=Paper=
{{Paper
|id=None
|storemode=property
|title=Evolution of OWL 2 QL Knowledge Bases: From Inexpressibility to Practical Approaches
|pdfUrl=https://ceur-ws.org/Vol-2576/paper13.pdf
|volume=Vol-2576
|authors=Evgeny Kharlamov,Dmitriy Zheleznyakov,Werner Nutt,Diego Calvanese
}}
==Evolution of OWL 2 QL Knowledge Bases: From Inexpressibility to Practical Approaches==
https://ceur-ws.org/Vol-2576/paper13.pdf
Evolution of OWL 2 QL Knowledge Bases:
From Inexpressibility to Practical Approaches
Evgeny Kharlamov1,2 , Dmitriy Zheleznyakov3 , Werner Nutt4 , and Diego Calvanese4
1
Bosch Centre for Artificial Intelligence, Germany, evgeny.kharlamov@de.bosch.com
2
University of Oslo, Norway, evgeny.kharlamov@ifi.uio.no
3
Ocado Technology, UK, zheleznyakov.dmitry@gmail.com
4
Free University of Bozen-Bolzano, Italy, fname.lname@unibz.it
Abstract. Knowledge bases (KBs) are not static entities: new information con-
stantly appears and some of the previous knowledge becomes obsolete. In order
to reflect this evolution of knowledge, KBs should be expanded with the new
knowledge and contracted from the obsolete one. This problem is well-studied
for propositional but much less for first-order KBs. In this work we investi-
gate knowledge expansion and contraction for KBs expressed in OWL 2 QL, a
tractable fragment of the Web Ontology Language OWL 2. We start with a novel
knowledge evolution framework and natural postulates that evolution should re-
spect, and compare our postulates to the well-established AGM postulates. We
then review well-known model and formula-based approaches for expansion and
contraction for propositional theories and show how they can be adapted to the
case of OWL 2 QL. In particular we show inexpressibility challenges for the for-
mer and practical algorithms for the latter approaches.
1 Motivation
Ontology Web Language (OWL) provides excellent mechanisms for representing struc-
tured knowledge as knowledge bases (KBs). OWL is the standard ontology language of
the Semantic Web. KBs have been successfully used in various applications including
Web search [19,1,2], and search over KGs [5,37,40,39,38], Medicine [3], Media [31],
E-commerce [8], data integration [23,25,20,35] and industrial modelling [22] and an-
alytics [26,24,34,33]. In these and other applications KBs naturally change over time
and thus KB management systems should be equipped with services to support KB
evolution [13].
In KB evolution the task is to incorporate new knowledge N into an existing KB
K, or to delete some obsolete knowledge N from K, in order to take into account
changes that occur in the underlying domain of interest [21]. The former evolution task
is typically referred to as knowledge expansion and the latter as contraction. In general,
the new (resp., obsolete) knowledge is represented by a set of formulas denoting those
properties that should be true (resp., false) after the ontology has evolved. In the case
where the new knowledge interacts in an undesirable way with the knowledge in the on-
tology, e.g., by causing the ontology or relevant parts of it to become unsatisfiable, the
new knowledge cannot simply be added to the ontology. Instead, suitable changes need
to be made in the ontology so as to avoid the undesirable interaction, e.g., by deleting
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Evgeny Kharlamov, Dmitriy Zheleznyakov, Werner Nutt, and Diego Calvanese
parts of the ontology that conflict with the new knowledge. Different choices are possi-
ble, corresponding to different semantics for knowledge evolution [4,41,21,11,12,36].
The main two types of semantics that were proposed for the case of propositional
knowledge are model-based [41] and formula-based [11]. In model-based semantics
the idea is to resolve the undesirable interaction at the level of models of K and N . For
example, in model-based expansion the result of evolution are those models of N that
are minimally distant from the ones of K, where a suitable notion of distance needs to
be chosen, possibly depending on the application. In formula-based semantics the idea
is to do evolution at the level of the deductive closure of the formulae from K and N .
Since many (possibly counter-intuitive) semantics can be defined within the model or
formula-based paradigm, a number of evolution postulates [21,11] have been proposed
and they define natural properties a semantics should respect. It is thus common to
verify for each evolution semantics whether it satisfies the postulates.
