…

The increasing availability and Open Data and Online Social Media create an opportunity to align the information made available by those Web 2.0 services. Open Data forms a backbone to interlink public and proprietary resources and social media allow the realization of collaborative filtering. The community drives ontologies, annotations and feedback that describe online available resources. You can think for example about DBPedia, Twitter Mentions/Retweets and Facebook Suggestions/Likes. The fact that Facebook has recently released its social graph that not only exposes relations (noun to noun) between people, but also describes the relations (with their so-called verbs) is emphasizing the importance of a networked backbone. Facebook uses this graph to connect people’s timeline and tries to interlink it with various paid advertisements that may or may not have got positive response from friends. The graph also feeds various apps that can be used to carry out advanced social analysis algorithms for marketing purposes.

To achieve a backdoor to what you are supposed to know as instructed or bounded by your research an information channels, there is need for an invisible engine that alligns above mentioned loose components. To make it work, you have to share. You share your CV, interesting resources, ideas you want feedback for, photos or videos. So basically you leave a trace of your intellectual property because you believe that it can be used as a seed to gain access to other interesting people or resources that lift you up to the next level. Because you are free to share, you are unbounded to any research group, but you can still work together. No need for a research institute. It is a backdoor, a way around the strictly regulated knowledge highway, to a better future, open and community driven. [2][4]

Such a ‘backdoor’ engine, contains important risks. First and foremost the reliability of the technology and the dependency on the community are aspects where you lack control. In a research center or knowledge institute it is common to sign a contract and follow a set of rules, but within those boundaries everything is safe and trusted. [3]

References

1. Bosc, H. and S. Harnad (2004). “In a paperless world a new role for academic libraries: Providing open access“. Spreading the Word: Who Profits from Scientific Publications. A Symposium within EOSF2004, Stockholm.
2. Borgman, C. (2003). “The invisible library: Paradox of the global information infrastructure.” Library Trends Vol. 51 No. 4: pp. 652-674
3. Stuart Basefsky. The End of Institutional Repositories & the Beginning of Social Academic Research Service: An Enhanced Role For Libraries. Published on June 16, 2009.
4. Clements, M. , 2010. Personalized access to social media. Thesis, (PhD). Technische Universiteit Delft.

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Laurens De Vocht, Selver Softic, Martin Ebner, and Herbert Mühlburger. 2011. Semantically driven social data aggregation interfaces for Research 2.0. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies (i-KNOW ’11), Stefanie Lindstaedt and Michael Granitzer (Eds.). ACM, New York, NY, USA, , Article 43 , 9 pages. DOI=10.1145/2024288.2024339 http://doi.acm.org/10.1145/2024288.2024339

Image via Wikipedia

Social media play an ever increasing role in the kind of information we get to see and especially how (fast). More than ever, any individual gets the opportunity to consume (after paying a fee) scientifific articles that they desire. Besides that it’s possible via news groups, forums, faq-sites, blogs and social networks to contribute ideas as a new article or directly as a quick tought directly to followers or subscribers. People no longer publish only in their free time or as a hobby. Professionals, experts, researchers and educators are increasingly using social media. Defining an expert or shaping a research project evolves enormously under influence of new technology as intelligent recommendation systems  who massively starting to adopt social media in their algorithms.

Every expert has developed his own personal way practicing and performing. Social media offer a way for experts to differentiate them even more. Rather than following one influencive professor or teacher in a niche group, they have the chance to quickly discover different methods to improve very specific aspect of their research and methodology. Intelligent recommendation systems will behave as the guide for every expert and researcher along their path, just as the old-school teacher did before the arrival of complex information systems and social media. Every consumer and producer of information learns to quickly pick-up the right puzzlepieces and to process them to a deliverable (such as a report, article, analysis, synhthesis…). Properly implemented information systems who incorporate feedback of their users can improve their service even faster as intelligent filtering algorithms are more and more focussed on collaborative input.

