notes-sssw13-1
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-3095559950473712969
actor: http://www.blogger.com/profile/12227954801080178130
content: Semantic Web questions we couldn't ask 10 years ago from Frank Van Harmelen Semantic Web & Web of data = a more manageable mission. Metaweb movie - got bought by Google and incorporated into Knowledge Graph. SW Principles: 1\\\\. Give everything a name (entities). 2\\\\. Relations form graph between things. 3\\\\. Names are addresses on the Web (so we inherit properties of Web like AAA). This becomes Giant Global Graph. (Maybe SW should be called Giant Global Graph?) 4\\\\. Add semantics. Types of things, relationships. Hierarchy, constraints: Inferences. Bounding shared beliefs by sharing ontological information. Space for confusion gets smaller and we begin to agree on interpretation of information. Semantics = predictable inference. Google: from just links to results, to information boxes (last May). Can't directly address Google Knowledge Graph. NXP (microprocessors): 26,000 products. Integrated all databases into triplestore. Exposing subset of triplestore to customers. BBC: 125 million triples. Many data sources. APIs to website. Own ontologies. All have the same triple-layer architecture: Raw data SW layer Output / API / UI etc DataGov: eg. air quality in cities, campaign money, if policies work. Companies don't care about SW, but are using these technologies for their own IRL purposes. These are all different types of use cases of SW technologies: search; data integration; content re-use; SEO; data publishing. It's important that the SW graph is so big. More questions to ask. Good that we no longer know how big, or how fast it is growing... Tens of billions of facts. How many are really permanent? Some are stable, some will disappear - just like the 'regular' Web. "...it being a mess is the only reason why it scales." We need to get used to the idea of SW being a mess - aka "a system so large you can no longer enforce central control" (complex system). The LD cloud is still poorly interconnected, but good graph properties. SameAs.org Heterogeneity is unavoid…
name: [Notes] Frank van Harmelen at #SSSW2013
published: 2013-07-08T13:46:00.000Z
tag: done
tag: Done
tag: events
tag: frank+van+harmelen
tag: notes
tag: phd
tag: raw+notes
tag: semantic+web
tag: sssw13
tag: sssw2013
updated: 2013-07-17T13:50:38.464Z

notes-sssw13-2
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-3215366757403317415
actor: http://www.blogger.com/profile/12227954801080178130
content: (by Mathieu D'Aquin). Introduction to Linked Data from Mathieu d'Aquin Linked Data = universal connections, like Lego. Universal is why it's important. Workshop instructions. The only problem we had during the workshop was disagreement about how to read the 'broader' and 'narrower' relations between courses. It instinctively ready contrary to what (my) common sense suggested (eg. that 'arts and humanities' is broader than 'history', which some people disagreed with). A quick reference to the ontology documentation resolved that.
name: [Notes] Introduction to Linked Data at #SSSW2013
published: 2013-07-08T13:56:00.000Z
tag: done
tag: Done
tag: events
tag: linked+data
tag: mathieu+d'aquin
tag: notes
tag: phd
tag: semantic+web
tag: sssw13
tag: sssw2013
tag: mathieu+daquin
updated: 2013-07-17T13:59:11.745Z

notes-sssw13-3
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-6724934902398748996
actor: http://www.blogger.com/profile/12227954801080178130
content: It's a dynamic world! Ubiquitous streams and the Linked Data Web from Manfred Hauswirth (pooh42) Streams: Any time dependant data / changes over time. Has done a paper about P2P stuff. Data silo - "natural enemy of SW scientists" Massive exponential growth of global data. Still have to integrate dynamic data with static data. Multiway joins are domintion operator. Need to be efficient. Everything/body is a sensor. Various research challenges: Query framework. Efficient evaluation algorithm. Optimise queries. Organisation of data. CoAP ~= http for sensors. Stuff about sensor networks and context - useful for Michael. Common abstraction levels for understanding. SSN-XG ontology Application: SPITFIRE You can buy a sensor off the shelf that runs a binary RDF store and can be queried. So possible to use SW tech with resource constrained devices. RESTful sensor interfaces stuff being standardised - CoRE, CoAP. Linked Stream Model CQELS-QL (extension to SPARQL 1.1; already legacy) Rewrite query to spit out static and dynamic - lots of overhead. But need to optimise between these. Neither existing stream processing systems nor existing databases could be efficient enough. So the built own LD stream processing system. (Optimised and adopted existing database stuff). HyperWave - didn't succeed. Didn't listen to customers and wasn't open source (license fees). But better than hypertext was back in the day. Performance important for success/uptake. Just putting it on cloud infrastructure doesn't mean it scales. Need to parallelize algorithm. Took it to a point where adding more hardware did help. Problems! Inconsistent results, engines don't support all query patterns.. very early, don't fully understand yet. Long way to go. How to prove what is a correct result? Needs to be easy to use - dumb it down. Linked Stream Middleware (available): Flights, live trains - SPARQL endpoint!, traffic cams. SuperStreamCollider.org Current Tomcat problem with twitter streams. To do? Scaleabil…
name: [Notes] Manfred Hauswirth at #SSSW2013
published: 2013-07-08T14:23:00.000Z
tag: done
tag: Done
tag: events
tag: linked+data+stream
tag: manfred+hauswirth
tag: notes
tag: phd
tag: raw+notes
tag: semantic+web
tag: sensors
tag: sssw13
tag: sssw2013
tag: stream
updated: 2013-07-17T14:25:01.453Z