For the case of propositional knowledge, there is a thorough understanding of se-
mantics as well as of computational properties of both expansion and contraction. The
situation is however much less clear when it comes to DL KBs, which are decidable
first-order logic theories. Differently from the propositional case, in general they admit
infinite sets of models and infinite deductive closures. Moreover, going from proposi-
tional letters to first-order predicates and interpretations, on the one hand calls for novel
postulates underlying the semantics of evolution, and on the other hand broadens the
spectrum of possibilities for defining such semantics. A number of attempts have been
made to adapt approaches for the evolution of propositional knowledge to the case of
DLs, cf. [12,10,36,32]. However, there is no thorough understanding of evolution from
the foundational point of view even for DLs with the most favorable computational
properties, such as the logics of the OWL 2 QL [7] and EL [6] families, which are at
the basis of two tractable fragments of OWL 2.
2 Contributions
In this work we address this problem and propose an exhaustive study of evolution for
OWL 2 QL. In particular, we address the problem considering three dimensions:
1. knowledge evolution tasks: we study how knowledge can be expanded or con-
tracted;
2. type of evolution semantics: we study model-based and formula-based semantics;
3. evolution granularity: we study when evolution affects the TBox (for terminological
knowledge), or the ABox (for assertional knowledge), or both of them.
We provide the following contributions [43]:
– We propose a knowledge expansion and contraction framework that accounts for
TBox, ABox, and general KB evolution.
– We propose natural evolution postulates and show how they are related to the well-
known AGM postulates [21].
– We show how one can rigorously extend propositional model-based evolution se-
mantics to the first-order case, defining a 5-dimensional space of possible options,
Evolution of OWL 2 QL Knowledge Bases 3
comprising 3 · 24 model-based evolution semantics for DLs that essentially include
all previously proposed model-based approaches for DLs. These dimensions are:
(1) ABox vs. TBox vs. general evolution; (2) expansion vs. contraction; (3) global
vs. local; (4) symbol vs. atom; (5) set inclusion vs. cardinality. 5 For most of these
semantics and the case of OWL 2 QL KBs we prove negative expressibility results:
in general evolution of OWL 2 QL KBs cannot be expressed as a OWL 2 QL KB.
– We investigate formula-based evolution for OWL 2 QL. In particular, for known
formula-based evolution approaches [11] we show intractability of computing evo-
lution results for OWL 2 QL KBs. Moreover, we propose a non-deterministic ap-
proach for general KB evolution, which turns out to become deterministic for ABox
evolution; for both cases we develop polynomial-time algorithms.
3 Illustration of Contributions
We now exemplify the inexpressibility of evolution results under model-based seman-
tics and then show how the same examples can be solved with formula based semantics.
3.1 Illustration of Inexpressibility
In order to understand why model-based approaches to evolution are problematic for
OWL 2 QL recall the following property of the logic. Let M be a set of interpretations
such that there are OWL 2 QL assertions ϕ, ψ such that
– J |= ϕ ∨ ψ for every J ∈ M, and
– there are Jϕ , Jψ ∈ M such that Jϕ 6|= ϕ and Jψ 6|= ψ.
Then, there is no OWL 2 QL KB K such that M = Mod(K).
We now illustrate this phenomenon on several evolution scenarios. We will do it
on the intuitive level and without referring to concrete model-based semantics. Formal
details can be found in [43].
Consider a KB where the structural knowledge is that wives (concept Wife) are
exactly those individuals who have husbands (role HasHusband) and that some wives
are employed (concept EmpWife). Bachelors (concept Bachelor) cannot be husbands.
Priests (concept Priest) are clerics (concept Cleric) and clerics are bachelors. Both
clerics and wives are receivers of rent subsidies (concept Renter). We also know that
adam and bob are priests, mary is a wife who is employed and her husband is john.
Also, carl is a catholic minister (concept Minister).
This knowledge can be expressed in OWL 2 QL by the KB Kex , consisting of the
following TBox T and ABox A:
T = { Wife v ∃HasHusband, ∃HasHusband v Wife,
EmpWife v Wife, Bachelor v ¬∃HasHusband− ,
Priest v Cleric, Cleric v Bachelor,
Cleric v Renter, Wife v Renter }
A = { Priest(adam), Priest(bob), EmpWife(mary), HasHusband(mary, john) }
5
Note that our proposed model-based semantics can be applied to any description logic.
