A database approach to monitoring the quality of information in RDF stores

A DATABASE APPROACH TO MONITORING THE
QUALITY OF INFORMATION IN RDF STORES
Alexandre Rademaker and Edward Hermann

Wednesday, November 30, 11

NOTES

This is not a research report, this is a research
propose!

Let us start by looking results from database
researchers.


WHAT IS (ENSURE) DATA QUALITY?

Semantic properties of databases can be represented
by integrity constraints!

Integrity enforcement means maintain correctness of
database. Truth Maintenance!

Hendrik, 2011


HENDRIK DECKER

http://web.iti.upv.es/~hendrik/
Universidad Politécnica de Valencia


EXAMPLE

A marriage is between one man and one women only.
How can we model such constraint in a relational
DB?

We are talking about more than: check constraint,
foreign key and primary key.


DB THEORY USES DATALOG

Datalog is more expressive than SQL (transitive
closure)

SQL is FOL (dedidable for ﬁnite model)

SELECT X WHERE Y (give me the binds that satisfy
the clauses)


TWO WAYS TO ENFORCE INTEGRITY

In each update, check if any integrity constraint is
violated. (not always rigorously check due its
performance penalty)

Repair extant violations of constraints. (accumulation
of inconsistency is inevitable)

Hendrik, 2011


INCONSISTENCY-TOLERANT METHODS

Rigorous way is to eliminate all inconsistency. Repair
the whole database.

Relaxation... partial (ﬂexible) repairs!

Absolute consistency is out of question
due its intractability!

Hendrik, 2011


FLEXIBILITY OF PARTIAL INCONSISTENCY

Flexibility served in two ways:
Integrity enforcement is more ﬂexible. Don’t have to
be done all at once. (constraint violations can be
tolerated to be solved in appropriate moment)

Some inconsistency may be unknown at update time.
Total approach would fail in such situation.

But...

Hendrik, 2011


PARTIAL REPAIRS

Absolute consistency is out of question due its intractability.

But, naive inconsistency-tolerant repairs can be data-
destructive.

For a rational ﬂexible repair strategy, one needs criteria
(expressed in terms of metrics)

Only admit repairs that are integrity-preserving! That is, total
amount of integrity violation not increase after the repair.

Hendrik, 2011


FORMAL DEFINITIONS

For an update U (inserts, deletes) of database D, we
denoted DU the updated database.

D = database
IC = integrity theory
I = constraint
U = update
D(F) = true if F eval to true in D

D(I) = true if I is satisfied in D

D(IC) = true if all I in IC is
satisfied in D

Hendrik, 2011


FORMAL DEFINITIONS

Let be an ordering antisymmetric, reﬂexive and transitive.
For two elements in a lattice A and B, A B is their least upper bound.

Hendrik, 2011


FORMAL DEFINITIONS

We say that (µ, ) is an inconsistency metric if
µ maps tuples (D, IC) to some lattice that is partially ordered by .

Simple example of a metric is given by (D, IC) = D(IC)
with the natural order true f alse of the range of .

That is, integrity sat, D(IC) = true,
mean lower inconsistency than integrity violation,
D(IC) = false.

Non trivial examples given by comparing or
counting violated constraints.

Hendrik, 2011


INCONSISTENCY METRICS

Inconsistency metrics are used to decide if an update preserves
integrity, that is, doesn’t create a integrity violation that
doesn’t exist before the update.

Intuitively, an update preserves integrity if it doesn’t increase
the measured inconsistency

For a metric (µ, ), an update U in a database D
with integrity theory IC is integrity-preserving with
regard to (µ, ) if µ(DU , IC) µ(D, IC).

Hendrik, 2011


AND MORE...

Inconsistency-tolerant integrity checking

Repairs

Computing and checking partial repairs

Computing integrity-preserving repairs

Hendrik, 2011


WHY WE ARE TALKING ABOUT IT?


WHY WE ARE TALKING ABOUT IT?

Lattes@FGV Project (a uniﬁed KB of FGV research
publications, researchers, skills etc), http://dck092.fgv.br/

Semantic Web brings, RDF, description logics, linked data etc.

Our research topics include Logics and knowledge
representation.

