Anytime Query Answering in RDF through Evolutionary Algorithms

22 Pages·2008·0.18 MB·English

Anytime Query Answering in RDF
through Evolutionary Algorithms
Eyal Oren Christophe Gu´eret Stefan Schlobach
VrijeUniversiteitAmsterdam
ISWC 2008
Overview
(cid:73) Problem: query answering over large RDF graphs
(cid:73) find variable assignments, such that the data graph
entails the substituted query graph
(cid:73) Requirements:
(cid:73) approximate (ranking more important than perfect)
(cid:73) anytime (streaming answers improving over time)
(cid:73) parallel (scale computation with added nodes)
(cid:73) Evolutionary approach:
(cid:73) guess complete variable assignment
(cid:73) verify solution on the data graph
(cid:73) improve assignment and repeat
Example
Listing 1: RDF data
(cid:7) (cid:4)
dblp:ullman88 rdf:type opus:Book .
dblp:ullman89 rdf:type opus:Book .
dblp:ullman88 rdfs:label "Principles of Database Systems" .
dblp:ullman88 opus:author dblp:ullman .
dblp:ullman foaf:homepage ... .
(cid:6) (cid:5)
Listing 2: SPARQL query
(cid:7) (cid:4)
SELECT ?title WHERE {
?publication rdf:type opus:Book .
?publication rdfs:label ?title .
}
(cid:6) (cid:5)
Listing 3: Expected answer
(cid:7) (cid:4)
(?title,"Principles of Database Systems")
(cid:6) (cid:5)
Traditional approach
(cid:7) (cid:4)
... WHERE { ?publication rdf:type opus:Book; rdfs:label ?title . }
(cid:6) (cid:5)
1. Solve: ?publication rdf:type opus:Book
?publication
dblp:ullman88
dblp:ullman89
2. Solve: ?publication rdfs:label ?title
?publication ?title
dblp:ullman88 "Principles of ..."
3. Join both result tables, project the result
(?title, "Principles of ...")
(cid:73) Solving clauses: iterate over all records
(cid:73) Solving joins: nested loops, etc.
Our approach
(cid:7) (cid:4)
... WHERE { ?publication rdf:type opus:Book; rdfs:label ?title . }
(cid:6) (cid:5)
1. Assign some value to each query variable
(?publication, dblp:ullman88)
(?title, opus:Book)
2. Verify solution (substitute into query, verify on graph)
triple correct?
dblp:ullman88 rdf:type opus:Book yes
dblp:ullman88 rdfs:label opus:Book no
3. If the solution is OK, stop. Otherwise, try again.
(cid:73) The loop may be stopped at any time
(cid:73) A result may satisfy a part of the query
(cid:73) Uses membership testing instead of lookup
(cid:73) Implementation uses dictionary encoding
Requirements
(cid:73) A good way to evolve answers
(cid:73) results should improve with each iteration
(cid:73) choice: evolutionary algorithm
(cid:73) A fast method to verify answers
(cid:73) we will try many solutions
(cid:73) verification should not require iteration
(cid:73) choice: Bloom filters
Evolutionary algorithms
Binary Bloom filters (1/2)
(cid:73) Compact representation: eg. set of n = 8 bits
1 2 3 4 5 6 7 8
(cid:73) Supports two operations
(cid:73) insert(key): insert a key into the filter
(cid:73) contains(key): test for the presence of a key
(cid:73) Use eg. k = 3 hash functions to compute a set of bits
from a key
hash1(“Hello World”)=8
hash2(“Hello World”)=6
hash3(“Hello World”)=3
Binary Bloom filters (2/2)
(cid:73) insert(“Hello World”)
Current
or (cid:73) Bit-wise OR
operation
“Hello World”
= (cid:73) Always
New successful
(cid:73) contains(“Bonjour”)
Current (cid:73) Bit-wise AND
and operation
“Bonjour” (cid:73) Positive result may be
= a collision:
Result
perror =(1−e−kmn)k
Basic operations
(cid:73) Parsing: construct Bloom filter triple, and construct
domain of candidate values
(cid:73) Initial population: for each variable v , select some
i
value d from domain
i
(cid:73) Verify solution: substitute all selected values, verify
each clause on the Bloom filter
(cid:73) Fitness: number of satisfied clauses
(cid:73) Evolution: one-point cross-over, random mutation