Spreading Activation for Web Scale Reasoning: Promise and Problems

Nazihah Md. Akim1, Alan Dix1,2, Akrivi Katifori3, Giorgos Lepouras4, Nadeem Shabir2, Costas Vassilakis3,4

1 School of Computing and Communications, InfoLab21, Lancaster University, Lancaster, UK
2 Talis, Birmingham, UK
3 Department of Informatics & Telecommunications, University of Athens, Athens, Hellas (Greece)
4 Dept. of Computer Science and Technology, University of Peloponnese, Tripolis, Hellas (Greece)

Presented at WebSci'11, Koblenz 15-17 June 2011.

Download draft paper (PDF, 399K)


Various forms of spreading activation has been used in a number of web systems, not least in the PageRank algorithm. In our own work we have been using this as a technique for managing context over small and large ontologies, and both our own work and that in LarKC suggests that spreading activation has the potential to aid in reasoning over web-scale data sets including the growing set of linked open data resources. Of particular importance is that spreading activation can be applied locally to a dynamic self-selecting working set of an (practically) unbound linked data collection, as well as globally to the entire collection. However, this potential does not come without problems, some concerning the nature of the algorithm on any large data set, and some more to do with the particular nature of linked open data.

Keywords: spreading activation, reasoning, linked open data, context


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poster [PDF 325K]


Fig1. Different shapes of the transformation function f.
[zoom image]


(i) transform non-linear near zero [zoom image]

(ii) transform linear near zero [zoom image]

Fig. 2. Greedy node collection


Fig. 3. Unexpected increase in activation
[zoom image]


(i) star [zoom image]

(ii) wheel [zoom image]

Fig. 4. Simulated graphs








Alan Dix 9/5/2011