notes-sssw13-6
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-3867912077064038876
actor: http://www.blogger.com/profile/12227954801080178130
content: Social Media Analytics with a Pinch of Semantics Using semantics to solve problems (not solving problems of semantics). SM for businesses: Analytics. How to measure success? SM silos impeding progress. In-house social platforms increasing, so even more so. SIOC to integrate online community information. SIOC + FOAF + SKOS. FB Graph. People are likeaholics. Their 'likes' become meaningless, so you need to take this into account when making recommendations. Browse your data and understand user actions. Behaviour analysis. Bottom-up analysis. Can handle unexpected or emerging behaviours. Community members classified into roles. Identify unknown roles. Cope with role changes over time. Clustering to identify emerging roles. eg. focussed novice; mixed novice; distributed expert; ... Spectrum across users you can or can't do without. Extending an ontology built on SIOC. Encoding rules in ontologies with SPIN. Three categories of features: Social features (people you follow, people follow you, ...) Content features (what you're posting, keywords, ...) Topical/semantic features Which behaviour categories you need to cater for more than others? How roles impact activity in online community. Consistently see that you need some sort of stable mixture of behaviours for activities in forums to increase. ==> Don't know what's causing which. What is a healthy community? Use behaviour analysis to guess what's going to happen to community. Eg. Churn rate. User count. Seeds/non-seeds prop (how many / if people reply to you). Clustering. Unexpected: the fewer focused experts in the community, the more posts received a reply. (But quality of answers?) Community types (Little work in this space) Muller, M. (CHI 2012) community types in IBM Connections: Communities of Practice Teams Technical support .. .. (see slides..) Need an ontology and inference engine of community types. Wants an automated process to tell you what type of community it is - it might be something it wasn't set up fo…
name: [Notes] Harith Alani at #SSSW2013
published: 2013-07-10T20:30:00.000Z
tag: done
tag: Done
tag: behaviour+analysis
tag: harith+alani
tag: online+communities
tag: phd
tag: semantic+web+summer+school
tag: semantic+web
tag: social+media
tag: sssw13
tag: sssw2013
updated: 2013-07-30T15:36:36.995Z

notes-sssw13-7
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-554189058804958709
actor: http://www.blogger.com/profile/12227954801080178130
content: Tools and Techniques Recommender systems Input: Set of users + set of items + rating matrix. Problem - given user, predict rating for an item. In real world, recommendation matrix data is sparse. Can use hybrid approaches. Collaborative RS: Like Amazon. Based on other users with similar profiles. Experimentally better than content-based, but you don't always have many users. Knowledge-based RS: No/little user history. Based on domain knowledge. User-based collaborative recommendation: Pearson's correlation coefficient - baseline. Imagine millions of users - computing similarities takes a lot of time. So .. Item-based collaborative recommendation: Focus on items not users. Compute similarity between each pair of items. Don't have to compute similarity between items that don't have overlapping ratings. Cosine similarity / adjusted cosine similarity (taking into account average rating related to a user to eliminate some bias). Content-based RS: Based on description of item and profile of user interests. Items are described in terms of attributes/features. Finite set of values associated with features. Item representation is a vector. Don't necessarily have complete descriptions of items - just have a 0 in your vector. Similarity between items: Jaccard similarity. Cosine similarity and TF-IDF (term frequency - inverse document frequency). Batch compute similarities offline, then use similarities to compute ratings on the fly based on user profile. Predict rate only for N nearest neighbours of items in user profile, that are not in the user profile. An item is worth rating if more than x of N number of neighbours are within user profile. Using LOD To mitigate lack of information/descriptions about concepts/entities. Recommender systems are usually vertical, but LD lets you easily build a multi-domain recommender system. To avoid noisy data, you have to filter it before feeding your RS. Freebase. Tiapolo Automating typing of DBPedia entities. Vector space model for LOD MA…
name: [Notes] Tommaso Di Noia at #SSSW2013
published: 2013-07-10T21:30:00.000Z
tag: done
tag: Done
tag: linked+data
tag: notes
tag: phd
tag: recommendations
tag: recommender+systems
tag: semantic+web+summer+school
tag: semantic+web+tools
tag: semantic+web
tag: sssw13
tag: sssw2013
tag: tommaso+di+noia
updated: 2013-07-30T16:11:22.128Z

