Use Case: Semantic Document Recovery

(continued from HD Semantic Maps)

Like most of you, I collect bookmarks. But unlike most of you, I store them into a semantic network, a topic map to be precise.

One problem I certainly share with you, is that all these laboriously collected links are prone to break. To recover them sometimes needs considerable effort and - according to another Murphy Law (are there actually any other laws?) - always hits you at the most inappropriate time.

The Manual Recovery Process

One lame-ass approach is to simply ignore the problem and throw away the URL. You may do this only for unimportant information, but how can you judge if you have lost the document?

When links break, then in most cases the document still exists somewhere, albeit at a different position. Google and Bing would know the answer, but they necessitate you to enter the proper search terms.

This is really easy if you are happy to

  • always keep copies of all documents, and
  • then read them through to find the most appropriate terms (title etc).

And it is even easier if you remember all documents as you go.

The Semantic Map Approach

Now, both approaches never worked well for me. Instead, I now make use of the contextual information "around" a document. This information I actually have in any map rendering:

http://kill.devc.at/system/files/mapreduce-small.jpg

Each document is placed on a certain spot based on a certain term constellation. If one such document disappears, I still have that constellation. And with that I simply do the search.

Needless to say, that this could be further automated given that full text search engine offers an API. Unfortunately (this has nothing to do with luck, but still with fortune!) most don't.

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