Knoesis-Semantic filtering-Tutorials
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Semantic Filtering as an example of Semantic technologies for real-time analysis. This presentation emphasizes the value of semantics for social data filtering, specifically for the challenges faced during dynamically evolving event analysis.
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Knoesis-Semantic filtering-Tutorials
- 1. Semantic Filtering An example of Semantic technologies for real-time analysis Pavan Kapanipathi Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) Wright State University, USA Tutorial @ Kno.e.sis Centre: Semantics Approach to Big Data and Event Processing, Oct 7-9, 2015
- 2. Streams are everywhere Social Data Text Images Videos Sensor Data Streams
- 3. Information Overload 500M users generate 500M tweets per day 3 It’s not information overload. It’s filter failure -- Clay Shirky
- 4. Each of our projects face Information Overload • Disaster Management • Hazards SEES • Healthcare Issues • Depression • Societal Issues • Edrug Trends • Harassment
- 5. • Filtering is necessary • Understanding the requirements and utilizing semantics for filtering is important Semantic Filtering
- 6. Two Main Topics • Twarql • Streaming annotation and flexible querying on Twitter • Continuous Semantics • Tracking dynamic topics on Twitter
- 7. Twarql Tracking health care debate in the United States on Social Media Health Care Reform Health Care Reform Healthcare reform legislation in the United States Patient Protection and Affordable Care Act (Obamacare) Health Care Reform
- 8. Twarql
- 9. Extraction Pipeline - Tweet I think it’s good deal Apple Ipad Tablet (3G, wifi, WiFi + 3G) Hard Nylon Cube Carrying Case for ipad ( iPad.. http://bit.ly/cry6LF) Dbpedia:Ipad Dbpedia:Tablet URLs http://penguinkang.com/tweetprobe/
- 10. RDF • RDF Annotation • Common RDF/OWL Data formats. • FOAF, SIOC, OPO, MOAT
- 11. : Health_care_reform Twarql – Use Case
- 14. Dynamic Topics Continuously Evolving on Twitter Entity – Event relevance changes Many entities are involved 14
- 15. Dynamic Topics Manually crawl using keywords “indianelection”“jan25” “sandy” “swineflu” “ebola” 15
- 16. Dynamic Topics Manually updating keywords to get topic relevant tweets is not feasible “indianelection” “modi” “bjp” “congress” “jan25” “egypt” “tunisia” “arabspring” “sandy” “newyork” “redcross” “fema” “swineflu” “ebola” 16
- 17. Problem How can we automatically update the filters to track a dynamically evolving topic on Twitter 17
- 18. Hashtags as Filters • Identify a topic on Twitter • Tweets with hashtags are more informative • Users have a lot of freedom to create them • Some get popular, most die 18
- 19. Exploring Hashtags as Evolving Filters for Dynamic Topics Colorado Shooting Occupy Wall Street CS OWS Tweets: 122,062 Tweets: 6,077,378 Tags: 192,512 Distinct: 12,350 100% Retrieval: 7,763 Tags: 15,963,209 Distinct: 191,602 100% Retrieval: 21,314 HASHTAG FILTERS 19
- 20. Top 1% retrieves around 85% of the tweets Hashtag distributions 20
- 21. Colorado Shooting Occupy Wall Street Event Related Hashtags co-occur with each other Hashtag Filters Co-occurrence Graph 21
- 22. Summarizing Hashtag Analysis Starting with one of the event relevant hashtags, by co-occurrence we can reach other relevant hashtags 22
- 23. Determining Relevancy of Co-occurring Hashtags #indianelection2015 #modikisarkar Too many co-occurring hashtags 23
- 24. Determining Relevancy of Co-occurring Hashtags #indianelection2015 #modikisarkar Co-occurring: Threshold δ Preferably a prominent hashtag 24
- 25. Hashtag Co-occurrence works? o No. Just co-occurrence does not work o Many noisy or unrelated hashtags co-occurs o Determine the “dynamic” relevance of the top co-occurring hashtag with the dynamic topic 25
- 26. Determining Relevancy of Co-occurring Hashtags #indianelection2015 #modikisarkar Co-occurring: Threshold Latest K (200,500) Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2 Entity Extraction and Scoring δ Normalized Frequency Scoring 26 (Vector Space Model)
- 27. Determining Relevancy of Co-occurring Hashtags (Vector Space Model) #indianelection2015 #modikisarkar Co-occurring: Threshold Latest K (200,500) Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2 Entity Extraction and Scoring Indian General Election,_2014 Dynamically Updated Background Knowledge δ 27
- 28. Event Relevant Background Knowledge o Wikipedia Event Pages 28
- 29. o Wikipedia Event Pages Event Relevant Background Knowledge 29
- 30. o Entities mentioned on the Event page of Wikipedia are relevant to the Event Event Relevant Background Knowledge 30
- 31. o Wikipedia’s Hyperlink structure is very rich o Page-Page (Wikipedia) links Indian General Election, 2014 Narendra Modi Rahul Gandhi NDA (India)UPA (India) BJP Indian National Congress Event Relevant Background Knowledge – Graph Structure 31
