Determining Intermediary Closely Related Languages to Find a Mediator for Intertribal Conflict Resolution

Abstract

Indonesia has a diverse ethnic and cultural background. However, this diversity sometimes creates social problems, such as intertribal conflict. Because of the large differences among tribal languages, it is often difficult for conflicting parties to dialog for conflict resolution. To address this problem, we aim to find intermediary closely related languages from a language similarity knowledge graph using the best-performing pathfinding algorithms. In this research, we analyze the performances of two pathfinding algorithms, namely, Dijkstra and Yen’s K, by comparing their execution time and the total lexical distances of the intermediary languages (called “the cost”). Our research findings show that even though the Dijkstra and Yen’s K algorithms have equal total cost for all the cases, Yen’s K outperformed Dijkstra at searching for intermediary languages that are closely related, with an average of 160% higher performance on execution time. The selection of native speakers of the obtained intermediary languages as mediators is formalized as an optimization problem with four criteria: language similarity, geographical distance, background, and expected salary. We present a case study where the intermediary closely related languages can be used as a guideline to find mediators who can help resolve the intertribal conflicts among Indonesian tribes. To calculate the first criteria, we implemented the Yen’s K algorithm to calculate the shortest path between target languages and return the path via the intermediary languages. This implementation shows the potential use of the mediator selection model defined in this paper in various other roles such as trader or salesman, politician’s spokesman, reporter or journalist, etc.