Kohonen Mapping and Text Semantics Xia Lin College of Information Science and Technology Drexel University

Ten years ago Lin, X., Soergel, D., & Marchionini, G. A self-organizing semantic map for information retrieval. SIGIR’89, pp Applied Kohonen’s mapping to a small document set: – 140 documents – 25 indexing words

A Semantic Map for 140 AI Documents Citation database retrieval Intelligent library search online application Machine learningknowledge expert systems natural process language research network others

Features of the Semantic Map Reveal frequencies and distribution of underlying data. Preserve metric relationships “as faithfully as possible” while mapping from high- dimensional data to a two-dimensional display Display co-occurrence structures through its neighborhood structures.

Why do you prefer using Self- organizing Map (SOM) to textual information? The power of abstraction The feature of self-organization The format of output -- rich information for display

Information Abstraction SOM utilizes statistical information of text in a unique way – Both individual data and their inter- relationships are represented. – Learning takes place gradually To tolerate uncertainty/fuzziness in the input data – It represents large amount of data economically Similar to the way the brain processes/stores information?

Information Organization SOM uses the input data to make a random network become an organized network. – Each piece of information will find its own identity (the best place) on the map. – All the related information should be organized together. – A compromise or enforcement of both “individual responsibilities” and “social responsibilities.”

Information Visualization SOM’s output is an associative network that can be used to implement various interactive functions of the interface – A good overview of underlying data – A variety of topologic structures Sizes of groups, distances, weights of vectors, patterns of inputs, etc. – A space of both documents and terms Effective use all the space of the two-dimensional area.

How much semantics are represented in Kohonen’s map? It’s an open question. Understanding can be gained through comparisons and applications.

dove hen duck goose owl hawk eagle cat fox wolf tiger lion cow dog horse zerba (a) Hierarchical cluster fox wolf eagle cat dog tiger lion horse zebra cow owl hawk hen goose duck dove (c) Kohonen's feature map dog goose dove hen tiger hawk owl wolf duck lion eagle fox cat horse zebra cow (b) Principal component analysis

graph tree minor survey time response user computer interface human EPS M3 M4 M2 M1 C2 C5 C1 C3 C4 system (a) Display of the Latent semantic indexing result System User Human Interface Graph Tree C4 C1 C3 C5 C2 M1 M2 M3 M4 (d) Document and term map by the feature map computer Response time EPS minors survey

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