It  is important for any expert or researcher to keep practicing their information processing skills. It is a cumulative effect, the so-called “Power-Law“. Experiments have shown that there is a neuroplasticity effect on the brain by the environment. A high-tech environment with lots of valuable inputs thus should have a more effective impact than an environment in which expert has to dismiss to much stuff instead of being able to focus on the right piece of information at the right time. The fast technology shift is so fast that basic reading, analysis and synthesis for reading and selecting texts are a good base, but not enough to be able to keep track of the ever increasing offer on information. Instead of really having to specialize in one certain area and having to read, let’s say all the material in that area, the task of an expert researcher now is more and more in having their ways of dealing with the diversity of methods to access the right piece of information and judging whether it’s a suitable piece or not. This kind of expertis is very time and location specific. Intelligent recommendation systems allow experts and researcher to define and keep track of the pieces which they have the most affinity with at a certain point in time from an immense pool of pieces. They are no longer limited to the resources in their local and often niche-environment. It is therefore less and less opportune for companies or institutions to invest in the education of very specific experts as it’s very unlikely that they will stay long enough to achieve that goal. Experts will temporarely gain expertise for a specific research project driven by powerful affinity and supported by an immense community of people interested in the same topic. The task of the experts is to bring to pieces together and achieve their research goals, same as before the technology and social media evolution, but much faster. Think of it as playing chess where at every move you can ask feedback from the community, instead of having to think of the best solution you have a set of possible solutions and you have to learn and judge which suggested move give you most chance to success.

So expert training to develop the necessary plasticity still has an important role, but the focus is different. It’s far more important to quickly judge the value of suggested or found pieces, would it be by a search engine, recommendation system or another expert. This requires an even higher pragmatic skill of every expert than ever before. The better you are able to formulate your problem for the community, the better results you can achieve. Depending on your level or estimated difficulty of the problem, intelligent recommendation systems can help researchers with formulating and being aware of the research goal at a certain moment. An amateur musician who has a great affinity with a certain piece of music can still have its own very important and more valuable interpretation to some other listener than a professional musician. It is only hard for the listener to find that one amateur (of thousands) that has the most amazing interpretation and performance (for him) compared to the ease of finding the professional performance.

The arrival of social media and intelligent recommendation systems has made it possible that both young and experienced researchers can focus more on specific aspects they have great affinity with, both as consumers (listeners) or producers (performers). They all can deliver a valuable expertise to someone else. The new technology brings all those people closer together than ever before. The possibility to follow our affinities as they evolve through our lives is very important as it keeps researchers motivated and thus the quality of produced content increases, which helps to ignite interested readers and so on. The exchange of experiences and possibility to improve skills is guaranteed by the giant network of information: Internet. The Internet is no longer a huge static source of information, like a digital library. The Internet is a way to be able to focus more on the own temporary interests more intensively by combining puzzlepieces from various sources to new information that can be reused by others. Intelligent recommendation systems make this information more accessible and bring researchers and experts in touch with each other.

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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.

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

The emergence of many semantically rich and structured datasets from Linked Open Data movement (LOD) can facilitate in more controlled search and fruitful results. Latif et al. employed an automatic technique to find the required information about experts using LOD dataset. The expert profile is discovered, aggregated, clustered, structured, and visualized to the administration of peer-review system.

Latif et al. designed a system divided in to three layers which interact with each other to make the system operational: Expertise Calculation, Visualization, Locating and Construction of Expert Profile. In their paper [2], they proposed and implemented an automatic technique for discovering this information. The system uses Linked Data paradigm for acquiring semantically rich information. The proposed set of heuristics was able to disambiguate experts, in acquiring relevant information, and structuring the information to produce a coherent view of the expert.

Their system has been implemented for a journal such as Journal of Universal Computer Science (J.UCS). The proposed system is useful for the J.UCS administration to assign reviewing duties by presenting a comprehensive expert profile. The proposed system is useful for the J.UCS administration to assign reviewing duties by presenting a comprehensive expert profile.

