{"@context":{"rdf":"http://www.w3.org/1999/02/22-rdf-syntax-ns#","rdfs":"http://www.w3.org/2000/01/rdf-schema#","owl":"http://www.w3.org/2002/07/owl#","foaf":"http://xmlns.com/foaf/0.1/","dc":"http://purl.org/dc/elements/1.1/","dct":"http://purl.org/dc/terms/","sioc":"http://rdfs.org/sioc/types#","blog":"http://vocab.amy.so/blog#","as":"https://www.w3.org/ns/activitystreams#","mf2":"http://microformats.org/profile/","ldp":"http://www.w3.org/ns/ldp#","solid":"http://www.w3.org/ns/solid#","view":"https://terms.rhiaro.co.uk/view#","asext":"https://terms.rhiaro.co.uk/as#","dbp":"http://dbpedia.org/property/","geo":"http://www.w3.org/2003/01/geo/wgs84_pos#","doap":"http://usefulinc.com/ns/doap#","time":"http://www.w3.org/2006/time#"},"@graph":[{"@id":"https://rhiaro.co.uk/2013/05/week-review-annotating","@type":"as:Article","blog:bloggerid":"tag:blogger.com,1999:blog-18505529.post-8314997278330328670","as:actor":{"@id":"http://www.blogger.com/profile/12227954801080178130"},"as:content":"
6th - 12th May
\r\nDiscovering lots of things to write about semantically annotating multimedia\r\ncontent. I decided there are three main ways to do this:
\r\nThese categories overlap somewhat really, and when I get round to it I'll type\r\nmy Venn diagram up.
\r\nTechnical is easy, and a lot of that is automatically captured by hardware\r\nor software used to produce and edit works. It's also relatively easy to\r\nextract automatically. Standards like MPEG-7 and MPEG-21 take care of\r\nformalising it, and Jane Hunter turned these standards into semantic\r\nontologies in 2002.
\r\nBibliographic can largely - but not entirely - be covered by vocabularies\r\nthat have been around forever like Dublin Core, FOAF and various library-\r\noriginated things. Things that might be missing (or I just haven't found them\r\nyet) are associating roles with tasks involved in digital media production,\r\nsince pieces are often a collaborative effort. has some idea of\r\nparticipants and roles, but the purpose of is digital rights management\r\nstuff, so it's more concerned with the distribution change, I think, than\r\ngranular production of content. I haven't read much about it yet.
\r\nContent is more interesting, and potentially more useful for ordinary human\r\nbeings. Imagine querying IMDB for "that film where John Goodman arrests an\r\nanimated talking moose on a US highway" instead of scouring John Goodman's filmography or googling for pictures\r\nof animated meese until you see the right one. Annotating characters, objects\r\nand events, and stringing them onto a timeline is possible with OntoMedia. It's very focussed around narratives, which is great, but doesn't\r\nlink back to technical so much. So if you did find the answer to that\r\nquery, it wouldn't be able to serve up the timestamp of that particular scene.
\r\nOn top of what I've looked at already, I still have this list to\r\n(re)investigate:
\r\nA thing I want to do is annotate some amateur content with OntoMedia and with\r\nABC to see how they compare. Maybe I'll do\r\nasdfmovie, because it has\r\nassociated comics, and multiple people participating in production. Then I'll\r\ndo something live action as well, because I can't base all my research on non-\r\nsequitur lolrandom stick figure cartoons.
\r\nNow, back to work..
\r\nMake a model of what is happening.
\r\nWeSenseIt \\\\\\\\- citizen water observations.
\r\nRiver belongs to citizens, not authorities.
\r\nPhysical sensors (hard layer) are expensive and brittle.
\r\nSo use people instead (soft layer, social).
Give people small sensors. Phones.
\r\nThen you just need software for information management.
Can't rely on phones.
\r\nOld people in Doncaster.
Give them easy sensors instead.
\r\nCosts about EUR 80.
\r\nOpen Source & hackable.
\r\nNot expected to substitute professional sensors, but a way to crowdsource\r\ninformation you would never get.
\r\nIn Delft
\r\nGive people flood preparation advice and record who ticks things off, to build\r\na picture of who/how/when preparations take place.
\r\nThe Floow Ltd
\r\n"Commercialises data solution for telematic insurance."