notes-sssw13-8
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-6703915713273014969
actor: http://www.blogger.com/profile/12227954801080178130
content: Make a model of what is happening. WeSenseIt \\\\- citizen water observations. River belongs to citizens, not authorities. Physical sensors (hard layer) are expensive and brittle. So use people instead (soft layer, social). Give people small sensors. Phones. Then you just need software for information management. capture. integrate and correlate data. share. Can't rely on phones. Old people in Doncaster. Give them easy sensors instead. camera humidity position GPS water depth, velocity rainfall via accelerometer could coverage via luminocity Costs about EUR 80. Open Source & hackable. Not expected to substitute professional sensors, but a way to crowdsource information you would never get. In Delft Give people flood preparation advice and record who ticks things off, to build a picture of who/how/when preparations take place. The Floow Ltd "Commercialises data solution for telematic insurance." World divided 10x10m squares, sense things everywhere. Traffic risks. Sensors tell you people are going somewhere, not why. That's what social media can tell you. Monitoring development of a house fire via Twitter. Seeing events through the eyes of the community. Social streams: High volume Duplicated, incomplete, imprecise, incorrect Time sensitive / short term Informal Only 140 characters Spam Large music festival. Monitor geolocated messages, trends, topics and relations. Most 'critical' events were management issues. Developing system to warn you automatically about things to pay attention to. Look/listen for event within 72 hours. 10 minutes to find out what it was. \\\\- Simulation of station bombing. Minute by minute description of event. 1.5 billion messages. Linguistic issues Alternative language Negatives Conditional statements Hope/prayer statements Irony/sarcasm Ambiguity Unreliable capitalisation Data sparsity Four things when monitoring: What Identify, classify, cluster Events and sub-events Involved entities Who Human or not? Bots can be beneign, but many are a…
name: [Notes] Fabio Ciravenga at #SSSW2013
published: 2013-07-11T21:00:00.000Z
tag: done
tag: Done
tag: fabio+ciravenga
tag: notes
tag: phd
tag: semantic+web
tag: semantics
tag: sensor+networks
tag: sensors
tag: social+machine
tag: sssw13
tag: sssw2013
updated: 2013-07-30T17:29:21.713Z

notes-sssw13-poster
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-5248111370792171177
actor: http://www.blogger.com/profile/12227954801080178130
content: Things to investigate further! LibRDF - linkeddata-perl for Debian by Kjetil Kjernsmo. Rakebul Hasan, Fabien Geandon (? not sure about names, can't read my handwriting..) - Trustworthiness of inferences. Taldea - fostering spontaneous communities. Ghada Ben Nejma. NERD ontology for spotting entities. nerd.eurecom.fr See photo:
name: [Notes] Poster session at #SSSW2013
published: 2013-07-08T14:39:00.000Z
tag: done
tag: Done
tag: events
tag: notes
tag: phd
tag: poster
tag: semantic+web
tag: sssw13
tag: sssw2013
updated: 2013-07-17T14:39:34.170Z

