Complexity of the Description Logic ALCM

Complexity of the Description Logic ALCM

Proceedings, Fifteenth International Conference on Principles of Knowledge Representation and Reasoning (KR 2016) Complexity of the Description Logic ALCM Monica´ Martinez and Edelweis Rohrer Paula Severi Instituto de Computacion,´ Facultad de Ingenier´ıa, Department of Computer Science, Universidad de la Republica,´ Uruguay University of Leicester, England Abstract in ALCM can be used for a knowledge base in ALC. Details of our ExpTime algorithm for ALCM along with In this paper we show that the problem of deciding the consis- proofs of correctness and the complexity result can be found tency of a knowledge base in the Description Logic ALCM is ExpTime-complete. The M stands for meta-modelling as in (Martinez, Rohrer, and Severi 2015). defined by Motz, Rohrer and Severi. To show our main result, we define an ExpTime Tableau algorithm as an extension of 2 A Flexible Meta-modelling Approach an algorithm for ALC by Nguyen and Szalas. A knowledge base in ALCM contains an Mbox besides of a Tbox and an Abox. An Mbox is a set of equalities of the form 1 Introduction a =m A where a is an individual and A is a concept (Motz, The main motivation of the present work is to study the com- Rohrer, and Severi 2015). Figure 1 shows an example of two plexity of meta-modelling as defined in (Motz, Rohrer, and ontologies separated by a horizontal line, where concepts are Severi 2014; 2015). No study of complexity has been done denoted by large ovals and individuals by bullets. The two so far for this approach and we would like to analyse if it ontologies conceptualize the same entities at different lev- increases the complexity of a given description logic. els of granularity. In the ontology above the horizontal line, The standard tableau algorithm for ALC which builds com- rivers and lakes are formalized as individuals while in the pletion trees, e.g. see (Baader et al. 2003), can be extended one below the line they are concepts. If we want to integrate with the expansion rules for meta-modelling of (Motz, these ontologies into a single ontology it is necessary to in- Rohrer, and Severi 2015). However, it has a high (worse terpret the individual river and the concept River as the case) complexity, namely NExpTime, and cannot be used to same real object. Similarly for lake and Lake. The Mbox prove that the consistency problem for ALCM is ExpTime- for this example is: complete. In our approach we define a flexible syntax to river =m River lake =m Lake equate an individual to a concept, with a strong semantics which ensures that the interpretation of the individual coin- These equalities are called meta-modelling axioms and in cides with that of the concept. So, the domain of an inter- this case, we say that the ontologies are related through pretation can no longer consist of only basic objects, but it meta-modelling. In Figure 1, meta-modelling axioms are has to be a well-founded set. Unlike other approaches in the represented by dashed edges. After adding the meta- literature, in our approach, general translations to the con- modelling axioms, the concept HydrographicObject is sistency of a DL without metamodelling does not work be- now also a meta-concept because it is a concept that con- cause our reasoner has to check inside the algorithm that the tains an individual which is also a concept. canonical model is well-founded. This kind of meta-modelling can be expressed in the unde- It is well-known that consistency of a (general) knowledge cidable logic of OWL Full (Motik 2007) but it cannot be base in ALC is ExpTime-complete (Schild 1991; De Gia- expressed in OWL DL. como and Lenzerini 1996). The main contribution of this OWL 2 DL has a very restricted form of meta-modelling paper is to show that the consistency problem for ALCM called punning where the same identifier can be used as an is ExpTime-complete too. So, complexity does not change individual and as a concept (Hitzler, Krotzsch,¨ and Rudolph when moving from ALC to ALCM. For proving our result, 2009). We next illustrate two examples where OWL would we define a tableau algorithm for checking consistency as an not detect inconsistencies because the identifiers, though extension of an algorithm for ALC by (Nguyen and Szalas they look syntactically equal, are treated as different objects. 