If you think about it in terms of a hypothetical ontology, you’d certainly expect to find more semantic structure in the first than in the second.

Ironically, there’s a lot of confusion though around what the term “meta-data” means though.

Thanks again for the discussion!

I certainly don’t think the idea of self-referential vs. meta-data search is the most important element of search topology, but I think it plays a greater role than the notion of unstructured vs. structured data. Whether you are building a search engine or a recommendation engine, I believe this issue remains important.

Let me try to clarify my thoughts…

There are many objects that don’t lend themselves to self-referential search. Objects like movies, songs, buildings, and pottery, for example. For these types of objects there is both structured meta data (ratings, genres, actors, size, shape,…) and unstructured meta data (reviews, comments, descriptions, rants,…). Analysis of this meta data is vital to any type of search for these objects.

At Netflix, they use structured meta data for their search engine (genres, ratings, actors, directors, etc.) and for their recommendation engine (ratings).

At Nanocrowd, we work strictly with unstructured commentary for our analysis. Based on that unstructured meta data, we create new meta data (nanogenres, ratings, most-like objects, and nutshells). So far we have primarily used this data as a recommendation engine, but we envision tools that will help you to predict if you will like an object or to search for one based on actor, director, words, etc.

Of course, people have already commented on how they are scraping our structured meta objects to create new methods for understanding the objects themselves. Reminds me of the cycle of life…

Does this help?
Roderic

Self-referential versus meta-data approaches seems to be more related to recommendations than search – intertwined, to be sure, but not exactly the same set of user goals being addressed.

thanks for calling my work interesting and insightful!

I think your dichotomy is interesting and insightful as well, and it’s definitely worth a separate discussion. It could be that self-referential versus meta-data search is a necessary extension to this framework. I can only hope to scratch the surface here and now.

I agree that meta-data search (non self-referential) belongs in the Ingenious quadrant. The purpose of these concepts seems to be finding answers, not simply links to documents. Answers would in this case be the objects (people, songs and films) referred to by meta-data. These objects map better to the users mental model (made up of people, songs and films) than self-referential documents.

Would you agree that (semantic) structure is the key to ingenious meta-data search, as opposed to unstructured self-referential web search?

One measure that seems to be missing is whether or not the search is based on the object itself (self-referential) or based on information about the object (meta-data). For example, Google searches are primarily self-referential — their search results are collections of web pages that were themselves the objects of the search. Google then adds PageRank, a meta-data element to enhance the sorting of the results.

Now think about searching for people, restaurants, movies, songs, etc. If you want to find a song to listen to, it is difficult to search the song itself, so now you have to rely on meta-data. Consider these three examples of companies using meta-data.

Netflix (www.netflix.com) relates the ratings of all users to predict the rating of an individual user.

At Nanocrowd (www.nanocrowd.com), we also search for movies using meta-data, but we apply semantic analysis of viewer comments. By analyzing what people say about movies, we can organize, summarize, rate, and find similar movies.

Other companies, like Pandora (www.pandora.com) hire people to study songs and add their own meta-data.

These three types of meta-data searches are clearly in your “ingenious” quadrant, but I think identifying whether or not search methods are self-referential is important to classifying search. For example, why is Google so bad at finding a book to read or movie to watch? Why is Bing unable to tell you what song you would like? No matter how refined their algorithms get, they are not working with the right data to find popular media.

How would you introduce this concept of self-referential search vs. meta-data search as an element of your classification?

Thanks for sharing your insight! Here’s my immediate reaction:

* Structured versus Unstructured – I think this could replace the dimension I have referred to as “information accessibility”. Ingenious and Diligent search concepts would require more structure to content, query (or both) than required by simple keyword-based web search and even (superficial) collaborative filtering. Several readers have objected to my use of information accessibility, and I’m seriously reconsidering my choice.

* Open Domain versus Vertical – An interesting dimension if my goal was to classify search services or vendors. It relates more to business goals than search concepts in general. Both Bing and Google can be positioned in several of the 4 quadrants.

* Statistical versus Logical – An interesting dimension once again, this time if my goal was to classify search technologies. I prefer to treat a concept like Question Answering as one, not focusing on the underlaying technology, which may be statistical or logical.

* Keyword versus Question Answering – This is on the other hand closer again to search user experience concepts, but it’s to narrow for this particular analysis.

The question you’re bringing up is very interesting, though. Natural language is undeniably our natural way of requesting information from other humans. But computers have only been around for a short while (in evolution time), and there is still no established “natural” way of communicating with a machine. As you say, search engines have taught us to present searches as keywords, and that is in my opinion the most natural way for us to request information from them – today. Tomorrow may bring us something entirely different, like gestural/natural interfaces, but that is all open to speculation.

Here are some other concepts/axes for you to consider:

Structured versus Unstructured
For me this is about the knowledge source. Is it unstructured natural language like what appears in web pages or structured data that computers can process and reason with. Most of the main search engines uses unstructured web pages as their primary knowledge source but also have databases of structured knowledge they use for certain types of response.

Open Domain search versus Vertical search
Google, Bing, Ask etc. are open domain – as is True Knowledge. Many other search companies specialise in a narrow area and are only interested in information that falls within that area.

Statistical versus Logical
Most search engines use statistical techniques to turn up results. Others (True Knowledge and Wolfram Alpha) generate responses using calculation and logical steps for which statistics is not part of the process.

Keyword versus Question Answering
Natural language questions are the natural way that humans request information. The statistical techniques used by search engines have taught users to present most search queries using keywords.

I cannot find a good term to better describe your vertical axis. It could be something along the lines of “information richness” or “information synthesis”. Basically, as you have described, those are the systems that try to present “information rich” results to better satisfy user’s information seeking need.

Your comment is very perceptive. Finding the “right” dimension for the vertical axis seems to be difficult, and “accessibility” is just the one that makes the most sense for me so far. I agree that rich presentation/interaction is another way to describe what is happening as search technology is used to dig deeper into the information space, either algorithmically or through user-system communication.

In your opinion, what would be a better interpretation of “accessibility”, and what kind of categorization would that lead to (if different from the one I have proposed)?

BTW, the authors of the paper on accessibility in information retrieval tells me that they have come to prefer the term retrievability over accessibility (because of the obvious confusion with general web accessibility): http://www.dcs.gla.ac.uk/publications/PAPERS/8984/fp0120-azzopardi.pdf

Unstructured vs. structured is another viable option for choosing axes. This is still work ib progress. However, I’m quite pleased with the resulting quadrant and how they can be used as a framework for understanding the potential impact of search technology in terms of business goals and user needs. Thanks for the feedback!

Also how about structured vs. unstructured, i.e. where structured is assumed to be anchored by a taxonomy/vocabulary (someone referred to semantic search). I’m referring to the difference between searching on the content itself vs. the meta of the content.