Temporal Analysis of Entity Relatedness and its Evolution using Wikipedia and DBpedia
This work addresses the time sensitivity of semantic similarity for researchers in knowledge representation and NLP, though it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of entity relatedness by analyzing temporal changes in the Wikipedia network, showing that using the 2010 network yields the strongest results and integrating multiple time frames improves similarity.
Many researchers have made use of the Wikipedia network for relatedness and similarity tasks. However, most approaches use only the most recent information and not historical changes in the network. We provide an analysis of entity relatedness using temporal graph-based approaches over different versions of the Wikipedia article link network and DBpedia, which is an open-source knowledge base extracted from Wikipedia. We consider creating the Wikipedia article link network as both a union and intersection of edges over multiple time points and present a novel variation of the Jaccard index to weight edges based on their transience. We evaluate our results against the KORE dataset, which was created in 2010, and show that using the 2010 Wikipedia article link network produces the strongest result, suggesting that semantic similarity is time sensitive. We then show that integrating multiple time frames in our methods can give a better overall similarity demonstrating that temporal evolution can have an important effect on entity relatedness.