Formal foundations for RDF/S KB evolution

Abstract

Abstract-There are ongoing efforts to provide declarative formalisms of integrity constraints over RDF/S data. In this context, addressing the evolution of RDF/S knowledge bases while respecting associated constraints is a challenging issue, yet to receive a formal treatment. We provide a theoretical framework for dealing with both schema and data change requests. We define the notion of a rational change operator as one that satisfies the belief revision principles of Success, Validity and Minimal Change. The semantics of such an operator are designed to be subject to customization, by tuning the properties that a rational change should adhere to. We prove some interesting theoretical results and propose a general-purpose algorithm for implementing rational change operators in knowledge bases with integrity constraints, which allows us to handle uniformly any possible change request in a provably rational and consistent manner. Then, we apply our framework to a well-studied RDF/S variant, for which we suggest a specific notion of minimality. For efficiency purposes, we also describe specialized versions of the general evolution algorithm for the RDF/S case, which provably have the same semantics as the general-purpose one for a limited set of (useful in practice) types of change requests.

Resolving conflicts in knowledge for ambient intelligence

“…A formal method for applying changes in the presence of custom validity rules was proposed by Konstantinidis et al (2008aKonstantinidis et al ( , 2008b and Flouris et al (2013), where the incorporation of changes is performed automatically, taking care that the validity rules are not violated at the end of the process (see also the discussion on invalidity in Section 3.4.2).…”

“…EvoPat (Rieß et al, 2010) ( Konstantinidis et al, 2008aKonstantinidis et al, , 2008bFlouris et al, 2013) (Djedidi & Aufaure, 2009 RDF/S Custom Automatic RUL (Magiridou et al, 2005) RDF/S (data only) Custom Automatic (Roger et al, 2002;Liu et al, 2006) DL Coherence consistency Automatic (Lee & Meyer, 2004) ALU DL Consistency Belief change (Halaschek-Wiener & Katz, 2006) OWL Consistency Belief change (Ribeiro & Wassermann, 2007) Logics without negation Consistency Belief change (Gutierrez et al, 2006) RDF/S Consistency Belief change (Flouris et al, 2004(Flouris et al, , 2005(Flouris et al, , 2006a(Flouris et al, , 2006bFlouris, 2006aFlouris, , 2006bFlouris & Plexousakis, 2006 definition of custom (provided by the user) constraints to be respected during evolution, whereas others focus on preserving coherence and/or consistency. Finally, the fourth column determines the resolution method, which can be manual, semi-automatic, or automatic; approximate; or inspired by/reusing other methodologies/fields, such as belief change approaches or the maxi-adjustment algorithm.…”

Ontology evolution: a process-centric survey

“…Moreover, validation during changes often requires that the changes performed to guarantee validity are ‘minimal’ (per the Principle of Minimal Change; Alchourron et al ., 1985), in the sense of having minimal effects on the ontology. Note that, even though various works have tried to quantify the ‘impact’ of a change, or to define what ‘minimality’ is, in various contexts (e.g., Alchourron et al ., 1985; Gärdenfors, 1992; Flouris et al ., 2006; Konstantinidis et al ., 2008a, 2008b; Ribeiro et al ., 2009; Flouris et al ., 2013), this notion is, in principle, application dependent. Finally, in most cases, we also want the original change to be actually applied to the ontology, that is, we do not want the process of resolving the invalidity to ‘undo’ one of the changes that the original evolution operation caused; this is called the Principle of Success.…”

“…A formal method for applying changes in the presence of integrity constraints appears in Konstantinidis et al (2008aKonstantinidis et al ( , 2008b and Flouris et al (2013). This work considers explicitly the three principles described above (validity, minimal change and success) and automatically determines the actions to be taken to resolve invalidities created by the update.…”

“…EvoPat (Riess et al, 2010;Konstantinidis et al, 2008aKonstantinidis et al, , 2008bFlouris et al, 2013;Djedidi & Aufaure, 2009, 2010 RDF/S Custom Automatic RUL (Magiridou et al, 2005) RDF/S (data only) Custom Automatic Moguillansky et al (2008), Liu et al (2006), Roger et al (2002), DL Coherence consistency Automatic Lee and Meyer (2004) ALU DL Consistency Belief revision Halaschek-Wiener and Katz (2006) OWL Consistency Belief revision Ribeiro and Wassermann (2007) Languages with no negation…”