Automated Syntactic Mediation for Web Service Integration
Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis @ ISWC2008
1. TAGora: Semiotic Dynamics of Online Social Communities EU-IST-2006-034721 Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis Martin Szomszor , Harith Alani, Kieron O’Hara, Nigel Shadbolt University of Southampton Iván Cantador Universidad Autonoma de Madrid
6. Personalisation Profiles could be exported to other sites to improve recommendation quality Profile of Interests Profiles could be used to support personalised searching Better user experience delicious.com
7. Consolidation and Integration currency travel hotels cuba http://dbpedia.org/resource/Cuba cuba holiday 2008 http://dbpedia.org/resource/Travel http://dbpedia.org/resource/Holiday http://dbpedia.org/resource/Category:Tourism
10. Tagging Variation [1] Szomszor, M., Cantador, I. and Alani, H. (2008). Correlating User Profiles from Multiple Folksonomies . In: ACM Conference on Hypertext and Hypermedia, 2008 , Pittsburgh, Pennsylvania. Raw Tags Filtered Tags
This is preliminary research so there are many gaps to be filled and much future work to be done.
This is a snapshot of how I participate in the World of Web 2.0 I bookmark pages in delicious, record my listening habits in last.fm and publish my photos using flickr
One important trend that we can observe is that it’s Increasingly common for users to maintain a profile in multiple social networking sites. Ofcom published April 2, survey carried out September – October 2007 Number of profiles significantly higher for under 21’s If you participate in such sites, your are likely to have multiple profiles. This is intuitive - People realise the benefits quickly and often signup to other sites to meet difference requirements.
This is a representation of where we’re heading with this work. The activity elicited from each of the individual’s accounts tells us something different about their interests: Technorati and delicious highlight areas of interest on the web flickr and facebook tell us about the events and places the person has been imdb and last.fm givess with knowledge of the user’s music listening and movie watching habits
We believe that providing such profiles of interest could help with Recommendation: You could image providing your profiles to a Site like Amazon so they can give you better recommendations Such profiles could also be used to personalise searching It’s all about providing users with a better experience without An overhead. Our idea is that this should happen automatically
Another motivation for this work is for consolidation and integration. People have information distributed across different sites and it Would be helpful to support them with an integrated view of this information. There are often activities in different sites that could be related via A common event or interest.
In case you’re not already familiar with tagging and folksonomies, here is an example from my delicious and flickr account What’s great about tagging is that it copes well with multimedia content. Web Pages, videos, photos, blogs, music, etc…
Overtime, the cumulative frequencies of the tags you use can be represented with a tag-cloud. This gives a visual snapshot of the terms that you use most frequently. When we began this work, the first thing we did was develop a tool For viewing tag clouds from multiple domains. We noticed that many tags represented concepts that could be considered Interests of the users. Hence, the motivation for our work is to exploit this tagging
In the field of folksonomy analysis, it’s also important to consider the syntactic Habits of tagging In previous xfolksonony work, we discovered that tagging habits can be quite erratic people use singular / plural / gerrand form compound nouns may be formed using _ - or nospace synonyms can be used misspellings also common
Now I will move on to describe the architecture for our system. The aim is to start from a user uri (such as a homepage or blog) And create a foaf file depicting the interests of the user via References to wikipedia category uri’s.
If you’re not familiar with Google’s social graph API, it’s pretty simple. In most web2.0 sites, you’re offered the opportunity to reference A uri that describes you.
The Tag Filtering engine converts a list of raw tags to a set of filtered tags. This is explained in more detail in the hypertext paper so I won’t go Through it in great detail.
The results of the previous tag filtering is a set of tags and their associated frequencies. Weighting of the category is the sum of all the frequencies of the tags Matching of categories includes some simple stemming and pluralisation
Explain anomalous spike: Due to unconventional tagging practice
This is more of an observation than an evaluation Wanted to understand what kinds of interests can be extracted from delicious and flickr and how they differ. This is a nice result because it reflects our intuition about what we can learn from Each domain
One central argument behind this work is that more can be learnt about individuals by Examining multiple profile activites. To evaluate this at a user level, we decided To measure the increase in categories generated in their user profile by adding Flickr information to delicious.
To evaluate the approach in terms of how well it identifies the relevant Wikipedia categories, we generated a Random sample of 100 users and then randomly select 1 tag from each of their delicious and Flickr profiles. We looked at their posts for the tag to establish accuracy Couple of false positives: oracle (divinity or database) labrador (dog and city in canada)
Creating such a linked model of tags with references to dbpedia uris