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Turn Twitter Into Your Personal Assistant : Page 2

The combination of social networking with public messaging, link posting, and subscriptions leads to impressive synergy effects, but there is also the drawback of information overload, information loss, distraction, and content redundancy.


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Step 4: Create Custom Reports with SPARQL

Fine-grained stream filtering is already helpful in terms of noise reduction and interest tracking. But in order to let Twitter assist you even more, you will also need quick access to custom query results. After all, it's not the post, but the information in the post that enables automation. One option is to simply bookmark query URLs from the SPARQL API. For example, a query to generate a list of your online contacts (not the people in your followers list, but those you really interact with) is not too complicated:

SELECT DISTINCT ?name WHERE { # people mentioned by me ?post sioc:has_creator ?account ; smr:mentionedUser ?name . ?account rdfs:label "your_twitter_username" . # people who mentioned me ?post2 sioc:has_creator ?account2 ; smr:mentionedUser "your_twitter_username" . ?account2 rdfs:label ?name . }

A single SPARQL operation alone might not be enough, though, especially if you want to inject additional data or reformat the results. In this case, you can extend the main page with your own tabs. Open code/smr/SMR_ViewBox.php and add your own entries to the getTabs method. This works similar to extending the filters, but this time you have to create a view class in code/smr/custom/ for each new tab:

function getTabs() { return array( 'all' => array('label' => 'All posts'), 'contacts' => array('label' => 'My Contacts'), 'bookmarks' => array('label' => 'Bookmarks'), ); } function getAllTabHTML() { $tab = Trice::getObject('SMR_Stream_Tab', $this->a, $this->caller); return $tab->getHTML(); } function getContactsTabHTML() { $tab = Trice::getObject('SMR_Custom_ContactsTab', $this->a, $this->caller); return $tab->getHTML(); } function getBookmarksTabHTML() { $tab = Trice::getObject('SMR_Custom_BookmarksTab', $this->a, $this->caller); return $tab->getHTML(); }

 
Figure 5. Custom Tabs with Bookmarks and Proven Contacts: The bookmark query was aligned with the filtering mechanism, so that you can use facets to narrow down the results.
You can also fine-tune the stylesheet information in code/smr/custom/custom.css if you like. Listing 1 contains the complete class for a basic bookmarks tab that orders entries by popularity (see Figure 5).



Other possible use cases include project reports or a birthday reminder. To a certain extent, you can implement the former based simple tags, but the latter requires the ability to detect and extract the month and calendar day of a person's birthday from a micropost.

Step 5: Type Your Tags and Run More Powerful Queries

You can filter your microposts nicely and also create custom reports now. However, as just mentioned, for a personal assistant you will need even more granularity. Technically, this is not too much of a problem as the data model of your RDF-driven application can be freely extended at run-time. The question is rather how to add the functionality to existing Twitter clients. You want to add more structure than provided through tags, but are restricted to a simple input form and the maximum length of 140 characters. The solution proposed in this article is a slightly extended tag syntax, similar to machine tags on Flickr, but for now without the namespace-qualified keys.

You can tweak the regular expressions in the RDFExtractor (code/smr/SMR_RDFExtractor.php) to add support for such typed tags that follow a #key=value pattern. (You actually don't have to write this method yourself, it's already part of the code bundle.) Now you can add something like #birthday #month=08, #todo #priority=A or #done #task="DevX article" #hours=16 to your tweets and let the application extract the classified tags (which use a local my: namespace in the resulting RDF). Create conventions you feel comfortable with and let them evolve.

 
Figure 6. Basic Summary of Working Hours: The report uses SPARQL+ to aggregate working hours and then groups the results by related tags.
Typed tags open the door to very sophisticated and automated reports on top of Twitter, for example, a personal software bug tracker (see Figure 6):

SELECT DISTINCT ?post ?prio ?text WHERE { ?post smr:tag "todo" , "bug" ; my:priority ?prio ; content:encoded ?text . } ORDER BY ASC(?prio)

or a calculator for working hours:

SELECT ?project SUM(?hours) as ?sum WHERE { ?post my:hours ?hours ; my:project ?project . } GROUP BY ?project

or upcoming birthdays:

SELECT ?text WHERE { ?post content:encoded ?text ; smr:tag "birthday" ; my:month "2" ; my:day ?day . FILTER(?day > 10) } ORDER BY ?day

Further Steps

This article demonstrated the basic building blocks and possibilities for accessing and processing semantic Twitter posts. The obvious next step is to create a dedicated microblogging client that keeps selected tweets or tags private. A long-running background script that sends alerts for pre-defined events would also be handy. Another compelling idea is a script that uses SPARQL to combine the simple working hours example with external information to generate a more complete project report; or perhaps a monthly invoice.



Benjamin Nowack focuses on utilizing RDF technology for everyday web development tasks. He runs the semantic web agency semsol (http://semsol.com/) and maintains ARC, an open-source RDF toolkit for LAMP environments.
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