Efficient keyword search on large tree structured datasets

Abstract

Keyword search is the most popular paradigm for querying XML data on the web. In this context, three challenging problems are (a) to avoid missing useful results in the answer set, (b) to rank the results with respect to some relevance criterion and (c) to design algorithms that can efficiently compute the results on large datasets.In this paper, we present a novel multi-stack based algorithm that returns as an answer to a keyword query all the results ranked on their size. Our algorithm exploits a lattice of stacks each corresponding to a partition of the keyword set of the query. This feature empowers a linear time performance on the size of the input data for a given number of query keywords. As a result, our algorithm can run efficiently on large input data for several keywords. We also present a variation of our algorithm which accounts for infrequent keywords in the query and show that it can significantly improve the execution time. An extensive experimental evaluation of our approach confirms the theoretical analysis, and shows that it scales smoothly when the size of the input data and the number of input keywords increases.

Diversified spatial keyword search on RDF data

“…(1) when a place is added to R, where we need to update the score of all seen elements and (2) when a new place is emerged from our kS P algorithms, where we need to calculate its diversity score against all elements in R. Finally, IAdU algorithm uses a threshold, θ , that facilitates the pruning of unseen places if they cannot qualify in R. [15][16][17][18][19] which is calculated against places already in R (lines 16,17). The threshold θ is updated accordingly (line 19).…”

“…In view of this limitation, keyword search paradigms facilitate retrieval using only keywords [16][17][18][19][20][21][22][23]26,40,46,50]. Given a query that consists of a set of keywords, an answer is a subgraph of the RDF graph.…”

Top-k-size keyword search on tree structured data

“…In [11] we present a multi-stack algorithm which also exploits multiple stacks to compute LCAs and their sizes. This algorithm does not compute results restricted by a threshold or top-k results which is the focus of this paper.…”

Αποδοτική Αναζήτηση Με Χρήση Λέξεων - Κλειδιών Σε Ημιδομημένα Δεδομένα

“…΄Ενα μεγάλο μέρος της βιβλιογραφίας ασχολείται με τον ορισμό της σημασιολογίας των ελάχιστων κοινών προγόνων, που ορίζουν ένα ακριβέστερο και πληρέστερο σύνολο αποτελεσμάτων αναζήτησης [13,22,21,26,35,52,23,28,11,17,6,29,34,49,42,2,18,1]. Οι βασικές σημασιολογίες φιλτραρίσματος των LCA μιας ερώτησης σε μια βάση δεδομένων είναι τέσσερις.…”

“…Ως εκ τούτου, οι εισαγωγές σε μια στοίβα ελαχιστοποιούνται. ΄Οταν ένας μερικός LCA προκύψει σε ένα επίπεδο του δικτυωτού, πρόστίθεται στη λίστα μερικών LCA του επόμενου επιπεδου (γραμμές [15][16][17][18][19]. Αν η λίστα περιέχει ήδη έναν συγκρίσιμο μερικό LCA για τον ίδιο κόμβο του δέντρου (ίδιο κωδικό Dewey), μόνο το μικρότερο μέγεθος αποθηκεύεται στη λίστα (γραμμές [18][19].…”