semantic (n): of or relating to meaning.

semantic finder (n): Apples File browser with files organised in a hierarchy based on meaning

Patent Application:
Inventors: Bellegarda, Jerome R.; (Los Gatos, CA) ; Loofbourrow, Wayne; (San Jose, CA)
Apple patent application for a semantic file browser for OS X Finder. Files would be searched and arranged in a hierarchy based on meaning. An exciting possibility for the future of the Finder.

An automatic file clustering algorithm enables documents within a file system to be displayed in a semantic view. The file clustering algorithm maps all words and documents into an appropriate semantic vector space, clusters the documents at a predetermined level of granularity, and assigns a meaningful descriptor to each resulting cluster. The documents are displayed to the user in a hierarchy in accordance with the resulting clusters. This results in a virtual file system with a semantic organization, that allows the user to navigate by content.

Most users start out with a reasonably principled directory structure, but as time goes by and the complexity of their file hierarchy grows, it typically becomes more and more difficult for them to navigate this ever-expanding portion of the file system. Advanced user interface elements, such as the “column view” in the MacOS X operating system distributed by Apple Computer Inc., are available for them to visualize what the file hierarchy looks like at any given point. In addition, sophisticated search capabilities can help them find the information they want to access, e.g. by file name/characteristics, document content, etc.

[0004] Nevertheless, a far better navigation experience could be achieved if there existed a method for visualizing/displaying documents based on their content, i.e., in a semantic hierarchy. This semantic view option would complement current directory structures, and likely help users keep their file hierarchies in a readily usable state.

[0005] To make a semantic view possible, it is necessary to classify each user-generated file against a suitable taxonomy, so that files sharing the same taxonomy node can be grouped together accordingly. There are a number of possible approaches to this information management problem.

[0006] A first information management approach is to classify information against an existing all-purpose taxonomy using standard similarity measures. This approach is not particularly adequate, however, because to be useful, the taxonomy needs to be user-specific. For example, consider the concept of “metal.” While it connotes a hard material to some users, it represents a type of music for other users. As another example, the term “jaguar” is likely to have a very different meaning to car enthusiasts, to animal lovers, and to personal computer afficionados (“Jaguar” being the code name for the MacOS X v 10.2 operating system).

[0007] A second of the three approaches is to modify the all-purpose taxonomy to more closely reflect the situation at hand, by applying hand-crafted mapping rules. This approach has limitations as well. Setting aside the problem of hand-crafting the mapping rules (a non-trivial endeavor, in itself), typically the method is only able to perform slight modifications on the node labels, not the basic structure of the taxonomy. This may work for some users some of the time, but because it fails to take into account individual preferences, this approach is likely to dilute the perceived value of the result. In the example above, “jaguar” might be very close to the top of the preferred taxonomy for a MacOS X enthusiast, but very deep into it for another person. The ability to re-structure the existing taxonomy to increase the visibility of “jaguar” would probably be critical to the MacOS X enthusiast.

[0008] Finally, the third approach is to first build a user-specific taxonomy by manually defining a set of suitable user-related topics. Classification proceeds by isolating a relatively small, for example 50 to 100, number of documents that are deemed paradigms of each topic, and training a statistical classification system on that data. The statistical classification system is then used to classify the remaining files. This method is clearly not suited to the particular problem at hand, as users are generally not the kind of information specialist capable of laboriously assembling the necessary training sets. Furthermore, as the number of categories increases, this task becomes exponentially more onerous.