Happy New Year everyone!
2011 was a slow year for this blog. Early last year I joined Erik Meijers team in order to work on an incubation project that involved actors, distributed graph-based processing, a highly-scalable and reliable document store, and more. Given the non-public nature of the project, I couldn’t really talk about it. It was a lot of fun and we learnt a lot. The nice folks in the team are continuing the work but I decided to move on, following my dream to work on knowledge representation and reasoning at scale. Unfortunately more months of work-related silence are going to follow.
I’ve already talked (on this blog, in public presentations, and in articles) about the opportunities and challenges associated with the data-information-knowledge spectrum. Semantic Computing is at the age of reason and I believe that 2012 will represent its big breakthrough into commercial applications and experiences.
As I’ve been talking with others about the knowledge representation space, I observed how the terms information and knowledge are often misused. As I expect to be writing more about information and knowledge in the coming months, I felt like sharing few words on the subject.
Information vs Knowledge: I find that the two terms are often used interchangeable. While an authoritative definition doesn’t exist, Bellinger, Castro, and Mills offer an informative description in their article on Data, Information, Knowledge, and Wisdom.
Data is symbols (bits, numbers, characters). Information adds meaning to data through the introduction of relationships. It answers questions such as who, what, where, and when. Knowledge is a description of how the world works. It’s the application of data and information in order to answer how questions.
As an example… while “Savas likes Coldplay” and “Coldplay is a band” represent information facts, the statement “for each person X there exists a female person that gave birth to X” makes an assertion about a truth in the world, about how we perceive the world. Also, “Savas is an adult” is an inferred statement from our general understanding (i.e. knowledge) that “for each person X with Age(X) > 18, X is considered an adult” when it is combined with the information fact that “Savas is 38 years old”. Of course, one might argue that the inference about Savas’ adultness is erroneous because we haven’t accurately described the world in which Savas acts like a teenager on many occasions :-)
The above is just part of the basics of course. Once we incorporate temporal and probabilistic reasoning things get a lot more interesting :-)
I predict that 2012 is going to be the year where parts of Vannevar Bush’s vision are going to start becoming a reality. Experiences such as Siri are leading the way in incorporating natural language understanding in general-purpose computing. However, I believe that Siri is only scratching the surface of what we can achieve today. 2012 is going to be very exciting :-)
As always, feedback/recommendations are always welcomed!
If you are interested in this space, here are the books I recommend:
- R. Brachman and H. Levesque, Knowledge Representation and Reasoning, vol. 1, no. 1. Morgan Kaufmann, 2004, p. 381
- (I am still going through this one) S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Prentice Hall; 3rd edition, 2009, p. 1152
- J. F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing Co., 2000