\r\nWorld divided 10x10m squares, sense things everywhere.
\r\nTraffic risks.
Sensors tell you people are going somewhere, not why.
\r\nThat's what social media can tell you.
Monitoring development of a house fire via Twitter.
\r\nSeeing events through the eyes of the community.
Social streams:
\r\nLarge music festival. Monitor geolocated messages, trends, topics and\r\nrelations.
\r\nMost 'critical' events were management issues.
\r\nDeveloping 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.
\r\n\\\\\\\\- Simulation of station bombing.
\r\nMinute by minute description of event.
\r\n1.5 billion messages.
Four things when monitoring:
\r\nBig problem - people tweet crap!
\r\nPeople don't realise when people nearby are in danger.
Deception on social media
\r\nFalse crowdsourcing political support on social networks.
\r\nSmear campaigns using bots.
\r\nBots to foster / prevent social unrest.
Identifying bots
\r\n23 behavioural features.
\r\nFeature set is open.
\r\nRecognise 90% of bots - more than humans can do.
Very small amount of tweets are geolocated, it's useless.
\r\nHave to use the text.
Timestamp is not necessarily correct.
\r\nIssues in events
\r\nNo infrastructure (eg. at music festivals).
\r\nPhone signal issues, phone charging issues.
Most tweets from outside event.
\r\nConclusions
\r\nNeed to convince citizens that authorities are not spying on them.
\r\nNeed to convince authorities that citizens are not all criminals.
Privacy and legality issues.
\r\nCreating a company on this research would be unethical.
\r\nNeed to pass the right message. Full disclosure. Non-intrusive use of tweet\r\ncontent.
What happens when authorities demand this technology for privacy-invading\r\nstuff.
\r\nHave to be careful with what you publish.
\r\nAlways assume the bad guys have thought of what you thought of.
\r\nAlways be in a situation where you can destroy your data at short notice.
\r\nBit legal barrage behind them. Know what they are/aren't allowed, know what\r\nthey do/don't have to do.
\r\nStart leading a blameless life.
Harith Alani talked about using semantics to solve problems around evaluating\r\nthe success of social media use in business. The SIOC ontology is widely used\r\nto describe online community information. It's not as simple as measuring\r\nsomeone's engagement with a brand's online presence - people are 'likeaholics'\r\non Facebook, so you have to look at someone's whole behaviour profile to judge\r\nwhether their like means anything or not. It's no good just aggregating your\r\ndata and spewing out numbers - you have to browse the data and try to\r\nunderstand where it came from.
\r\nHe mentioned how little work has been done in classifying community types.\r\nMost of the work that has been done seems to be with social networks internal\r\nto an organisation. A bottom-up approach to community analysis can handle\r\nemergent behaviours and cope with role changes over time. Looking at\r\nbehaviour categories and roles can help an organisation to decide who to\r\nconcentrate on supporting and how in order to sustain the community. The\r\nresults they have seen so far suggest that a stable mix of the different types\r\nof behaviours are needed to increase activities in forums - but they don't\r\nknow what causes what. They're reaching a point where they can use their\r\nbehaviour analysis to guess what's going to happen to a community: how long it\r\nwill last, how fast it will grow, how many replies a certain type of post is\r\nlikely to get, etc.
\r\nNext they want to be able to classify community types, and be able to look at\r\nactivities within a community over a period of time and automatically discover\r\nwhat kind of community it is; it might be something different than what it was\r\nset up for.
\r\nThey created an alternative Maslow's Hierarchy of Needs to correspond with\r\nactivities seen on forums, and found that most people are happy to stay at the\r\nlower levels of the hierarchy. For example, join a community, lurk for a bit,\r\nask one question and leave. Not everyone wants or needs to be a power user.
\r\nPapers are being written that find patterns in individual datasets for a\r\nparticular community in a particular context. Harith and his team are getting\r\ntired of this; they want to generalise across communities. So they took seven\r\ndatasets and looked at how the analysis features differed as well as comparing\r\nthe results across community types, randomness (vs. topicality) of datasets,\r\nand compared similar experiments.
\r\nUpcoming work includes the Reel Lives project, in which UoE is involved.\r\nThey're taking media fragments - photos, videos, audio clips, text recorded as\r\naudio - and creating automated compilations to tell a story.