- 32. Determining Relevancy of Co-occurring Hashtags (Vector Space Model) #indianelection2015 #modikisarkar Co-occurring: Threshold Latest K (200,500) Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2 Entity Extraction and Scoring Indian General Election,_2014 Extract, Periodically Update Hyperlink structure One hop from Event Page δ 32
- 33. o Hyperlink structure is dynamically updated Indian General Election, 2014 Narendra Modi Rahul Gandhi NDA (India)UPA (India) BJP Indian National Congress 10 May 2010 Event Relevant Background Knowledge 33
- 34. o Hyperlink structure is dynamically updated Indian General Election, 2014 Narendra Modi Rahul Gandhi NDA (India)UPA (India) BJP Indian National Congress 10 May 2010 29 March 2013 29 March 2013 29 March 2013 29 March 2013 Event Relevant Background Knowledge 34
- 35. o Hyperlink structure is dynamically updated Indian General Election, 2014 Narendra Modi Rahul Gandhi NDA (India)UPA (India) BJP Indian National Congress 10 May 2010 29 March 2013 29 March 2013 29 March 2013 29 March 2013 20 May 2013 20 May 2013 Event Relevant Background Knowledge 35
- 36. Determining Relevancy of Co-occurring Hashtags (Vector Space Model) #indianelection2015 #modikisarkar Co-occurring: Threshold Latest K (200,500) Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2 Entity Extraction and Scoring Indian General Election,_2014 Extract, Periodically Update Hyperlink structure Entity scoring based on relevance to the Event One hop from Event Page δ 36
- 37. o Edge Based Measure o Link Overlap Measure: Jaccard similarity o Out(c) are the links in Wikipedia page “c” o Final Score: r(c,E) = ed(c,E) + oco(c,E) Hyperlink Entity Scoring India General Election, 2014 Narendra Modi India General Election, 2014 India General Election, 2009 1 Mutually Important ed (c,E) = 1 ed (c,E) = 2 37
- 38. Determining Relevancy of Co-occurring Hashtags (Vector Space Model) #indianelection2015 #modikisarkar Co-occurring: Threshold Latest K (200,500) Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2 Entity Extraction and Scoring Indian General Election,_2014 Extract, Periodically Update Hyperlink structure Entity scoring based on relevance to the Event One hop from Event Page Indian General Elec: 1.0 India: 0.9 Elections: 0.7 UPA: 0.6 BJP: 0.3 NDA: 0.3 Narendra Modi: 0.3 δ 38
- 39. Determining Relevancy of Co-occurring Hashtags (Vector Space Model) #indianelection2015 #modikisarkar Co-occurring: Threshold Latest K (200,500) Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2 Entity Extraction and Scoring Indian General Election,_2014 Extract, Periodically Update Hyperlink structure Entity scoring based on relevance to the Event One hop from Event Page Indian General Elec: 1.0 India: 0.9 Elections: 0.7 UPA: 0.6 BJP: 0.3 NDA: 0.3 Narendra Modi: 0.3 Similarity Check Relevance Score: 0.6 δ 39
- 40. o Set Based o Jaccard Similarity o Considers the entities without the scores o Vector Based o Symmetric o Cosine Similarity o Asymmetric o Subsumption Similarity Similarity Check 40
- 41. India General Election 2014 Narendra Modi Intuition behind Asymmetric India General Election 2014 Narendra Modi Penalized Ignored Similarity Symmetric Asymmetric 41
- 42. Determining Relevancy of Co-occurring Hashtags (Vector Space Model) #indianelection2015 #modikisarkar Co-occurring: Threshold Latest K (200,500) Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2 Entity Extraction and Scoring Indian General Election,_2014 Extract, Periodically Update Hyperlink structure Entity scoring based on relevance to the Event One hop from Event Page Indian General Elec: 1.0 India: 0.9 Elections: 0.7 UPA: 0.6 BJP: 0.3 NDA: 0.3 Narendra Modi: 0.3 Similarity Check Relevance Score: 0.6 δ 42
- 43. o 2 events o US Presidential Elections (#election2012) o Hurricane Sandy (#sandy) o Top 25 co-occurring hashtags Evaluation – Dataset 43
- 44. o Ranking Problem o Rank the Top 25 hashtags based on the relevancy of tweets to the event o Experiment with all the similarity metrics o Manually annotated the tweets of these hashtags as relevant/irrelevant (Gold Standard) o Ranking Evaluation Metrics o Mean Average Precision o NDCG Evaluation – Strategy 44
- 45. Evaluation 45
- 46. Evaluation Evaluated tweets comprising of top-relevant hashtags detected for dynamic topics • NDCG - 92% at top-5 Mean Average Precision 46
- 47. Conclusions • Semantic Technologies for Real-time filtering of Social Data – Wikipedia as a Dynamic Knowledge base for events – Determining relevant hashtags using Asymmetric similarity measure – More hashtags in turn increase the coverage of Tweets for events • Hashtag Analysis – Co-occurrence technique can be used to detect event relevant hashtags – More popular hashtags are easier to be detected via co- occurrence 47
Notas del editor
- Streams are everywhere. I am sure by now manualle, Dr. Sheth would have convinced you about this.
