An evaluative baseline for geo-semantic relatedness and similarity

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer’s semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity

Trust Between International Joint Venture Partners: Effects of Home Countries

Trust is an important factor in interorganizational relations. Interorganizational trust in cross-border relationships is likely to be influenced by the home countries of both partners. Using data on 165 international joint ventures (IJVs), we show that the perceived trustworthiness of an IJV partner is influenced by the general propensity to trust in the trustor's home country. Moreover, the trustworthiness perceived by a focal parent firm is also affected by the home country of the other IJV partner. This second effect is mitigated by experience between the partners