The linking as proposed by the authors is helpful for different scenarios e.g.: for users who are searching research collaborators, for journal administration who want to assign new reviewers and for users who want to explore experts to seek guidance. A comprehensive profile of an author was structured and visualized at one place providing various opportunities for collaborations. This is helpful in getting deep insights of author’s work, personal and professional life.

We will base our user interface on their automatic method of expert profile construction.

References

1. Latif A., Afzal M.T., Helic D., Tochtermann K., Hermann Maurer H., Discovery and Construction of Authors’ Profile from Linked Data (A case study for Open Digital Journal). coronet.iicm.edu (2010)
2. Latif A., Afzal M.T., Tochtermann K., Constructing experts profiles from Linked Open Data. Emerging Technologies. (ICET)

According to this paper of M. Stankovic there are many potential sources of evidence about users’ interest and expertise (e.g., research papers, blogs, activities) are becoming ubiquitously present as Linked Data. In their paper they presented a research effort for suggesting the right way to search for potential Open Innovation problem solvers in Linked Data sources, by looking at the structure of available data sources. In addition, the author sought to develop ways of suggesting domains of expertise that are in some way relevant to the domain of the Open Innovation problem, in order to enable a cross-domain solution transfer.

Expert finding approaches mostly took a legacy data set (research paper pdf files [2], e-mails, documents, etc.) and tried to extract structured data containing some traces about user’s knowledge and interest. These data are later used for ranking them by the level of expertise. In each of those approaches there is an implicit assumption about what does it take to be an expert, and what makes one a better expert then another. The dataset that is used partially determines this assumption. An approach that uses research papers as a corpus, implicitly assumes that persons who wrote a research paper on a certain topic are experts on the topic. The author called such assumptions expertise hypotheses.An example expertise hypothesis would be: “authors of two or more research papers on a particular topic can be considered as experts on that topic”. Different expert search approaches use different expertise hypotheses.

This project expert case is: “Twitter users that are tweeting about certain conference have an interest in that research topic”. Even more accurate: “Two Twitter users who are constantly tweeting about similar scientific conferences are experts in a similar research topic”.

The authors noted a difference from the time before Linked Data (LD). At that time expert search approaches had to focus on a particular hypothesis based on the data that was available to them. LD-based approaches can benefit from plenty of different kinds of traces and can choose among many expertise hypotheses. Therefore the choice of an appropriate expertise hypothesis becomes a challenge for LD-based expert finding. The authors proposed a way to suggest appropriate expertise hypotheses for a given problem topic, based on the type of user trace that is used in those hypotheses. They argued that experts from different domains would use different communication channels (e.g., one domain mostly tweets, the other mostly blogs) and leave different user traces. Detection of such patterns would allow to choose the expertise hypotheses that rely on traces significant for the given domain. M. Stankovic proposed to explore the structure of LD and establish LD metrics that would help to identify good evidence types for particular topics. These metrics might also be beneficial in choosing the right data set in a scenario of running distributed queries over several data sets.

Hence our framework could also easily support more hypotheses considering the fact that it is based on linked data. As stated many times before. We limit the evaluation to a proof-of-concept for one hypothese.

A simple metric was defined as the number of available instances. Further on, it would be interesting to know the number of instances of a certain type, having some particular concept property. The author thus defined another metric as a set of concepts that identify topics that are associated with the instances to be counted. A similar metric is in fact already used by systems that use data summaries to accelerate query execution like [3]. Those graph summaries could directly serve as a source of that metric. If the data taken into account is a representative subset of world’s data, then higher values of should indicate that most of interactions around a particular topic are happening on a particular type of medium, and thus the use of such sources might result in higher precision.

This projects framework uses a similar metric. To calculate the relevance of a certain item, the count of unweighted links to that item is computed. To improve results, weighted links could be considered. This is however not in the scope of this thesis.