\r\nAnother is social methods to change energy consumption behaviour. LiSC in\r\nLincoln did something in this area back in the day.. an app that posted that\r\nyou were listening to an embarrassing song on your facebook feed if you left\r\nyour lights on.
\r\nNotes from Harith's talk are here.
\r\nSSSW 2013 - Feeding Recommender Systems with Linked Open Data from Tommaso Di Noia
\r\nFrom Tommaso Di Noia's talk, I learnt that recommender systems have a lot of\r\nmaths behind them, especially for evaluating things, and reinforced something\r\nI already knew: I don't maths good enough to be taken seriously by most of the\r\nInformatics world. I think I understand the principles behind the maths, but\r\nwhen something is descried in just maths, I have no idea what it relates to.\r\nI'll work on this.
\r\nReal world recommender systems use a variety of approaches, including\r\ncollaborative (based on similar users' profiles); knowledge-based (domain\r\nknowledge, no user history); item-based (similarities between items); content-\r\nbased (combination of item descriptions and profile of user interests).\r\nLinked Open Data is used to mitigate a lack of information about entities, and\r\nhelps with recommending across multiple domains. You do have to filter the LD\r\nyou use before feeding it to your recommender system though, to avoid noise.\r\nNotes here.
\r\nTommaso's talk was followed up by a hands-on\r\nsession, where we got to poke about with some of\r\nthe tools he mentioned, including FRED\r\n(transforms natural language to RDF/OWL); Tipalo (gets entity types from natural language text); and\r\nusing DBpedia to feed a recommender system.
\r\nThen we worked on our mini-projects for the afternoon. We made some progress\r\ntowards breaking down the concept of serendipity and working out what\r\nproperties we might need to represent as linked data, and how we could\r\nobserver a user and work out if/when/how they were having serendipitous\r\nexperiences without intruding too much.
\r\nIn the evening we took a coach to 'nearby' historical town Segovia.\r\nApparently an extremely motion-sickness-inducing two and a half hour coach\r\njourney around twisty mountain paths is 'nearby'. Fortunately I was\r\ndistracted from this horrible journey by a conversation with Lynda Hardman,\r\nwhich I wish I had recorded. Lynda challenged various aspects of my PhD until\r\nI could explain/justify them reasonably, including:
\r\nShe also recommended a number of resources, including theses of her recent\r\nformer students to help me with a structure for my own, and advice on\r\nmaintaining a healthy balance between thinking and doing.
\r\nPlus she used to live in Edinburgh, more or less across the road from where I\r\nlive now. Cool. Thanks Lynda! You haven't heard the last of me :)
\r\n#travel
\r\nOnce we got to Segovia, we had a guided tour of the ancient Roman\r\narchitecture, interesting building façades and local legends. It was a very\r\ngood tour, but too hot to really focus. Then they took us to a restaurant for\r\na local speciality. I was all set to write a whole individual blog post\r\nsurveying the barbaric nature of human beings, but I didn't do it straight\r\naway and now the passion has faded slightly, so I'll leave it at a paragraph.\r\nSome people watched the local 'ceremony' out of morbid curiosity I imagine,\r\nbut it was the fact that so many people took so much pleasure in the idea of\r\nviolently hacking up bodies of three-week-old piglets that really bothered me.\r\nFortunately the surging standing crowd allowed me (and only one other) to\r\ninconspicuously sit it out. The veggie option was tasty, but it was difficult\r\nto really enjoy the rest of the evening whilst wondering vaguely about the\r\nstates of minds of most of the people I was sharing a table with.
