Design and Use of a Semantic Similarity Measure for Interoperability Among Agents.

The capability to identify the sense of polysemic words, i.e. words that have multiple meanings, is an essential part of intelligent systems, e.g. when updating an agent's beliefs during conversations. This process is also called Word Sense Disambiguation and is approached by applying semantic similarity measures. Within this work, we present an algorithm to create such a semantic similarity measure using marker passing, that: (1) generates a semantic network out of a concepts used e.g. in semantic service descriptions, (2) sends markers through the networks to tag sub-graphs that are of relevance, and (3) uses these markers to create a semantic similarity measure. We will discuss the properties of the algorithm, elaborate its performance, and discuss the lifted properties for the algorithm to be used in WSD. To evaluate our approach, we compare it to state of the art measures using the Rubinstein1965 dataset. It is shown, that our approach outperforms these state of the art measures.