Sina Fakhraee - Academia.edu (original) (raw)
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Papers by Sina Fakhraee
2011 22nd International Workshop on Database and Expert Systems Applications, 2011
2011 22nd International Workshop on Database and Expert Systems Applications, 2011
An important and challenging task in any keyword-based search system in text documents or relatio... more An important and challenging task in any keyword-based search system in text documents or relational databases is the capability of the system to find additional results besides the actual search results and present them to the users as recommendations. This function allows the records that might be of interest to the user to be discovered and essentially enhances the user's browsing experience. Most recommender systems such as Amazon and IMDB rely heavily on the users' ratings, previously learned patterns from the users and their selected items to achieve this goal. In this paper we present a system called Tuple Recommender which first searches a relational database for a given keyword query and then makes the search recommendations based on the similarity of the tuples with respect to the tables' attributes in which the search terms are found, without relying on the previously learned patterns or users' ratings.
Proceedings of the Third International Workshop on Keyword Search on Structured Data - KEYS '12, 2012
Keyword search over relational databases has been broadly studied in recent years. Research works... more Keyword search over relational databases has been broadly studied in recent years. Research works have been done to address both the efficiency and the effectiveness of the keyword search over relational databases. One issue with keyword search in general is its ambiguity which can ultimately impact the effectiveness of the search in terms of the quality of the search results. This ambiguity is primarily due to the ambiguity of the contextual meaning of each term in the query (e.g. each query term can be mapped to different schema terms with the same name or their synonyms). In addition to the query ambiguity itself, the relationships between the keywords in the search results are crucial for the proper interpretation of the search results by the user and should be clearly presented in the search results. To address these issues we have designed and implemented a prototype system DBSemSXplorer which can answer the traditional keyword search over relational databases in a more effective way with a better presentation of search results. We address the keyword search ambiguity issue by adapting some of the existing approaches for keyword mapping from the query terms to the schema terms/instances. The approaches we have adapted for term mapping capture both the syntactic similarity between the query keywords and the schema terms as well as the semantic similarity (e.g. definition of the keywords) of the two and give better mappings and ultimately more accurate results. Finally, to address the last issue of lacking clear relationships among the terms appearing in the search results, our system has leveraged semantic web technologies in order to enrich the knowledgebase and to discover the relationships between the keywords. Our experiments show that our system is more effective than the traditional keyword search approaches by enabling the users to find the search results which are more relevant to their keyword queries.
2011 22nd International Workshop on Database and Expert Systems Applications, 2011
2011 22nd International Workshop on Database and Expert Systems Applications, 2011
An important and challenging task in any keyword-based search system in text documents or relatio... more An important and challenging task in any keyword-based search system in text documents or relational databases is the capability of the system to find additional results besides the actual search results and present them to the users as recommendations. This function allows the records that might be of interest to the user to be discovered and essentially enhances the user's browsing experience. Most recommender systems such as Amazon and IMDB rely heavily on the users' ratings, previously learned patterns from the users and their selected items to achieve this goal. In this paper we present a system called Tuple Recommender which first searches a relational database for a given keyword query and then makes the search recommendations based on the similarity of the tuples with respect to the tables' attributes in which the search terms are found, without relying on the previously learned patterns or users' ratings.
Proceedings of the Third International Workshop on Keyword Search on Structured Data - KEYS '12, 2012
Keyword search over relational databases has been broadly studied in recent years. Research works... more Keyword search over relational databases has been broadly studied in recent years. Research works have been done to address both the efficiency and the effectiveness of the keyword search over relational databases. One issue with keyword search in general is its ambiguity which can ultimately impact the effectiveness of the search in terms of the quality of the search results. This ambiguity is primarily due to the ambiguity of the contextual meaning of each term in the query (e.g. each query term can be mapped to different schema terms with the same name or their synonyms). In addition to the query ambiguity itself, the relationships between the keywords in the search results are crucial for the proper interpretation of the search results by the user and should be clearly presented in the search results. To address these issues we have designed and implemented a prototype system DBSemSXplorer which can answer the traditional keyword search over relational databases in a more effective way with a better presentation of search results. We address the keyword search ambiguity issue by adapting some of the existing approaches for keyword mapping from the query terms to the schema terms/instances. The approaches we have adapted for term mapping capture both the syntactic similarity between the query keywords and the schema terms as well as the semantic similarity (e.g. definition of the keywords) of the two and give better mappings and ultimately more accurate results. Finally, to address the last issue of lacking clear relationships among the terms appearing in the search results, our system has leveraged semantic web technologies in order to enrich the knowledgebase and to discover the relationships between the keywords. Our experiments show that our system is more effective than the traditional keyword search approaches by enabling the users to find the search results which are more relevant to their keyword queries.