Abstract: Various methods and systems for determining a dominant object of a query and employing the dominant object to provide enhanced search services are discussed. A query is segmented into a set of n_grams. Entity extraction and resolution (EER) methods are employed to determine implicit and explicit aspects for each n_gram. N_grams that include explicit aspects are pruned from the set of n_grams and a pruned set of candidate n_grams is generated from the non-pruned n_grams. Knowledge graphs are employed to generate a ranked list of associated categories for each candidate n_gram. A ranked list of categories associated with the un-segmented query is generated based on knowledge graphs. The candidate n_gram with the highest ranked associated category that is also a highly ranked category associated with the un-segmented query is selected as the dominant object of the query. Enhanced search results are provided based on the determined dominant object.

Abstract: A query segmentation system segments a search query into a query segment that identifies search results from items stored on a database. The query segmentation system receives a query string, and the query string comprises a plurality of tokens. A first token and a second token are identified from the plurality of tokens, and a first vector and a second vector associated with the first token and the second token respectively are determined. The query segmentation system determines whether to include the first and second tokens in a single query segment based on the first and second vectors. The single query segment is processed to identify the search results from the database.

Abstract: Various methods and systems for determining a dominant object of a query and employing the dominant object to provide enhanced search services are discussed. A query is segmented into a set of n_grams. Entity extraction and resolution (EER) methods are employed to determine implicit and explicit aspects for each n_gram. N_grams that include explicit aspects are pruned from the set of n_grams and a pruned set of candidate n_grams is generated from the non-pruned n_grams. Knowledge graphs are employed to generate a ranked list of associated categories for each candidate n_gram. A ranked list of categories associated with the un-segmented query is generated based on knowledge graphs. The candidate n_gram with the highest ranked associated category that is also a highly ranked category associated with the un-segmented query is selected as the dominant object of the query. Enhanced search results are provided based on the determined dominant object.

Abstract: Various methods and systems for determining a dominant object of a query and employing the dominant object to provide enhanced search services are discussed. A query is segmented into a set of n_grams. Entity extraction and resolution (EER) methods are employed to determine implicit and explicit aspects for each n_gram. N_grams that include explicit aspects are pruned from the set of n_grams and a pruned set of candidate n_grams is generated from the non-pruned n_grams. Knowledge graphs are employed to generate a ranked list of associated categories for each candidate n_gram. A ranked list of categories associated with the un-segmented query is generated based on knowledge graphs. The candidate n_gram with the highest ranked associated category that is also a highly ranked category associated with the un-segmented query is selected as the dominant object of the query. Enhanced search results are provided based on the determined dominant object.

Abstract: Various methods and systems for determining a dominant object of a query and employing the dominant object to provide enhanced search services are discussed. A query is segmented into a set of n_grams. Entity extraction and resolution (EER) methods are employed to determine implicit and explicit aspects for each n_gram. N_grams that include explicit aspects are pruned from the set of n_grams and a pruned set of candidate n_grams is generated from the non-pruned n_grams. Knowledge graphs are employed to generate a ranked list of associated categories for each candidate n_gram. A ranked list of categories associated with the un-segmented query is generated based on knowledge graphs. The candidate n_gram with the highest ranked associated category that is also a highly ranked category associated with the un-segmented query is selected as the dominant object of the query. Enhanced search results are provided based on the determined dominant object.

Abstract: Methods and systems for query segmentation are disclosed. In one aspects, a method includes receiving, by one or more hardware processors, a query string, the query string comprising a plurality of tokens, identifying, by the one or more hardware processors, a first token and a second token from the plurality of tokens, determining, by the one or more hardware processors, a first vector and a second vector associated with the first token and the second token respectively, determining, by the one or more hardware processors, whether to include the first and second tokens in a single query segment based on the first and second vectors, generating, by the one or more hardware processors, a plurality of query segments based on the determination; and processing, by the one or more hardware processors, the query based on the plurality of query segments.