Contextualized Knowledge Repositories for the Semantic Web

Serafini, Luciano; Homola, Martin

doi:10.2139/ssrn.3198952

Cited by 16 publications

(23 citation statements)

References 23 publications

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“…Nevertheless, none of them explicitly defines clear input and out-put interfaces for modules as our approach does. Regarding module inheritance, all of the above approaches support inheritance of knowledge, some [21,10] allow redefinition of knowledge whereas others do not [22,31,9]. Compared to our approach, none of the above approaches supports modification restrictions, i.e., one cannot control modifications to the knowledge in a module.…”

Section: Contextualized Knowledge Representation and Inheritancementioning

confidence: 99%

Rule Module Inheritance with Modification Restrictions

Burgstaller

Neumayr

Sallinger

et al. 2018

Lecture Notes in Computer Science

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Adapting rule sets to different settings, yet avoiding uncontrolled proliferation of variations, is a key challenge of rule management. One fundamental concept to foster reuse and simplify adaptation is inheritance. Building on rule modules, i.e., rule sets with input and output schema, we formally define inheritance of rule modules by incremental modification in single inheritance hierarchies. To avoid uncontrolled proliferation of modifications, we introduce formal modification restrictions which flexibly regulate the degree to which a child module may be modified in comparison to its parent. As concrete rule language, we employ Datalog ± which can be regarded a common logical core of many rule languages. We evaluate the approach by a proof-of-concept prototype.

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Section: Contextualized Knowledge Representation and Inheritancementioning

confidence: 99%

Rule Module Inheritance with Modification Restrictions

Burgstaller

Neumayr

Sallinger

et al. 2018

Lecture Notes in Computer Science

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“…Following [17], we formalize context as a mathematical object. Basically, we follow the approach in [19] of contextualizing knowledge bases by adopting the metaphor of considering context as a box [4,13]. Specifically, this means that the knowledge represented by the TBox (together with the ABox) in a certain context is affected by the values of parameters used to characterize the context itself.…”

Section: Contextualizing Knowledge Basesmentioning

confidence: 99%

“…An important aspect in modeling context is related to the choice of which kind of information is considered to be fixed and which context dependent. Specifically, for DLs, one can define the assertions in the TBox [1,11], the concepts [3], or both [19,14] as context-dependent. Each choice addresses different needs, and results in differences in the complexity of reasoning.…”

Section: Introductionmentioning

confidence: 99%

Verification of Context-Sensitive Knowledge and Action Bases

Calvanese

Ceylan

Montali

et al. 2014

Logics in Artificial Intelligence

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Abstract. Knowledge and Action Bases (KABs) have been recently proposed as a formal framework to capture the dynamics of systems which manipulate Description Logic (DL) Knowledge Bases (KBs) through action execution. In this work, we enrich the KAB setting with contextual information, making use of different context dimensions. On the one hand, context is determined by the environment using context-changing actions that make use of the current state of the KB and the current context. On the other hand, it affects the set of TBox assertions that are relevant at each time point, and that have to be considered when processing queries posed over the KAB. Here we extend to our enriched setting the results on verification of rich temporal properties expressed in μ-calculus, which had been established for standard KABs. Specifically, we show that under a run-boundedness condition, verification stays decidable and does not incur in any additional cost in terms of worst-case complexity. We also show how to adapt syntactic conditions ensuring run-boundedness so as to account for contextual information, taking into account context-dependent activation of TBox assertions.

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“…The need for explicit treatment of context has been also broadly acknowledged by the Semantic Web community and introduced in diverse aspects over different Semantic Web knowledge representation frameworks [11,23,12,7,39,41,45]. Particularly interesting here are the efforts on representing and reasoning with meta-level descriptions of knowledge, such as involved in our axioms of type C :ϕ, where C can be seen as a meta-level annotation of the assertion ϕ.…”

Section: Related Workmentioning

confidence: 99%

Description logics of context

Klarman¹,

Gutiérrez-Basulto

2013

J Logic Computation

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We introduce Description Logics of Context (DLCs)-an extension of Description Logics (DLs) for context-based reasoning. Our approach descends from J. McCarthy's tradition of treating contexts as formal objects over which one can quantify and express first-order properties. DLCs are founded in two-dimensional possible world semantics, where one dimension represents a usual object domain and the other a domain of contexts, and accommodate two interacting DL languages-the object and the context language-interpreted over their respective domains. Effectively, DLCs comprise a family of two-sorted, two-dimensional combinations of pairs of DLs. We argue that this setup ensures a well-grounded, generic framework for capturing and studying mechanisms of contextualization in the DL paradigm. As the main technical contribution, we prove 2ExpTime-completeness of the satisfiability problem in the maximally expressive DLC, based on the DL SHIO. As an interesting corollary, we show that under certain conditions this result holds also for a range of two-dimensional DLs, including the prominent (Kn)ALC.

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Contextualized Knowledge Repositories for the Semantic Web

Cited by 16 publications

References 23 publications

Rule Module Inheritance with Modification Restrictions

Rule Module Inheritance with Modification Restrictions

Verification of Context-Sensitive Knowledge and Action Bases

Description logics of context

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