sssw2013-practical
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-5661119888817867566
actor: http://www.blogger.com/profile/12227954801080178130
content: Harith Alani talked about using semantics to solve problems around evaluating the success of social media use in business. The SIOC ontology is widely used to describe online community information. It's not as simple as measuring someone's engagement with a brand's online presence - people are 'likeaholics' on Facebook, so you have to look at someone's whole behaviour profile to judge whether their like means anything or not. It's no good just aggregating your data and spewing out numbers - you have to browse the data and try to understand where it came from. He mentioned how little work has been done in classifying community types. Most of the work that has been done seems to be with social networks internal to an organisation. A bottom-up approach to community analysis can handle emergent behaviours and cope with role changes over time. Looking at behaviour categories and roles can help an organisation to decide who to concentrate on supporting and how in order to sustain the community. The results they have seen so far suggest that a stable mix of the different types of behaviours are needed to increase activities in forums - but they don't know what causes what. They're reaching a point where they can use their behaviour analysis to guess what's going to happen to a community: how long it will last, how fast it will grow, how many replies a certain type of post is likely to get, etc. Next they want to be able to classify community types, and be able to look at activities within a community over a period of time and automatically discover what kind of community it is; it might be something different than what it was set up for. They created an alternative Maslow's Hierarchy of Needs to correspond with activities seen on forums, and found that most people are happy to stay at the lower levels of the hierarchy. For example, join a community, lurk for a bit, ask one question and leave. Not everyone wants or needs to be a power user. Papers are being written that fin…
name: #SSSW2013: Practical semantics and human nature
published: 2013-07-10T23:00:00.000Z
tag: done
tag: Done
tag: dbpedia
tag: fred
tag: harith+alani
tag: ontologies
tag: phd
tag: recommender+systems
tag: segovia
tag: semantic+web+summer+school
tag: semantic+web
tag: semantics
tag: social+media
tag: sssw13
tag: sssw2013
tag: tipalo
tag: tommaso+di+noia
tag: travel
updated: 2013-07-30T17:34:20.984Z

sssw2013-research
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-5969396273262467883
actor: http://www.blogger.com/profile/12227954801080178130
content: The 10th Summer School for Ontology Engineering and the Semantic Web _ _Sunday Arriving by train into Cercedilla, north of Madrid, we immediately encountered other confused looking folk with poster tubes. So we shared taxis (EUR 10) from Cercedilla station to the summer school residence further north, in the forest. After getting keys for our pleasant, single, en-suite rooms, arrivals congregated in the shade by the building to introduce ourselves.. Again, and again, and again, as new people continuously arrived over the space of a few hours. A really broad mix of people are here in terms of nationalities and places and levels of study, but I still haven't quite got used to the fact that answering 'Semantic Web stuff' is not specific enough in this crowd, when someone asks you what your research is about. Nobody needs convincing that these technologies are useful! Later we received schedules, maps, ill-fitting t-shirts* and very helpful name badges, and headed for dinner at the bar down the road. As is traditional when I write about my experiences in new places, I will describe the food every day. It has become apparent, at this residence at least, that variety of ingredients is not ordinary, so in this respect meals are simple. Dinner that first night started with a salad (lettuce, olives, tomato, onion, shredded beetroot and a single slice of hard boiled egg; no dressing), followed by - for the majority - slices of meat (beef? Pork? I dunno..) and fries. Mine was a plate of mushy green vegetables with a little seasoning, that was pretty tasty. Dessert was a single pear, delivered with ceremony, but otherwise unadorned. Healthy, at least. Yet we were all (those I sat with at least) were left feeling a little unsatisfied. I shared a table with a French, Spanish, Italian and Irish guy. Conforming appropriately to stereotypes, and setting up reputations for the rest of the week, the French and the Italian shared the bottle of wine on the table; the rest of us went wit…
name: #SSSW2013: Research in theory and practice, and where on earth am I?
published: 2013-07-08T23:30:00.000Z
tag: done
tag: Done
tag: cercedilla
tag: events
tag: hacking
tag: knowledge+engineering
tag: learning
tag: linked+data
tag: madrid
tag: ontologies
tag: phd
tag: poster
tag: presenting
tag: semantic+web+summer+school
tag: spain
tag: sssw13
tag: sssw2013
tag: summer+school
tag: travel
updated: 2013-07-17T14:52:15.424Z