2009), and prove that it is ExpTime. Hardness follows triv- ially from the fact that ALCM is an extension of ALC since Example 1 If we introduce an axiom expressing that Hy- any algorithm that decides consistency of a knowledge base drographicObject is a subclass of River, then OWL’s rea- soner will not detect that the interpretation of River is not Copyright c 2016, Association for the Advancement of Artificial a well founded set (it is a set that belongs to itself). Intelligence (www.aaai.org). All rights reserved. 585 Abox Tbox HydrographicObject(river) River Lake ⊥ HydrographicObject(lake) River(queguay) Mbox River(santaLucia) river =m River Lake(deRocha) lake =m Lake Lake(delSauce) Figure 2: Tbox, Abox and Mbox for Figure 1 also need to be able to express equality and difference be- tween the corresponding individuals. If we have an equality A ≡ B between concepts then a and b should be equal. So, without equalities, the language lacks expressibility for do- K|= = Figure 1: Two ontologies on Hydrography ing inferences of the form a b. Similarly, if we have that A and B are different, (i.e. there exists an element in A that is not in B, since inequalities cannot be expressed Example 2 We add two axioms, the first one says that river by axioms in the Tbox), then a and b should be different. and lake as individuals are equal and the second one says In our semantics this “correspondence” is in both directions that the classes River and Lake are disjoint. Then OWL’s (from individuals to concepts and viceversa), it is what we reasoner does not detect that there is a contradiction. call Equality Transference (Motz, Rohrer, and Severi 2015). ALCM In order to detect these inconsistencies, river and River Definition 1 (Model of a Knowledge Base in ) I should be made semantically equal, i.e. the interpretations An interpretation is a model of a knowledge base K =(T A M) ALCM of the individual river and the concept River should be the , , in if the following holds: same. The domain Δ can no longer consist of only basic 1. the domain Δ of the interpretation is a subset of some Sn objects and cannot be an arbitrary set either. We require that where Sn is defined by starting from an arbitrary set S0 the domain be a well-founded set. The reason for this is ex- of atomic objects and by giving as inductive step Sn+1 = I plained as follows. Suppose we have a domain Δ = {X} Sn ∪P(Sn). where X = {X}. Intuitively, X is the set {{{...}}} which 2. I is a model of (T , A) in ALC. I I is the solution of a recursive equation obtained by unfolding 3. a = A for all a =m A ∈M. it an infinite number of times. Clearly, a set like X cannot In the first part of Definition 1 we restrict the domain of an represent any real object from our usual applications in Se- ALCM mantic Web. The well-foundness of our model is guaranteed interpretation in to be a subset of Sn, which can by the reasoner which checks for circularities. now contain sets since the set Sn is defined recursively. It is easy to prove that Sn is well-founded for all n ∈ N. Our approach allows the user to have any number of levels or ALC layers (meta-concepts, meta meta-concepts and so on). The The second part of Definition 1 refers to the - user does not have to write or know the layer of the concept knowledge base without the Mbox axioms. because the reasoner will infer it for him. In this way, ax- The third part of the definition restricts the interpretation ioms can also naturally mix elements of different layers and of an individual that has a corresponding concept through the user has the flexibility of changing the status of an indi- meta-modelling to be equal to the concept interpretation. vidual at any point without having to make any substantial Figure 3 shows a model for the knowledge base of Figure 2. change to the ontology. Definition 2 (Consistency in ALCM) We say that a knowledge base K =(T , A, M) is consistent (satisfiable) 3 The Description Logic ALCM if there exists a model of K. In this section, we extend the description logic ALC An algorithm for checking consistency gives only one model (Schmidt-Schauß and Smolka 1991; Baader et al. 2003) with amongst many that, depending on the choices, e.g. the ap- meta-modelling (Motz, Rohrer, and Severi 2014; 2015). plication of the or rule, may or may not be well-founded. A knowledge base K in ALCM is a triple (T , A, M) where A (general) reduction from the consistency of a DL with T , A and M are a Tbox, Abox and an Mbox respectively. meta-modelling to the consistency of a DL without meta- An Mbox M is a finite set of meta-modelling axioms. A modelling does not work. So, checking for circularities meta-modelling axiom is a statement of the form a =m A has to be done inside the algorithm.

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