M. Stankovic assumed that prevailing use of particular topics with particular type of trace instances could positively influence the effectiveness of expert search. They constructed a sample, rich enough data set with different types of traces and many different topics. Such a data set will enabled them to launch experiments for evaluating the correlation of particular LD metrics with the precision and recall of expert identification. They first imported existing public LD data about user traces (mostly publications). In addition Sindice.com gave us a number of public data sources containing blog and publications data. To enrich the set we constructed an extractor for conference event tweets.

The author has identified main challenges for the Semantic Web technologies to help perform Open Innovation on the Web. They have designed an approach for finding potential solvers on LD, by picking the right expertise hypothesis.

References

1. Stankovic, M., Open Innovation and Semantic Web: Problem Solver Search on Linked Data. iswc2010.semanticweb.org
2. Buitelaar, P., & Eigner, T. (2008). Topic Extraction from Scientific Literature for Competency Management.The 7th International Semantic Web Conference. Karlsruhe, Germany.
3. Harth, A., Hose, K., Karnstedt, M., Polleres, A., & Sattler, K. (2010). Data Summaries for On- Demand Queries over Linked Data. In Proceedings of the 17th international conference on World Wide Web, WWW2010 (pp. 411-420). Raleigh, NC, USA: ACM Press.

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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.

Stankovic et al. conducted several experiments to evaluate the feasibility of existing expert search and profiling approaches on a recent snapshot of the LOD cloud. Our findings indicate that LOD cloud is already a useful source for some kinds of expert search approaches (e.g., those based on publications and professional events) but still has to meet certain requirements in order to reach its full potential. In the existing literature on expert finding, different authors make different assumptions on what makes an expert and how expertise can be assessed. They called these assumptions expertise hypotheses.

One section presents hypotheses which assume that a user’s online activities related to a certain topic imply his/her expertise in that topic. In order to be a useful evidence source for expert search, the LOD cloud needs to satisfy certain conditions. They have designed the some tests to verify if those conditions are met by the current LOD cloud and conducted these test for each particular expertise hypothesis. In particular in this thesis project 3 hypothesis are very relevant:

• H4: If a user answers questions (on topic X) from experts on topic X then he might himself be an expert on topic X
• H5: If a user is among the first to discover (and share) “important/good” resources (i.e. resources which become later popular) on topic X, then he might be an expert on topic X.
• H6: If a user participates in collaborative software development project then he might be an expert in the programming language that is used in the project.

The authors found some interesting linked data resources for H5 the Faviki, Virtuoso (via Sponger) and for H6 the DOAP Store, RDFOhloh. Q&A sites are a useful source of data about expertise, and despite the possibility to represent them using the SIOC ontology, they have not found any such website that provides SIOC-based data export. H4 was thus not applicable on the current LOD cloud.

Faviki is a good example of this issue as well. It provides useful data about tagging with links to DBPedia, but the data about the time of tagging is missing, thus making it difficult to design expert search approaches based on H5. In some cases LOD is not a good source for expert finding because the datasets which may be used by certain hypothesis are not interlinked. During their evaluation they have found some examples of data that would be a useful source for expertise evidence if they would be interlinked.

For approaches based on H6, a useful data source is RDFOhloh – the export of data related to software development projects that take place at Ohloh . This source provides both inverse functional properties for the members of the projects, and links to DBPedia concepts identifying the programming languages that are used. It is thus perfectly suited for finding experts on specific programming languages.

DoapStore is a promising source for H6-based approaches. It contains data on software development projects and their participants. Although the programming language data are present, they are only given in form of literals, and the presence of links to some general concepts (e.g. DBPedia or Freebase ones) is not common. The H6-based approaches may rely on RDFOhloh for a more complete support. RDFOhloh also provides direct links to DoapStore descriptions, thus making the integration possible despite the lack of links in DoapStore.

