26 Responses

  1. Vegard Sandvold
    Vegard Sandvold May 4, 2009 at 19:17 |

    A Topology of Search Concepts | The Noisy Channel says:

    More importantly, I hope this analysis helps advance our ability as technologists to match solutions to information seeking problems. Many of us have an intuitive sense of how to do so, but I rarely see principled arguments–particularly from vendors who may be reluctant to forgo any use case that could translate into revenue.

    (editor’s note: still trying to fix broken backlinks)

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  2. Gene Golovchinsky
    Gene Golovchinsky May 4, 2009 at 19:29 |

    How does this taxonomy address issues of interaction as applied to information seeking? How would you classify systems that make it easier to construct queries, to refine queries, to make sense of the results (obtained through whatever algorithms are available), etc. These interaction issues are important because they affect how usable (and therefore effective) systems that support information seeking truly are.

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  3. Vegard Sandvold
    Vegard Sandvold May 4, 2009 at 19:50 |

    @Gene Golovchinsky
    Thanks for the feedback!

    If I understand you correctly, I believe the interaction issues you’re describing belong in the upper right quadrant, together with faceted search. I’ve named this quadrant “Diligent Search”, due to the combination of high information accessibility and user-powered retrieval. Does it sound alright to you?

    I know you’re an expert on collaborative search. Where do you think that may fit into this taxonomy?

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  4. Gene Golovchinsky
    Gene Golovchinsky May 4, 2009 at 20:00 |

    I am not sure that “user-powered” captures what’s going on. Perhaps “Empowered user” is a better way to look at it. Users can be empowered by making the latent structure of the data more apparent, they can be empowered by making it easier to express their information needs, by making it easier to do all this iteratively. Facets are one example; there are many others. But to empower users, we typically resort to some algorithmic processing. It seems more appropriate to think of the horizontal axis of your model as the degree of interaction the system accords the user. The vertical axis seems to reflect the depth of processing of the data to facilitate these possible interactions. Does that make sense?

    To answer your question, collaboration seems to be orthogonal to this, although there may be some coupling of interactions between people and the underlying system, if that system represents people’s actions explicitly.

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  5. Dorai Thodla
    Dorai Thodla May 5, 2009 at 03:17 |

    A good collection. Here are some additions:

    Contextual Search – Search in a specific space or with useful contextual information
    Vertical Search – a little narrower than contextual search
    Semantic Search – Searching against well coded information

    My quest for a better search:
    http://dorai.wordpress.com/2007/04/25/searching-for-a-better-search/

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  6. Gene Golovchinsky
    Gene Golovchinsky May 5, 2009 at 08:18 |

    Perhaps the reason I am finding it difficult to categorize collaborative search in this framework is that the term “collaborative search” can mean many different things. If you’re referring to something like SearchTogether, an interface-mediated search with data synchronization, then, yes, it probably belongs somewhere on the right side, although it’s not clear to me where on the vertical continuum to place it.

    But there are ways of implementing collaboration that might be classified closer to the middle left of your chart, because the system winds up doing quite a bit to mediate the collaboration.

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  7. Vegard Sandvold
    Vegard Sandvold May 5, 2009 at 16:07 |

    @Gene Golovchinsky
    I can see why it’s difficult to categorize “collaborative search” as one thing. Interface-mediated search could probably fit in with faceted search and the other approaches in the upper right quadrant, which gives the user direct manipulative control over the search results. Algorithmically-mediated search sounds more like a black-boxed approach, and less “user powered”.

    Suddenly I realize that “Algorithm v.s User Mediated” could be a better name for the horizontal axis. You can say that every search involves a process of resolving an information need, and this resolution process can be mediated by algorithms, users (alone or in groups), or a mix of both. Is that what you had in mind?

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  8. Vegard Sandvold
    Vegard Sandvold May 5, 2009 at 16:15 |

    @Dorai Thodla
    Thanks for the suggestions. The concepts you mention are perhaps a bit too broad for this model, but I’m sure they can be broken down into smaller concepts, which would be easier to place.

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  9. Gene Golovchinsky
    Gene Golovchinsky May 5, 2009 at 16:33 |

    @Vegard I tried to make a stab an explanation in this blog post. Maybe it will make more sense.

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  10. William Mougayar
    William Mougayar May 17, 2009 at 17:19 |

    I wonder where you would place “serendipitous” vs. more “deterministic” approaches.

    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.

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  11. Vegard Sandvold
    Vegard Sandvold May 18, 2009 at 22:40 |

    @William Mougayar
    I think deterministic vs. serendipitous is more of an orthogonal dimension to the ones that I used for the plot. Serendipity may be characteristic property of any of the four search approaches. What kind of analysis would you get from changing the axes, do you think?

    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!

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  12. Jay Jiang
    Jay Jiang June 10, 2009 at 22:22 |

    I am not sure “accessibility” is a good term for the vertical axis here. It seems that the upper quadrants here represent those systems that go beyond simple search (where results tend to be just a one dimension list) by trying to provide users with a better information seeking experience with a richer presentation (e.g. hierarchical or multi-dimensional data) or further interactions of the result set.

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  13. Jay Jiang
    Jay Jiang June 23, 2009 at 19:51 |

    Based on the general conception and the interpretation from your linked papers that “accessibility” tends to be in line with concepts like “searchability” and “retrievability” that measure how good a particular document can be accessed for a given IR model. In this regard, traditional simple presentation of list of search result models may not necessarily be weaker than a more sophisticated presentation model.

    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.

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  14. William Tunstall-Pedoe
    William Tunstall-Pedoe August 1, 2009 at 18:06 |

    I have spent a lot of time thinking about this in positioning my business, True Knowledge.

    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.

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  15. Roderic March
    Roderic March August 2, 2009 at 20:37 |

    Thanks for the interesting approach. I think your dimensions are important and insightful.

    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?

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  16. John Shaw
    John Shaw August 5, 2009 at 22:23 |

    I think “Structured versus Unstructured” (really, the degree of structure) is a more exact description of the dimension you’re reaching for – degree of structure in the information being searched, the query, or both.

    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.

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  17. Roderic March
    Roderic March August 7, 2009 at 04:13 |

    Hi Vegard and John,

    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

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  18. John Shaw
    John Shaw August 7, 2009 at 18:29 |

    I think so – the concept of self-referential, meaning having to do with the identity of the object, vs metadata, meaning having to do with placing it in some larger context – is clear, and an important distinction in both search and recommendations, as you say.

    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.

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    Johnf907 July 28, 2014 at 04:21 |

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