Knoesis-Semantic filtering-Tutorials

Editor's Notes

• #3: Streams are everywhere. I am sure by now manualle, Dr. Sheth would have convinced you about this.
• #4: In this presentation, we will focus on the social data streams
• #8: So the journalist needs to track and he opts for some keyphrase healtcare reform. What would happen if we include semantics
• #14: These examples such as epidemics, natural disasters, political events, and civil unrest are dynamic events.
• #15: 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.
• #16: 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.
• #17: However we need to update these keywords as and when the topics evolve. This technique is very tedious and not feasible.
• #18: We focus on this problem where we need to automatically update the filters
• #19: We focus on this problem where we need to automatically update the filters
• #20: 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.
• #21: 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.
• #22: This is how the co-occurrence graph of
• #23: 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
• #24: If we use co-occurrence as the primary strategy, we will reach a lot of hashtags as filters.
• #25: 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
• #26: Hence we start with a manually added event relevant hashtag and periodicall determine the dynamic relevance of top co-occurring hashtag
• #27: 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.
• #28: 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.
• #29: There are event wikipedia pages.
• #30: Arab Springs
• #31: 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.
• #32: 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
• #33: It is also important that the knowledge base has to be dynamically updated based on the changes in the event.
• #34: Also, its dynamically updated
• #35: Since it is crowd sourced pages on Wikipedia are updated in near real time. we are expecting the knowledge to be
• #37: We score these entities based on its relevance to the event – Compare vector of entities of the event with thtat of the hashtag
• #41: We use three different types of semantic similarity measures. (1) Set based, (2) Symmetric Vector Based, (3) Assymmetric Similarity
• #42: 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
• #44: 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.
• #45: 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.
• #46: 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.
• #47: Interesting to hear the findings. More about the results.
• #49: This presentation emphasizes the value of semantics for social data filtering, specifically for the challenges faced during dynamically evolving event analysis.