I submitted this paper last week for Assignment #4, INFS 892, Semantic Web Programming. (I’d link to the syllabus and course information, but DSU boxes all that stuff up in boring old D2L. Can anyone say World Wide Web?)

Knowledge management and decision support require tools that can prevent information overload by filtering information based on its quality and its users (Smart et al., 2005). Semantic Web technologies should help in that regard. However, quickly evolving situations, like breaking news, financial information, and battlefield intelligence may challenge the capacity of Semantic Web applications to provide advantages over traditional, organic methods of information filtering. Semantic Web technology, like existing search technology, can certainly empower users, but there appears to be a threshold of newness and immediacy that Semantic Web programmers may not be able to cross.

Two news reports on the role of social media in the current political protests in Iran  got me thinking about this problem. The lead-in report (Gallafent, 2009) noted the usefulness and popularity of Twitter in covering this event: Twitter.com is blocked in Iran, but people can read and write to Twitter through so many devices besides computers that there’s still plenty of access. Reporter Laura Lynch (Werman, 2009) noted that while reporting from Tehran, faced with a government clamping down on the local media, she was relying on Twitter and other online sources, just like other observers and the protesters themselves. Even though Twitter is nigh impossible to source, there is still a wealth of clearly authentic content, especially links to photos and video. One Tweet on #iranelection drew my attention to a photo essay (Taylor, 2009) that I found sufficiently meaningful to blog and link (Heidelberger, 2009). Of course, as readers pause to investigate one such link for a minute, Twitter users may generate dozens if not hundreds of new Tweets about the Iran protests.

Human readers can hardly keep up with such a flood of information, but can the Semantic Web do any better? Lynch said that at her hotel in Tehran, one woman spent the entire day online, sifting through Twitter and other sources and pointing out highlights to Lynch and other reporters (Werman, 2009). Instead of somehow assigning RDF tags to the Twitter posts she found valuable (and really, there’s no way she could tag everything, certainly not the vast majority of Tweets that she found unimportant), she assigned meaning to bits of information by passing them on to her journalist friends. That still doesn’t make the information machine-readable, but it does filter the information and make valuable nuggets available to a larger audience. Perhaps a fast-moving news story like the Iranian protests is a strange reversal of fortune, where things happen too fast for machines to follow and we must turn to human reporters and commentators for the best story. People don’t have time to tweet in RDF. Protesters don’t have time to slap rich tags on their cellphone videos: they’re going to hit the upload button and then run from the baton-wielding riot cops. Formal Semantic Web program seems necessarily after-the-fact, retrospective, and by then, we’re on to the next breaking event.

Other fast-paced information settings may pose similar challenges. On the battlefield, will military intelligence specialists have time to convert their updates into RDF? Or do they better use their analytical powers to immediately analyze and report the data from the battlefield? Or consider the stock market: there is a wealth of easily quantifiable, Semantic-Web programmable information, but if things are moving fast, those tags won’t grab our attention; we will talk to each other, listen to the main commentators we trust, and go from there.

O’Connor et al. (2008) and Kim et al. (2009) both propose Semantic Web application models for real-world situations. O’Connor et al. (2008) discuss a useful application for helping doctors “explore treatment options for HIV-positive patients” by incorporating information from numerous doctors and past medical experiences. Kim et al. (2009) design a decision-support method for online purchases that would gather a variety of business information from different sources. These solutions do not offer a complete guide to dealing with real-time, evolving situations: both are relatively structured problems with clearly defined variables. In seeking important news about political protests or compiling and analyzing battlefield intelligence, we may be dealing with new actors, new variables that do not exist yet in any ontology. Real-time Semantic Web applications need somehow to deal with this possibility.

Some researchers are tackling the challenge of real-time Semantic Web applications. Blogging, with its use of tagging and RSS feeds, already produces a great deal of the metadata the Semantic Web relies on (Karger and Quan, 2005). A semantic blogging platform like Haystack (Karger and Quan, 2005) supports user creation of machine-readable semantic data with specialized forms. Such forms are immensely more workable for the majority of online content producers than raw coding of RDF (just as blogs, Twitter, and Facebook are much more accessible to users than HTML). However, even those forms add complication that may be unworkable for journalists (both professional and citizen) who are uploading blog posts, videos, and tweets on breaking news. Such forms may be helpful in a collaborative format, where eyewitnesses can use a simple fire-and-forget publishing interface while secondary users can access and edit the same content through semantic forms that allow them to annotate the original content with metadata that Semantic Web applications can manipulate.