The authors concluded that expert search and profiling systems aggregate and analyze certain types of data depending on the types of expertise hypotheses they use. Traditional approaches tend to retrieve their data from closed or limited data corpuses. LOD on the other hand allows querying the whole Web like a huge database, thus surpassing the limits of closed data sets, and closed online communities. They believe that this opens new possibilities for traditional expert search and profiling systems which usually only rely on data from their local and limited databases or on unstructured data gathered from the Web. LOD also stands up for a great promise to deliver mutli purpose data that can be used to find experts in many domains and with many different expertise hypotheses. In this paper they have explored the potentials and drawbacks of LOD in comparison to traditional datasources used for expert search. They haven’t only asked the question what LOD can do, but also what one can do for LOD to make it an even better source of expertise evidence.

References

• Stankovic M., Wagner C., Laublet P., Jovanovic J.: Looking for experts? what can linked data do for you. In: Linked Data on the Web.

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In the paper “Making Sense of Twitter“ [1], David Laniado and Peter Mika first took a look at whether hashtags behave as strong identifiers, and thus whether they could serve as identifiers for the Semantic Web. Twitter users have adopted the convention of adding a hash at the beginning of a word to turn it into a hashtag. Hashtags are meant to be identifiers for discussions that revolve around the same topic. When used appropriately, searching on these hashtags would return messages that belong to the same conversation (even if they don’t contain the same keywords), and thereby solving the aggregation prob- lem. Coincidentally, this is the same function that strong identifiers (URIs) play in the Semantic Web. The questions they asked then is which hashtags behave as strong identifiers (if any), and if they could be mapped to concept identifiers in the Semantic Web?According to the authors there are a number of desirable criteria that a hashtag should fulfill in this role, similar to how ‘cool URIs’ are differentiated from poor URIs: frequency, specificity, consistency in usage and stability over time.  In line with previous works on the analysis of folksonomy systems [2], they capture the semantics of the hashtags by their usage in the social media system. In particular, they represented the meaning of hashtags using a Vector Space Model (VSM) [3].  For this study they relied on a dataset of 539,432,680 messages, collected over the whole month of November 2009 (about 18 million per day).

In order to assess how well their metrics were able to indicate which hashtags represent stable concepts with a unique identity, they have performed a manual evaluation on a random sample of 257 hashtags. Slightly more than half of the tags (137) could be associated to a Freebase entry; this is higher than the number of named entities because Freebase contains also some general terms. As expected, most application and sentiment tags could not be mapped to Freebase. Only 33% of application and 14% of sentiment tags could be resolved, and many of these mappings are rough approximations of the intended meaning.

Laniado and Mika found that not all hashtags are used in the same way, not all of them aggregate messages around a community or a topic, not all of them endure in time, and not all of them have an actual meaning. In this work they had addressed the issue of evaluating Twitter hashtags as strong identifiers, as a first step in order to bridge the gap between Twitter and the Semantic Web. The first contribution of this paper stands in the formalization of the problem, and in the elaboration of a number of desired properties for a good hashtag to serve as a URI. Based on these data, they had tested the results obtained with the algorithms described in their paper, showing how a combination of the proposed measures can help in the task of assessing which tags are more likely to represent valuable identifiers. These results are promising, with respect to the perspective of anchoring Twitter hashtags to Semantic Web URIs, and to detect concepts and entities valuable to be treated as new identifiers.

References

1. Laniado, D., Mika. P.: Making sense of twitter. In: The Semantic Web–ISWC 2010 (2010)
2. Cattuto, C., Benz, D., Hotho, A., Stumme, G.: Semantic grounding of tag related- ness in social bookmarking systems. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 615–631. Springer, Heidelberg (2008)
3. Raghavan, V.V., Wong, S.K.M.: A critical analysis of vector space model for infor- mation retrieval. Journal of the American Society for Information Science (1986)

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