Multi-knowledge resources-based semantic similarity models with application for movie recommender system

In recent years, researchers have proposed several feature-based methods to measure semantic similarity using knowledge resources like Wikipedia and WordNet. While Wikipedia covers millions of concepts with multiple features, it has some limitations such as articles with limited content and concept ambiguity. Disambiguating these concepts remains a challenge. Conversely, WordNet offers unambiguous terms by covering all possible senses, making it a useful resource for disambiguating Wikipedia concepts. Additionally, WordNet can enrich the limited content of Wikipedia articles. Thus, we present a new approach that combines both resources to enhance previous feature-based methods of semantic similarity. We begin by analyzing the limitations of previous research, followed by introducing a novel method to disambiguate Wikipedia concepts using WordNet's synonym structure, resulting in more effective disambiguation. Furthermore, we use WordNet to supplement the features in Wikipedia articles and redefine the feature similarity functions. Finally, we train non-linear fitting-based models to measure semantic similarity. Our approach outperforms other previous methods on various benchmarks. To further showcase our approach, we apply our models to develop a movie recommender system using the MovieLens dataset, which consistently outperforms other systems.

Automated summary

Researchers have proposed feature-based methods using knowledge resources like Wikipedia and WordNet to measure semantic similarity. While Wikipedia has limitations such as limited content and concept ambiguity, WordNet offers unambiguous terms and can enrich the limited content of Wikipedia articles. Combining both resources can enhance previous methods of semantic similarity.