Personalized Keyword Search Contributions

Transcription

1 Personalized Keyword Search Contributions

2 Introduction Georgia Koutrika, Alkis Simitsis, Yannis E. Ioannidis: Précis: The Essence of a Query Answer. ICDE 26 Kostas Stefanidis, Marina Drosou, Evaggelia Pitoura: PerK: Personalized Keyword Search in Relational Databases through Preferences. EDBT 2

3 Koutrika et al. (26): Weights assigned to edges of the database graph. Weights. A weight, w [,], is assigned to each edge of the graph showing the significance of the bond between the corresponding nodes. Movie Database Schema: Movie Database Graph: THEATRE (tid, name, phone, region) PLAY (tid, mid, date) (mid, title, year, did) (mid, genre) THEATRE NAME PHONE REGION DATE.6 ( ) PLAY w = strong relationship: if one node appears in an answer, then the other node appear as well. w =, occurrence of one node in an answer does not imply occurrence of the other one. TITLE YEAR DID ( ) ( ) ( ).9 ( ) ( ).7

4 Koutrika et al. (26) A directed join edge expresses the dependence of the left part of the join on the right part. TITLE YEAR DID ( ).9 ( ) Movies and genres are related but one may consider that genres are more dependent on movies than the other way around. For instance, the weight of the edge from to is, while the weight of the edge from to is.9 answer regarding a genre always contain related movies answer regarding a movie not necessary contain related genre

5 Genre & detailed answers Date & shorter answers THEATRE.7 NAME PHONE REGION DATE ( ) PLAY THEATRE.7 NAME PHONE REGION DATE ( ) PLAY TITLE YEAR DID ( ) ( ) ( ) ( ) ( ) TITLE YEAR DID ( ) ( ) ( ) ( ) ( ) Information about,, PLAY, THEATER in his query answer. Incorporating implicit information Just Information about and THEATER.

6 Personalized Keyword Search Contributions Personalized Keyword Search with an Interactive Exploration Data approach. Personalized Keyword Search with a semantic approach.

7 Personalized Keyword Search with an Interactive Exploration Data approach Smart Drill Down: an operator for interactively exploring a relational table to discover and summarize interesting groups of tuples. Each group of tuples is described by a rule. For instance, the rule (a; b; *; ) tells us that there are a thousand tuples with value a in the first column and b in the second one (and any value in the third one). It allows simultaneous drill downs on multiple columns. The result of the drill downs operator are lists of rules in a summary table. A rule is a tuple with a value for each column in tables of the database. A rule has other attributes, such as count and weight associated with it. The weight column represents the number of non-starred (*) attributes and the column count represents the number of tuples that are cover by this rule. The value in each column of the rule can either be one of the values in the corresponding column of tables, or *, representing a wildcard character that is all values in the column.

8 Personalized Keyword Search with an Interactive Exploration Data approach Smart Drill Down: Summary table after first smart drill down K = 2 Movie Theater Region Genre Weight Count * * * * Matrix Cine Atlas Tampere Drama 4 4 Beautiful Mind * Tampere Action 3 6 But analysts can query on increasingly larger databases, having too many results and just a part of them is useful for this final user.

9 Personalized Keyword Search with an Interactive Exploration Data approach Combining smart drill down and personalized keyword-based search, analysts could interact with information related to their final goals. Based on weights, the operator smart drill down could identify the piece of the database in which the final user is interested in. Selecting in that way which columns are going to be shown in the summary table (attributes with w = in tables with relationships with w = as well). Each rule in the summary table will contain at least one keyword in any of its cells.

10 Genre & detailed answers Date & shorter answers THEATRE.7 NAME PHONE REGION DATE ( ) PLAY THEATRE.7 NAME PHONE REGION DATE ( ) PLAY TITLE YEAR DID ( ) ( ) ( ) ( ) ( ) TITLE YEAR DID ( ) ( ) ( ) ( ) ( ) Information about,, PLAY, THEATER in his query answer. Incorporating implicit information Just Information about and THEATER.

11 Personalized Keyword Search with an Interactive Exploration Data approach User : Information about,, PLAY, THEATER in his query answer K = 2 Q = {Tampere} Movie Theater Region Genre Weight Count * * * * Matrix Cine Atlas Tampere Drama 4 4 analyst Beautiful Mind * Tampere Action 3 6 Summary table after first smart drill down

12 Personalized Keyword Search with an Interactive Exploration Data approach User 2: Information about and THEATER in his query answer K = 2 Q = {Tampere} Movie Theater Region Genre Weight Count * * * * Matrix Cine Atlas Tampere Drama 4 4 Beautiful Mind * Tampere Action 3 6 Movie Theater Region Weight Count * * * Matrix Cine Atlas Tampere 3 4 analyst Beautiful Mind * Tampere 2 6

13 Semantic approach in Personalized Keyword Search Traditional information retrieval systems, and particularly Web search engines, have focused on keyword matching. Users typically input their information needs as a set of keywords and the search engines match these keywords with documents to find the most relevant. This semantic approach goes beyond keyword-matching when scoring documents to provide personally relevant results. It is important to understand the structure of the information.

14 Contributions mid year title description m 999 Matrix Excellent m2 2 Beautiful Mind Excellent m3 23 Finding Nemo Recommended m4 2 The Black Swan Tampere User query = Movies in Tampere Weight between tables and tables and theirs attributes mid tid date m t m t m2 t m3 t m4 t PLAY tid region name t Tampere Cine Atlas t2 Tampere Plevna t3 Helsinki Tennispalatsi t4 Pori Promenadi THEATER

15 No semantic Approach mid year title description m 999 Matrix Excellent m2 2 Beautiful Mind Excellent m3 23 Finding Nemo Recommended m4 2 The Black Swan Tampere User query = Movies in Tampere Weight between tables and tables and theirs attributes User does not want to find a movie that its description contains the keyword Tampere mid tid date m t m t m2 t m3 t m4 t PLAY tid region name t Tampere Cine Atlas t2 Tampere Plevna t3 Helsinki Tennispalatsi t4 Pori Promenadi THEATER User wants to find movies located in the region Tampere

16 Semantic Approach mid year title description m 999 Matrix Excellent m2 2 Beautiful Mind Excellent m3 23 Finding Nemo Recommended m4 2 The Black Swan Tampere User query = Movies in Tampere Weight between tables and tables and theirs attributes User does not want to find a movie that its description contains the keyword Tampere mid tid date m t m t m2 t m3 t m4 t PLAY tid region name t Tampere Cine Atlas t2 Tampere Plevna t3 Helsinki Tennispalatsi t4 Pori Promenadi THEATER User wants to find movies located in the region Tampere

17 Thank you!