RDF are the key concept of Semantic Web

Relational has ﬁxed model (TBOX of an ontology)


TOPOS: THEORETICAL PART
scra
tchi
n g th
e su
rfac
e!
A topos (plural topoi or toposes) is a category with a quite expressive internal logic

The category of graphs and graph-homomorphisms can be viewed as a topos.

This topos already has a Heyting algebra that is used as the truth-basis of its internal logic.

A Heyting algebra is a lattice with additional properties. This topos-theoretic view of RDF
stores can be investigated in order to provide a natural way to provide foundations to
partial repairs in RDF stores.

Besides that, if we view traditional DBs as finite first-order logical structures, the category
of (finite) first-order structures and homomorphism between then has its own internal
logic. This internal logic can be investigated also regarding partial repairs.


LATTES@FGV


LATTES@FGV: THE RDF KB

http://dck092.fgv.br:10035/repositories/fgv (800k triples)


LATTES@FGV

480 CV Lattes and collected data from other sources (Qualis,
Digital Library etc) in one triple store

lots of errors (inconsistencies) for different reasons: poor user
interface for input data, misinterpretation etc.

How to identify the errors? (non ad-hoc matter)

How to ﬁx what can be ﬁxed automatically?


INTEGRITY CONSTRAINTS IN RDF

We can consider the extension of what was discussed so far to
non-SQL

KR/DB can be viewed as a graph

The query language of RDF based stores, SPARQL, can be
used to provide semantics to the store.


EXAMPLES

An article referenced by a CV
must have the author of this CV as
one of its authors!


EXAMPLES

If two resources were identiﬁed by
reference to the same article, every
author of the ﬁrst one should also
be related to the second one!


IN THE LAST EXAMPLE

Of course, two publications cannot be considered
the same comparing only their titles!

We need entity alignment, similarity checker...

Suppose we have identiﬁed all resources that
represent the same real “entity” using ask {
owl:sameAs, than ...   ?p1 owl:sameAs ?p2 ;
  dc:creator ?c .
  OPTIONAL {
  ?p2 ?rel ?c .
  }
  FILTER( !bound(?rel) )
}


A LITTLE BIT ABOUT THE
IDENTIFICATION OF SIMILARITY

(defun assert-same-list (list)
(let ((new nil))
(mapcar (lambda (pair)
(let ((a (first pair))
(b (second pair)))
(if (not (blank-node-p a))
(push (reverse pair) new)
(push pair new))))
list)
(dolist (pair new)
(add-triple (first pair) !owl:sameAs (second pair)))))

(select0/callback (?x ?y) #'insert-same-as
(q- ?x !rdf:type !foaf:Agent)
(q- ?y !rdf:type !foaf:Agent)
(q- ?x !foaf:name ?n)
(q- ?y !foaf:name ?n)
(lispp (upi< ?x ?y)))

Naive approach: Shaking hands!


A LITTLE BIT ABOUT THE
IDENTIFICATION OF SIMILARITY

(defun components (vertices n generator)
(do ((res nil)
(vtx vertices
(set-difference vtx (car res) :test #'upi=)))
((null vtx) res)
(push (ego-group (car vtx) n generator) res)))

(defsna-generator same-journal (node)
(select0 (?j)
(q- (?? node) !bibo:issn ?i)
(q- ?j !bibo:issn ?i)
(lispp (utils::check-issn (part->value ?i)))
(lispp (upi< node ?j))
(q- ?j !dc:title ?t2)
(q- (?? node) !dc:title ?t1)
(lispp (> (utils::jaro-winkler-distance (part->value ?t1) (part->value ?t2)) 0.7))))

(let ((nodes (mapcar #'subject (get-triples-list :p !bibo:issn :limit nil))))
(dolist (g (components nodes 2 'same-journal)))
(merge-nodes g))

An ad-hoc solution: breath-ﬁrst-search of connected components!


A database approach to monitoring the quality of information in RDF stores

Recommended

Recommended

More Related Content

More from Alexandre Rademaker

More from Alexandre Rademaker (12)

Recently uploaded

Recently uploaded (20)

A database approach to monitoring the quality of information in RDF stores