4 Evgeny Kharlamov, Dmitriy Zheleznyakov, Werner Nutt, and Diego Calvanese
In the TBox expansion scenarios the new information NT can state that wives are
not renters anymore or that priests are not renters anymore:
NT = {Priest v ¬Renter}, or NT = {Wife v ¬Renter}.
In both cases the inexpressibility phenomenon holds. Indeed, in the first case since
priests are not renters anymore, in each model of the evolution result both axioms ϕ =
(Priest v Cleric), ψ = (Cleric v Renter) cannot hold at the same time, but,
due to the minimality of change principle, at least one of them should hold. The second
case is analogous but with ϕ = (EmpWife v Renter) and ψ = (Wife v Renter).
These two cases hold for different model-based semantics and nicely illustrate that the
inexpressibility property affects evolution at the TBox level even when there is a rather
simple interaction between atomic concepts such as A v B v C or A v B and
A v C. The same effect can be also shown for TBox contraction: instead of adding
Priest v ¬Renter to the example KB, one can contract the KB with the axiom
Priest v Renter and essentially the same argument for inexpressibility will hold.
In the ABox expansion scenario the new information Ne consider the case when
John becomes a priest, that is,
NA = {Priest(john)}.
The example TBox entails that in this case Mary cannot be the wife of John anymore:
John becomes a bachelor who cannot be a husband. In this case one can show that
in each model of the evolution result the disjunction of the two ABox axioms ϕ =
Priest(bob) and ψ = Priest(adam) holds but there are models where one of the two
holds but not the other, that is, when either Bob or Adam becomes the new husband of
Mary. This again leads to the inexpressibility of the evolution result.
3.2 Illustration of Bold Semantics
Given a KB and new knowledge that should be added to the KB, Bold Semantics essen-
tially takes a maximal subset of axioms (entailed) from the KB that together with the
new knowledge is satisfiable. There clearly may be more than one such maximal sub-
set and thus Bold Semantics is non-deterministic for OWL 2 QL. On the positive side,
computation of evolution results under Bold Semantics can be done in time polynomial
in the size of the KB. Moreover, one can show that if the evolution of OWL 2 QL KBs
affects only the ABox level, then the evolution result is always is unique.
Following our example, evolution with any of the NT should delete either ϕ or ψ
from the original KB, thus there is non-determinism. While, for NA evolution is unique
and it requires to drop from the original KB the fact that Mary is a wife of john but add
that she is a wife of someone ∃HasHusband(mary).
3.3 Postulates
Postulates are basic principles that describe the rational behind knowledge evolution.
They are typically defined independently from the actual approaches or algorithms to
Evolution of OWL 2 QL Knowledge Bases 5
compute evolution results. Moreover, in order to make sure that a defined approach
(algorithm) makes sense one typically verifies whether it confirms some postulates.
The classical AGM postulates for knowledge evolution have originally be defined for
the case of propositional knowledge. At the same time, the ’granularity’ of knowledge
changes when moving from propositional to Description Logics: the atomic statements
of a DL, namely the ABox and TBox axioms, are more complex than the atoms of
propositional logic. On the other hand, a set of propositional formulas makes sense,
intuitively, if it is satisfiable, while a KB can be satisfiable, but incoherent, that is, one
or more concepts are necessarily empty. Therefore, we proposed new postulates for
expansion and contraction, to be adopted in the context of evolution on the Semantic
Web. Moreover, we showed how our postulates are related to the AGM ones and showed
that our evolution operators satisfy the proposed postulates. Due to lack of space we
now present only postulates for knowledge expansion.
Let K be the KB and Ke0 the result of its expansion with new knowledge Ne .
E1: Expansion should preserve the coherence of the KB, that is, if K is coherent, then
so is Ke0 .
E2: Expansion should entail all new knowledge, that is, Ke0 |= Ne .
E3: Expansion with old information should not affect the KB, that is, if K |= Ne , then
Ke0 ≡ K.
E4: The union of N2e with the expansion of K with N1e implies the expansion of K
with N1e ∪ N2e .
