{'fraction_non_alphanumeric': 0.014693171996542784, 'fraction_numerical': 0.0, 'mean_word_length': 5.579545454545454, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Synthesizing Ontology Alignment Methods Using the Max-Sum Algorithm

This paper addresses the problem of synthesizing ontology alignment methods by maximizing the social welfare within a group of interacting agents: Specifically, each agent is responsible for computing mappings concerning a specific ontology element, using a specific alignment method. Each agent interacts with other agents with whom it shares constraints concerning the validity of the mappings it computes. Interacting agents form a bipartite factor graph, composed of variable and function nodes, representing alignment decisions and utilities, respectively. Agents need to reach an agreement to the mapping of the ontology elements consistently to the semantics of specifications with respect to their mapping preferences. Addressing the synthesis problem in such a way allows us to use an extension of the max-sum algorithm to generate near-to-optimal solutions to the alignment of ontologies through local decentralized message passing. We show the potential of such an approach by synthesizing a number of alignment methods, studying their performance in the OAEI benchmark series.