Post-semantic web enhances society and the meaning of data

David Gutelius — Sat, 24 Nov 2007 16:57:31 -0800

Description

In 1999, Tim Berners-Lee first described the semantic web in this way: " I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize."

Since that time, there has been significant progress towards making such an idea reality (note Radar Networks' Twine, or Metaweb's Freebase). It has also become more tightly constrained and defined (e.g. Wikipedia's current definition: "The Semantic Web is an evolving extension of the World Wide Web in which web content can be expressed not only in natural language, but also in a format that can be read and used by software agents, thus permitting them to find, share and integrate information more easily.").

Going beyond RDF-related technologies OWL and other ontology frameworks, however, we may be approaching a post-semantic web phase of development of the Internet. It's not that the "semantic web" as Tim B-L dreamed it or Wikipedia defines has really fully appeared. In fact, I have a suspicion that in either case, it may never appear and function the way its proponents envision. For one, there is still deep disagreement over standards - for all its Sematicness, the community can't even agree on the semantics!

By post-semantic web, I do not mean that it has become irrelevant - but it is beginning to show signs of turning out far differently than anyone could have imagined.

We are now seeing advanced machine learning combined with natural language processing, social graph analysis, and data mining techniques that half a decade ago few could have imagined. These technologies are being put to use by incredibly powerful compute resources (particularly those in mesh or p2p networks) to pick up and analyze a tremendous array of "signals". By signals, I mean not just those most in vogue in "web 2.0" like tags or networks of friends, although these are new and valuable sources for machines to learn to serve people more effectively. I also mean "digital gestures" - small signals that convey meaning to others but differently than "natural language" typically conveys; examples might include symbology or avatars. We are becoming more expressive digitally, and we are now just beginning to be able to also harvest these expressions and have machines learn from them in order to adapt to us.

The artificial intelligence field has for many years been fascinated with the idea of autonomous agents - semi-stupid digital servants that can act on our behalf under certain circumstances. The recent push into probabilistic reasoning and advances in a particular subfield of AI called machine learning (a characteristically poor name for a field of inquiry, but oh well) has begun to produce something better than semi-stupid in terms of serving us users.

The promise of a post-semantic web goes beyond just a language and representation framework (the techno-wonk vision of the semantic web) or a series of agents that do things for people. It's really a combination of 1) the power of distributed computing, 2) the growing expressivity of digital life and the signals such a life leaves behind, and 3) a way for software to learn and adapt itself to serve users and the human communities that they belong to, better. The implications for such powerful applications are not that they necessarily do things for us (although that would be a useful side effect), but rather give us new cognitive, and perhaps social, capabilities that let us do what we humans already do - just more and better.

AAAI Symposium on Social Information Processing - http://www.aaai.org/Symposia/Spring/sss08symposia.php#ss06

iLink KDD - http://www.ai.sri.com/pub_list/1523

Radar Networks' Twine: http://www.radarnetworks.com/

Metaweb - http://www.metaweb.com/

Signals

Radar Networks: Twine

Machine-to-Machine Intelligence (m2mi) Corporation

Social Information Processing

The Rise of Proactive and Context-Aware Computing

Alex Soojung-Kim Pang — Tue, 23 Oct 2007 11:10:30 -0700

Description

Proactive and context-aware computer systems that anticipate users' needs and perform tasks in a timely and context-sensitive manner may begin to have an impact within the next 10 years.

By 2015 computer users could be moving away from foreground computing where our devices and networks wait patiently and passively for our typed instructions or mouse clicks, toward new ways of interacting with computers in which delegated systems perform tasks proactively on users’ behalf, tuned precisely to the momentary requirements of time and place. A range of complex automated tasks could be performed proactively if a mobile computing unit were able to sense a person, application, or device's context. Examples include:

* Search a smart calendar and itinerary for available times and destinations
* Preconfigure account logins and identities
* Set appointments with colleagues
* Search and summarise background data for use in transit and at probable destinations, including proactively searching for and caching background data for meetings with people
* Distribute documents and meeting work products

Context-aware systems will use software applications that understand our human context or our devices’ context. Micro-local information could be cached on embedded wireless micro servers. Geo-coded information (tagged with latitude, longitude, and altitude or height) and invisible digital map data could be accessible and actionable. Data about the functions, activities, and infrastructure of a place, along with its history, culture, politics, and economics could become selectively visible and useful online in information-saturated spaces.

Smart programs -- such as contextually enhanced calendars and to-do lists -- running on our smart wearable and persistent systems will negotiate with embedded sensor nets and pervasive net servers, performing delegated tasks on our behalf in a manner appropriate to the context. Mobile devices could be able to sense the signal environment -- the signal strength and the location of transmitters at different frequencies, including wide area mobile networks and wireless local area networks -- and then, on the next anticipated visit, preconfigure network signalling with programmable signal processors and software-defined radios.

Change may be gradual as many small component systems become available one-by-one. But over time, contextual computational processes will enhance personal and group productivity by proactively executing time-consuming processes to meet human requirements.

Implications:
* Enhanced personal and group productivity

Early Indicators:
* Increasing personal use of computerized calendars, to-do lists, and itineraries
* Delivery of low-key targeted ads in the context of each Google search
* Development of a simple thermostat that adjusts air conditioners in the context of human and energy requirements for each time and place in a building
* Development of demonstrations and prototypes, such as the toolkit for context-aware applications being built by Anind K. Dey at the University of California, Berkeley

What to Watch:
* Demonstrations of prototypes of context-aware applications at annual conferences such as the International Conference on Ubiquitous Computing and the International and Interdisciplinary Conference on Modeling and Using Context improve in robustness and credibility as services and applications become practical for popular usage.
* Convergence of technologies (eg "Tom-Tom"-like navigation aids) into mobile phones/hand-held devices.

Parallels/Precedents:
* Xerox's PARCTAB

Enablers/drivers:
* Continued research and converging developments in related disciplines such as semantic processes, mobile communications, and embedded computers and sensor nets

semantic processing

Post-semantic web enhances society and the meaning of data

Description

Signals

The Rise of Proactive and Context-Aware Computing

Description

Signals