E5: Expansion should not depend on the syntactical representation of knowledge, that
0 0
is, if K1 ≡ K2 and N1e ≡ N2e , then K1e ≡ K2e .
4 Discussion of Contributions
The first important conclusion from our work is that model-based approaches are in-
trinsically problematic for KB evolution, even in the case of such a lightweight DL as
OWL 2 QL. Indeed, recall that OWL 2 QL is not closed under evolution for any of
the model-based semantics and thus these semantics are impractical. As a consequence,
one has either to search for conceptually different semantics that rely on other princi-
ples of ‘composing’ the output set of models constituting the evolution result, or one
has to develop natural restrictions on how model-based approaches can ‘compose’ this
set. An alternative approach would be to develop approximation techniques that allow
one to efficiently capture evolution results.
A second important conclusion is that classical formula-based approaches are too
heavyweight from the computational point of view and thus their practicality is ques-
tionable. On the other hand, the most conceptually simple model-based semantics such
as bold semantics can potentially lead to practical evolution algorithms. However, their
practicality requires further empirical evaluation. Finally, we have discussed that the
classical evolution postulates that were originally developed for propositional theories
are not directly applicable to the case of first-order knowledge since they are blind to
some fundamental properties of such knowledge, such as coherency. We have shown
how to adapt such postulates to the richer setting considered here, and have analyzed
6 Evgeny Kharlamov, Dmitriy Zheleznyakov, Werner Nutt, and Diego Calvanese
whether the various model-based and formula-based semantics satisfy the revised pos-
tulates.
We believe that our work opens new avenues for research in the area of knowledge
evolution, which is an important part of knowledge engineering, since it shows how to
lift approaches to knowledge evolution from the propositional to the first-order case.
Moreover, we have presented techniques that allow one to prove inexpressibility of
model-based evolution, and coNP-hardness of formula-based evolution. We believe that
these techniques can be relevant to knowledge management tasks beyond evolution.
5 Future Work
We see several important directions for future work. First, the problem of expressibility
in OWL 2 QL is still open for various model-based evolution semantics (see Table 1).
These settings are all for ABox expansion and contraction under global model-based
semantics. An important research direction is to apply in practice the ideas we devel-
oped and, in particular, to implement an ontology evolution system. The system can be
based on formula-based approaches and implement Algorithms 1–4 that we proposed.
Such system could also be based on approximations of model-based semantics, which
are out of the scope of this paper, see, e.g., [30,28,27,29,28,10,17,18]. Then, it would
be interesting to conduct an empirical evaluation for various semantics, in order to es-
tablish which semantics give more intuitive results from the users’ point of view, and
which ABox evolution approaches are more scalable. A further direction to investigate
is to identify the minimum extensions of OWL 2 QL that would allow it to capture
the results of model-based evolution for OWL 2 QL KBs. For this, one can draw in-
spiration from the work in [32]. Also, it is still unknown what are minimal DLs that
are closed under local model-based evolution, and in general that are well tailored to-
wards model-based approaches. Then, knowledge evolution has important implications
to privacy: one should make sure that changes in knowledge do not make violations in
access control policies. This is a non-trivial task since, e.g., new knowledge can inter-
act with the old one in such a way that a person without access rights to a particular
knowledge can derive such knowledge from this combination [16,14,15]. Finally, we
believe that it is important to develop knowledge evolution techniques where the user
has a much better control over the evolution process. For this, one can draw inspiration
from previous work, e.g., from [9], where the authors proposed techniques to control
what syntactic structures of a given KB cannot be changed by the evolution process, or
from [42], where the authors proposed to combine knowledge evolution with models of
trust, i.e., the new knowledge in their approach is only partially trusted (note that this
scenario inherits the inexpressibility issues of MBAs).
Acknowledgements This work was partially funded by the SIRIUS Centre, Norwe-
gian Research Council project number 237898; by the UNIBZ projects PARCIS and
TaDaQua, and by the “European Region Tyrol-South Tyrol-Trentino” (EGTC) under
the first call for basic research projects within the Euregio Interregional Project Net-
work IPN12 “Knowledge-Aware Operational Support” (KAOS).
Evolution of OWL 2 QL Knowledge Bases 7
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