Knowledge Soup for the Soul

Thinking about how digital texts fit into the bigger picture of us building richly interconnected informational spaces to embed knowledge in them, I ended up before a knowledge soup.

Knowledge Representation and a Soup

It is in The Challenge of Knowledge Soup where computer scientist John F. Sowa writes about knowledge and how difficult it is to represent it in a language with which computers would be able to reason – that is to solve complex tasks, figuring out what something “means” and how to act upon this “understanding” included.

Robots can assemble precisely machined parts with far greater accuracy than any human, but no robot can build a bird nest from scattered twigs and straw or wash irregularly shaped pots, pans, and dishes the way people do.

It appears computers have limitless power when it comes to calculating and processing information, but when it comes to building a “nest” from the irregularly shaped, ambiguous and dynamic “strings” of human language (and texts), they might be rather clueless. Actually we are sometimes clueless how to give them cues. As Sowa writes, our “natural desire to organize, classify, label, and define things” always meets the “inevitable change, growth, innovation, progress, evolution, diversity, and entropy.“

But then again, in all that mess, despite the big seemingly insurmountable hurdle of organizing and formalizing knowledge we still need to get some pizza, to, say be able to get through a sleepless night of researching Wittgenstein’s words and concepts. And we need to figure out how the knowledge we embed in our texts is to be represented to the algorithms which mediate that knowledge.

At least with pizza, the agreed upon shared understanding of what is what seems to work! Pizza is pizza, working hours are working ours, the location of the restaurant is a clearly defined address. No “irregularly shaped pots, pans” here. The system that searches pizza on our behalf on the Web, would “know” what to show us in terms of location and opening hours that fit our needs. And this is not some kind of a magic. It is all machine-readable rules and definitions. It is structured data. For a very nice explanation of what structured data are and why they matter, check Structured data: Where did that come from & why are Google asking for it by Richard Wallis to know more.

Of course, for the more complex things, such as our research about Witgenstein, there’s lots of work and thinking ahead. But there too, things are off to a good start: check Witgenstieniada. [caveat: a bit mind-boggling]

And before I go on, let me explain the quotation marks in the verb know I used above, with an excerpt from the book Knowledge Representation & Reasoning by Brachman & Levesque (p. 5):

We might think of a thermostat, to take the classic example, as “knowing” that the room is too cold and “wanting to warm it up. But this type of anthropomorphization is typically inappropriate: there is a perfectly workable electrical account of what is going on. […] Moreover it can be quite misleading to describe an AI system in intentional terms: using this kind of vocabulary, we could end up fooling ourselves into thinking that we are dealing with something much more sophisticated than it actually is.

Digital Text and Knowledge

Somewhere in-between the need to categorize, the impossibility to formalize our fluid language and the attempts to make computers make sense of our messy world. there lie today’s digital text – a blend of code, culture and incessant connectivity.

Looking to understand the strands digital text on the Web is woven of is the reason I even dared to enter the vast scary field of knowledge representation. For without knowledge representation and the related tools and technologies, no matter how big and rich, our digital texts are it would be impossible to find and use them.

Also, I believe that understood in the light of knowledge representation, any text on the web – be that the digitized version of Homer’s Iliad on the amazing Perseus Digital Library Project, or the newly written web copy with a pizza description –  will help us navigate the Web (and the world) a little bit more in the know.

When the Knowledge Soup Became a Knowledge Soup for the Soul…

It could be that the common thread between digital text, web writing and knowledge representation lies somewhere in the space between data and dialogues.

With that assumption having emerged, the tough task of thinking about knowledge, the electronic word on the Web and meaning became a little bit less intimidating and a lot more inspiring. It became a talk about empowering human connection.

Dialogues have the power to free words and concepts, to let the latter dance with and around meaning, in a quest for the essence. It is in the exchange of perspectives and views that we search for and find understanding.

The electronic word – be it our good old beautiful container of thought or those strange strings we try to explain to computers – will feed these spaces where we mix, meet, collaborate and know (no quotation marks this time) together. Spaces that would increasingly become machine-readable and with that highly connected and mediated by smarter sets of rules (algorithms), which will only add to the power of dialogues.

The Real Human Semantics and a Call for Dialogic Digital Tools

“our brains do much more than solve differential equations.”

Cit. Digital Cultures by Esko Kilpi

In his article on Digital Cultures, Esko Kilpi talks about the importance (and the vital force) of Dialogues and the importance of keeping the paradoxes alive. Here’s a beautiful quote:

The next digital tools dealing with intelligence need to be more “dialogic”. The concept of dialogue has a very precise meaning. It is a discussion which does not resolve itself by finding common ground. Though no shared agreements are reached, people often become more aware of their own views and learn through expanding their understanding of one another and the different contexts of different people. We become more intelligent if the paradoxes are kept alive.

Kilpi’s article I read with J. Sowa’s Knowledge Soup in mind and out of these two, a perspective for web writing was born.

The binary and smooth I borrowed from Lora Aroyo and her wonderful presentation Data Science with Humans in the Loop.

Knowledge’s dynamic structure does need textual fabrics and dialogues trying to reach common understanding. Web writing can feed that purpose of transferring knowledge and enacting knowledge in a beautiful way.

Serving that purpose, with a knowledge soup all over the place, it helps to keep in mind that it is all about sharing ways of knowing and doing with an open-world assumption in mind.

The Web, in contrast, does not try to define a whole system, just one page at a time. Every page can link to every other. In like fashion, the Semantic Web will allow different sites to have their own definition of a “car”. it can do this because the inference layer will allow machines to link definitions. This allows us to drop the requirement that two people have the same rigid idea of what something “is”.

cit. Weaving the Web, Tim Berners-Lee, p. 186

Epilogue

We never step into the same river (knowledge soup) twice. Neither we enter any informational flow of bits and bytes the same. The nature of language, and the nature of the thing it carries – knowledge, are dynamic, as everything. And it is up to us, as web writers (and web weavers) to embed this understanding into our texts and with every written piece on the Web become yet another step on our shared quest for knowledge, word by word, link by link.