- In this presentation, we will focus on the social data streams
- So the journalist needs to track and he opts for some keyphrase healtcare reform. What would happen if we include semantics
- These examples such as epidemics, natural disasters, political events, and civil unrest are dynamic events.
- They are continuously evolving. They involve many other entities. for example during indian elections Modi, Rahul Gandhi, Congress, and BJP related to the event. And in many cases these entity-event relevance changes over time. For example, Considering hurricane sandy NYC was a part of it for 2 days.
- A naïve approach to get tweets relevant to an event is to use keywords as filters such as these. Twitter’s streaming API allows upto 400 keywords (unpaid) as filters.
- However we need to update these keywords as and when the topics evolve. This technique is very tedious and not feasible.
- We focus on this problem where we need to automatically update the filters
- We focus on this problem where we need to automatically update the filters
- The number of tweets collected for colorado shooting are around 125,000 where as OWC we collected approximately 6M tweets. Total number of distinct hashtags found in these tweets are 12K and 191k. In order to retrieve all the tweets of the event we need 7k for CS and 21k tags for OWS. In other words, if we had to automatically update all the filters, that are hashtags, we would need 7k hashtags to get all the tweets of Colorado Shooting and 21k tweets for OWS. It is important to note that we are just crawling for tweets with hashtags for now.
- And the top 1% of these hashtags retrieves around 85% of the tweets. So practically speaking, In order to retrieve the tweets, we need to find an approach to automatically reach these top event related hashtag on the go.
- This is how the co-occurrence graph of
- From these analysis we get to know that A small percentage of the hashtags should be detected to retrieve most tweets of the event These tweets can be reached via co-occurrence If we start with a popular event related hashtag we can reach other popular ones quickly due to its clustering co-efficient
- If we use co-occurrence as the primary strategy, we will reach a lot of hashtags as filters.
- Now coming back to the approach. The input is a hashtag that is relevant to the event, this is manually added and we hope that this is a prominent hashtag. Using a threshold delta we get the other relevant hashtag
- Hence we start with a manually added event relevant hashtag and periodicall determine the dynamic relevance of top co-occurring hashtag
- The latest 500 tweets of the hashtags are considered and entities from these tweets are scored based on its normalized frequency. Hence the hashtag is represented using a vector of entities. These entities just build a semantic context for the hashtag.
- Next we utilize the wikipedia page of the event and extract all the relevant entities to the event. The relevant entities are the ones present in the Wikipedia page.
- There are event wikipedia pages.
- Arab Springs
- The links to other wikipedia pages form a rich source of infomration. These can be the relevant semi structured knowledge for the event. For example, UPA, NDA, BJP, RG and NaMo are the entities mentioned in the event wikiipedia page and are relevant to the event.
- A graph structure can be created with all the entities on the Topic Wikipedia page. This is a subgraph of Indian General Election 2014, with links between entities of the event. Narendra Modi and Indian National Congress
- It is also important that the knowledge base has to be dynamically updated based on the changes in the event.
- Also, its dynamically updated
- Since it is crowd sourced pages on Wikipedia are updated in near real time. we are expecting the knowledge to be
- We score these entities based on its relevance to the event – Compare vector of entities of the event with thtat of the hashtag
- We use three different types of semantic similarity measures. (1) Set based, (2) Symmetric Vector Based, (3) Assymmetric Similarity
- Penalize to check how big subset is the hashtag compared to the event. #modisarkar cannot cover the whole of indian elections where as the vice versa is possible. Better way to explain this. What we want to penalize
- To evaluate, we picked two other events. US Presidential Elections and Hurricane Sandy. We picked #election2012 and #sandy as the starting point for the crawl. We retrieved 5000 tweets in real-time and extracted the top 25 co-occurring hashtags. For each of these hashtags we pick the latest 200 tweets for analysis.
- We rank the top-25 hashtags based on its relevance to the event. Hence, we transformed it to be a ranking problem using all the similarity measures. For evaluation, all the tweets of the 25 hashtags were manually evaluated to be relevant/irrelevant to the event.
- The subsumtion similarity works the best, where as if we look at just the co-occurrence of hashtags as a ranking mechanism it performs pretty low compared to that of using Wikipedia as knowledge base.
- Interesting to hear the findings. More about the results.
- This presentation emphasizes the value of semantics for social data filtering, specifically for the challenges faced during dynamically evolving event analysis.