With an ever increasing amount of news being published every day, being able to effectively search these vast amounts of information is of primary interest to many Web ventures. As word-based approaches have their limits in that they ignore a lot of the information in texts, we present Destiny, a linguistic approach where news item sentences are represented as a graph featuring disambiguated words as nodes and grammatical relations between words as edges. Searching is then reminiscent of finding an approximate sub-graph isomorphism between the query sentence graph and the graphs representing the news item sentences, exploiting word synonymy, word hypernymy, and sentence grammar. Using a custom corpus of user-rated queries and sentences, the search algorithm is evaluated based on the Mean Average Precision, Spearman's Rho, and the normalized Discounted Cumulative Gain. Compared to the TF-IDF baseline, the Destiny algorithm performs significantly better on these metrics.
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