Searching for a person's name is a common online activity. However, Web search engines provide few accurate results to queries containing names. In contrast to a general word which has only one correct spelling, there are several legitimate spellings of a given name. Today, most techniques used to suggest synonyms in online search are based on pattern matching and phonetic encoding, however they often perform poorly. As a result, there is a need for an effective tool for improved synonym suggestion. In this paper, we propose a revolutionary approach for tackling the problem of synonym suggestion. Our novel algorithm, GRAFT, utilizes historical data collected from genealogy websites, along with network algorithms. GRAFT is a general algorithm that suggests synonyms using a graph based on names derived from digitized ancestral family trees. Synonyms are extracted from this graph, which is constructed using generic ordering functions that outperform other algorithms that suggest synonyms based on a single dimension, a factor that limits their performance. We evaluated GRAFT's performance on three ground truth datasets of forenames and surnames, including a large-scale online genealogy dataset with over 16 million profiles and more than 700,000 unique forenames and 500,000 surnames. We compared GRAFT's performance at suggesting synonyms to 10 other algorithms, including phonetic encoding, string similarity algorithms, and machine and deep learning algorithms. The results show GRAFT's superiority with respect to both forenames and surnames and demonstrate its use as a tool to improve synonym suggestion.
|State||Published - 9 Dec 2019|