","as:name":"#SSSW2013: Practical semantics and human nature","as:published":{"@type":"http://www.w3.org/2001/XMLSchema#datetime","@value":"2013-07-10T23:00:00.000Z"},"as:tag":[{"@id":"https://rhiaro.co.uk/tags/done"},{"@id":"blog:Done"},{"@id":"https://rhiaro.co.uk/tags/dbpedia"},{"@id":"https://rhiaro.co.uk/tags/fred"},{"@id":"https://rhiaro.co.uk/tags/harith+alani"},{"@id":"https://rhiaro.co.uk/tags/ontologies"},{"@id":"https://rhiaro.co.uk/tags/phd"},{"@id":"https://rhiaro.co.uk/tags/recommender+systems"},{"@id":"https://rhiaro.co.uk/tags/segovia"},{"@id":"https://rhiaro.co.uk/tags/semantic+web+summer+school"},{"@id":"https://rhiaro.co.uk/tags/semantic+web"},{"@id":"https://rhiaro.co.uk/tags/semantics"},{"@id":"https://rhiaro.co.uk/tags/social+media"},{"@id":"https://rhiaro.co.uk/tags/sssw13"},{"@id":"https://rhiaro.co.uk/tags/sssw2013"},{"@id":"https://rhiaro.co.uk/tags/tipalo"},{"@id":"https://rhiaro.co.uk/tags/tommaso+di+noia"},{"@id":"https://rhiaro.co.uk/tags/travel"}],"as:updated":{"@type":"http://www.w3.org/2001/XMLSchema#datetime","@value":"2013-07-30T17:34:20.984Z"}},{"@id":"https://rhiaro.co.uk/2013/07/sssw2013-social","@type":"as:Article","blog:bloggerid":"tag:blogger.com,1999:blog-18505529.post-4981764477729666207","as:actor":{"@id":"http://www.blogger.com/profile/12227954801080178130"},"as:content":"We started work on the serendipity project before breakfast today, although I\r\ndidn't make it down as early as some of my teammates.
\r\nTo start the day, Fabio Ciravenga talked about some really exciting practical\r\napplications of monitoring and analysing social media streams. It's\r\nparticularly interesting during emergencies, or large events where problems\r\nmight occur. The people on the ground make the perfect sensors if you can\r\nwork out the differences between people who are saying something useful and\r\nwho aren't; people who are really there, and people who are speculating or\r\nasking about the situation. A main problem has been that people tweet crap.\r\nThey were trying to monitor a house fire, but so many people were tweeting\r\nlyrics from Adele's various singles at the time, which all apparently contain\r\nreferences to fire, it was almost impossible.
\r\nThey also put (or tapped into existing) sensors in peoples' cars to monitor\r\ndriving patterns with the aim of more fairly charging for car insurance. I\r\ntold my Mum about this the other day, and she was pretty alarmed by the idea.\r\nWhich made me wonder how they'll get mass adoption, if it's going to go\r\nanywhere.
\r\nFabio did have some interesting things to say about using all this data\r\nethically though, and never working for someone who is going to take that away\r\nfrom you. But in case the 'bad guys' do find out about all this data you have\r\nabout people, keep a magnet handy.
\r\n\r\nThis was followed by a hands-on session where we got to mess with a mini\r\nversion of the twitter topic monitoring system that Fabio's team use at large\r\nevents, to try to answer questions about the Tour de France only by\r\nmanipulating the incoming social media streams and following only links which\r\ncame through that.
\r\nSpanish omelette sandwiches were an amazing outdoor leisurely lunch. We\r\nheaded to the pool down the road and chilled out there for a couple of hours.\r\nUs tough British folk found the water pleasantly tepid, whilst all those wimpy\r\nEuropeans and Latin Americans shivered on the grass. They'd made such a fuss\r\nin advance about how cold the pool was going to be.
\r\nWe regrouped that afternoon to work on Project Cusack, creating a slide deck\r\nof pictures from Serendipity. I don't like slides with too much to read on,\r\nso I enforced this. The imagery from the movie will be lost on most people,\r\nbut we have at least managed to choose pictures of John Cusack with\r\nappropriate expressions for each part of the presentation. We worked outside\r\nin the forest, because Oscar's 3G was faster than the residence wifi.
\r\n\r\nWe also brainstormed for the required short film, which we only just\r\ndiscovered doesn't have to be about our project.
\r\nWe returned to the residence to find everyone eating ham and cheese, and\r\nattempted to get some shots for our film, but other people were unwilling to\r\nparticipate.
\r\nThat evening we ate tasty vegetable soup, weird (in a bad way) pasta in a\r\ncreamy onion sauce, and chocolatey ice cream cake. The tutors spontaneously\r\norganised a game where students had to arrange the tutors by age, which was\r\nfunny. Someone suggested the tutors ought to play it with the students.\r\nObviously there were too many students, but they elected to find the youngest\r\nstudent, and that turned out to be me.
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