Smart et al. (2005) explore the use of Semantic Web technology to support situational awareness in the complex interaction of military and humanitarian organizations. This particular challenge goes beyond the conventional question of battlefield awareness: where an army must maintain its own secure, unified intelligence system, a humanitarian intervention requires diverse governmental and non-governmental agencies to be able to communicate. Their model assumes prior knowledge of  well-defined agents and capabilities and their relationships; such a model may be difficult to apply in a setting like the Iranian protests where the “agents”—protest leaders, online coordinators, citizen journalists—may not be well-defined in any existing ontology.

Addressing the need to ontologies to evolve and capture information from diverse sources, Köhler et al. (2006) build a model that allows reasonably easy combination and updating of ontologies. However, their content-based indexing seems limited to text that exists in sufficient context. Content like tweets and brief blog posts and links might defy their model by not providing enough surrounding text to establish the full verbal context their model requires. Scharl et al. (2008) propose a framework that would allow distributed users to collaborate in updating ontologies. Karger and Schraefel (2006) might challenge that model given its reliance on ontology visualizations, which Karger and Schraefel suggest are not the most effective representations of knowledge. Whatever form we may use to represent ontologies, perhaps a more important question is whether collaborative ontology construction adds usefully and efficiently to knowledge management in a swiftly evolving situation where numerous individuals are creating and seeking information. It may be that formal efforts at Semantic Web programming in such situations produce only marginal improvements over meaning-making of busy journalists and the viral spread of information fostered by spontaneous, organic collective judgments.

Photos and video from the streets of Tehran are not searchable the way text is. Someone has to tag the video, embed it in a blog post with commentary, Digg it, etc., to get it into either conventional or semantic search results. Whether that video has meaning, and what sort of meaning it has for understanding what is happening in Iran, depend on the continuing, evolving aggregate judgment of the masses, readers like me, clicking, annotating, and forwarding that content. We may capture that meaning just as effectively through human means as through Semantic Web applications. And even if we can apply Semantic Web technologies to make quickly evolving situations more quickly comprehensible to decision-makers and outside observers, the coding and ontologies we apply to that video must remain open to reinterpretation and reassignment by the users to capture new circumstances and understandings.


Gallafent, A. (2009, June 17). Twitter’s role in Iran protests. The World. Public Radio International. Retrieved June 19, 2009, from http://www.theworld.org/?q=node/26972.

Heidelberger, C.A. (2009, June 15). Courage in the streets of Iran. Madville Times. Retrieved June 19, 2009, from http://madvilletimes.blogspot.com/2009/06/courage-in-streets-of-tehran.html.

Karger, D. R., & Quan, D. (2005). What would it mean to blog on the Semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web, 3(2-3), 147-157. Retrieved June 16, 2009, from http://www.cs.uga.edu/~pdoshi/Courses/CSCI%204900_6900/KargerBlogISWC04.pdf.

Karger, D., & Schraefel, M. C. (2006). The pathetic fallacy of RDF. Retrieved June 20, 2009, from http://swui.semanticweb.org/swui06/papers/Karger/Pathetic_Fallacy.html.

Kim, H.‐J., Kim, W., & Lee, M. (2009). Semantic Web Constraint Language and its application to an intelligent shopping agent. Decision Support Systems, 46(4), 882‐894.

Köhler, J., Philippi, S., Specht, M., & Rüegg, A. (2006). Ontology based text indexing and querying for the semantic web. Knowledge-Based Systems, 19(8), 744‐754.

O’Connor, M. J., Shankar, R. D., Tu, S. W., Nyulas, C. I., & Das, A. K. (2008). Developing a Web‐Based Application using OWL and SWRL. Paper presented at the AAAI Spring Symposium.

Scharl, A., Weichselbraun, A., & Wohlgenannt, G. (2008). A web-based user interaction framework for collaboratively building and validating ontologies. In Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems (pp. 244-247). Porto Alegre, RS, Brazil: Sociedade Brasileira de Computação. Retrieved June 20, 2009, from http://portal.acm.org/citation.cfm?id=1497470.1497498.

Smart, P. R., Shadbolt, N. R., Carr, L. A., & Schraefel, M. C. (2005). Knowledge-based information fusion for improved situational awareness. In Information Fusion, 2005 8th International Conference on (Vol. 2, p. 8 pp.). doi: 10.1109/ICIF.2005.1591969.

Taylor, A. (2009, June 15). Iran’s disputed election. The Big Picture: News Stories in Photographs. Boston.com. Retrieved June 19, 2009, from http://www.boston.com/bigpicture/2009/06/irans_disputed_election.html.

Werman, M. (2009, June 17). Deciphering the messages from Iran: Interview with Laura Lynch and Azadeh Moaveni. The World. Public Radio International. Retrieved June 19, 2009, from http://www.theworld.org/?q=node/26973.