sssw2013-social
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-4981764477729666207
actor: http://www.blogger.com/profile/12227954801080178130
content: We started work on the serendipity project before breakfast today, although I didn't make it down as early as some of my teammates. To start the day, Fabio Ciravenga talked about some really exciting practical applications of monitoring and analysing social media streams. It's particularly interesting during emergencies, or large events where problems might occur. The people on the ground make the perfect sensors if you can work out the differences between people who are saying something useful and who aren't; people who are really there, and people who are speculating or asking about the situation. A main problem has been that people tweet crap. They were trying to monitor a house fire, but so many people were tweeting lyrics from Adele's various singles at the time, which all apparently contain references to fire, it was almost impossible. They also put (or tapped into existing) sensors in peoples' cars to monitor driving patterns with the aim of more fairly charging for car insurance. I told my Mum about this the other day, and she was pretty alarmed by the idea. Which made me wonder how they'll get mass adoption, if it's going to go anywhere. Fabio did have some interesting things to say about using all this data ethically though, and never working for someone who is going to take that away from you. But in case the 'bad guys' do find out about all this data you have about people, keep a magnet handy. My notes are here. This was followed by a hands-on session where we got to mess with a mini version of the twitter topic monitoring system that Fabio's team use at large events, to try to answer questions about the Tour de France only by manipulating the incoming social media streams and following only links which came through that. Spanish omelette sandwiches were an amazing outdoor leisurely lunch. We headed to the pool down the road and chilled out there for a couple of hours. Us tough British folk found the water pleasantly tepid, whilst all those wimpy Europea…
name: #SSSW2013: Social semantics and serendipity
published: 2013-07-11T21:30:00.000Z
tag: done
tag: Done
tag: fabio+ciravenga
tag: phd
tag: pool
tag: semantic+web
tag: semantics
tag: serendipity
tag: social+machine
tag: social+media+streams
tag: social+media
tag: sssw13
tag: sssw2013
updated: 2013-07-30T18:22:38.186Z

sssw2013-team
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-2094529228400394655
actor: http://www.blogger.com/profile/12227954801080178130
content: We were introduced to the various mini-projects on Tuesday morning, and encouraged to form teams with people who weren't from the same university. I quickly shortlisted the five that sounded most interesting to me, but was disappointed that there weren't any about multimedia. Because how to evaluate a very subjective system is a potential problem for me, the project proposed by Valentina Presutti was my first choice: "Serendipity can be defined as the combination of relevance and unexpectedness: an information is considered serendipitous if it is at the same time very relevant and unexpected for a given user and in the context of a given task. In other words, a user would learn new relevant knowledge. To evaluate the performance of a tool (e.g., an exploratory search tool, a recommending system) in terms of its ability to provide users with serendipitous knowledge is a hard task because both relevance and unexpectedness are highly subjective. This miniproject focuses on two main research questions: what is the correct way of designing a user-study for evaluating an exploratory search tool performance in terms of serendipity? Is it possible to build a reusable set of resources (a benchmark) for evaluating ability to produce serendipity, allowing easier evaluation experiments and comparison among different tools?" Nobody else seemed to be interested though, so I resigned myself to not being able to do it... until I explained the project and why it was interesting, to the best of my ability, to Andy, Oscar and Josef, and they were sold enough to mark it as our first choice. Thus Team Anaconda Disappointed (a name of significant and mysterious origins) was born, and Project Cusack (because of the movie Serendipity, which nobody got) was underway. Our first lecture today was from Lynda Hardman, about telling stories with multimedia objects. It was super relevant to what I'm doing, to the point where I'm surprised I hadn't come across her work already. My notes are here. …
name: #SSSW2013: Collaborative ontology engineering and team formation
published: 2013-07-09T19:58:00.000Z
tag: done
tag: Done
tag: events
tag: lynda+hardman
tag: ontologies
tag: ontology+engineering
tag: phd
tag: semantic+web
tag: serendipity
tag: sssw13
tag: sssw2013
tag: valentina+presutti
updated: 2013-07-30T14:47:29.341Z

week-review-catching
type: Article
bloggerid: tag:blogger.com,1999:blog-18505529.post-7090165647671052514
actor: http://www.blogger.com/profile/12227954801080178130
content: Returned to Edinburgh on Wednesday. Suffering from post-travel depression. That's a thing, I looked it up. It's a thing nobody has any sympathy for. Life is hard. Started and finished my poster for the Semantic Web Summer School. Went to Spain for aforementioned summer school. Spain is hot. Let this mental week of learning begin!
name: Week in review: Catching up, poster
published: 2013-07-07T17:00:00.000Z
tag: doing
tag: Doing
tag: phd
tag: post-travel+depression
tag: poster
tag: semantic+web
tag: sssw13
tag: week+in+review
updated: 2013-07-07T17:00:03.565Z

sssw13
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total items: 12

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