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Evolving multi-context systems
, 2014
"... Abstract. Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in hetero-geneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. In this paper, we introduce evolving Multi-Context Systems ..."
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Cited by 6 (4 self)
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Abstract. Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in hetero-geneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. In this paper, we introduce evolving Multi-Context Systems (eMCSs), a general and flexible framework which inherits from mMCSs the ability to in-tegrate knowledge represented in heterogeneous KR formalisms, and at the same time is able to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorpo-rating new knowledge. We show that eMCSs are indeed very general and expressive enough to capture several existing KR approaches that model dynamics of knowledge. 1
Evolving bridge rules in evolving multi-context systems
, 2014
"... In open environments, agents need to reason with knowledge from various sources, represented in different languages. Managed Multi-Context Systems (mMCSs) allow for the integration of knowledge from different heterogeneous sources in an effective and modular way, where so-called bridge rules expres ..."
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Cited by 3 (3 self)
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In open environments, agents need to reason with knowledge from various sources, represented in different languages. Managed Multi-Context Systems (mMCSs) allow for the integration of knowledge from different heterogeneous sources in an effective and modular way, where so-called bridge rules express how information flows between the contexts. The problem is that mMCSs are essentially static as they were not designed to run in a dynamic scenario. Some recent approaches, among them evolving Multi-Context Systems (eMCSs), extend mMCSs by allowing not only the ability to integrate knowledge represented in heterogeneous KR formalisms, but at the same time to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. These approaches, however, only consider the dynamics of the knowledge bases, whereas the dynamics of the bridge rules, i.e., the dynamics of how the information flows, is neglected. In this paper, we fill this gap by building upon the framework of eMCSs by further extending it with the ability to up-date the bridge rules of each context taking into account an incoming stream of observed bridge rules. We show that several desirable proper-ties are satisfied in our framework, and that the important problem of consistency management can be dealt with in our framework.
Nonmonotonic nominal schemas revisited
- In Procs. of DL. CEUR-WS.org
, 2015
"... Abstract. Recently, a very general description logic (DL) that extends SROIQ (the DL underlying OWL 2 DL) at the same time with nominal schemas and epistemic modal operators has been proposed, which encompasses some of the most prominent monotonic and non-monotonic rule languages, including Datalog ..."
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Abstract. Recently, a very general description logic (DL) that extends SROIQ (the DL underlying OWL 2 DL) at the same time with nominal schemas and epistemic modal operators has been proposed, which encompasses some of the most prominent monotonic and non-monotonic rule languages, including Datalog under the answer set semantics. A decidable fragment is also presented, but the restricted language does not fully cover all formalisms encompassed by the complete language. In this paper, we aim to remedy that by studying an alternative set of restrictions to achieve decidability, and we show that the existing embeddings of the formalisms covered by the full language can be adjusted accordingly.
On Efficient Evolving Multi-Context Systems
"... Abstract. Managed Multi-Context Systems (mMCSs) provide a general frame-work for integrating knowledge represented in heterogeneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the p ..."
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Cited by 1 (1 self)
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Abstract. Managed Multi-Context Systems (mMCSs) provide a general frame-work for integrating knowledge represented in heterogeneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the pres-ence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. However, the general complexity of such an expressive formal-ism may simply be too high in cases where huge amounts of information have to be processed within a limited short amount of time, or even instantaneously. In this paper, we investigate under which conditions eMCSs may scale in such situations and we show that such polynomial eMCSs can be applied in a practical use case. 1
Towards Efficient Evolving Multi-Context Systems (Preliminary Report)
"... Abstract. Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in hetero-geneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the p ..."
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Cited by 1 (1 self)
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Abstract. Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in hetero-geneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. However, the general complexity of such an expressive formalism may simply be too high in cases where huge amounts of information have to be processed within a limited short amount of time, or even instantaneously. In this paper, we investigate under which conditions eMCSs may scale in such situations and we show that such polynomial eMCSs can be applied in a practical use case. 1
On minimal change in evolving multi-context systems (preliminary report
- in ReactKnow 2014
, 2014
"... Abstract. Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in hetero-geneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. Some recent approaches, among them evolving Multi-Context S ..."
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Abstract. Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in hetero-geneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. Some recent approaches, among them evolving Multi-Context Systems (eMCSs), extend mMCSs by allowing not only the ability to integrate knowl-edge represented in heterogeneous KR formalisms, but at the same time to both react to, and reason in the presence of commonly tempo-rary dynamic observations, and evolve by incorporating new knowl-edge. The notion of minimal change is a central notion in dynamic scenarios, specially in those that admit several possible alternative evolutions. Since eMCSs combine heterogeneous KR formalisms, each of which may require different notions of minimal change, the study of minimal change in eMCSs is an interesting and highly non-trivial problem. In this paper, we study the notion of minimal change in eMCSs, and discuss some alternative minimal change criteria. 1
Reasoning over Ontologies and Non-monotonic Rules
"... Abstract. Ontology languages and non-monotonic rule languages are both well-known formalisms in knowledge representation and reasoning, each with its own distinct benefits and features which are quite orthogonal to each other. Both ap-pear in the Semantic Web stack in distinct standards – OWL and RI ..."
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Abstract. Ontology languages and non-monotonic rule languages are both well-known formalisms in knowledge representation and reasoning, each with its own distinct benefits and features which are quite orthogonal to each other. Both ap-pear in the Semantic Web stack in distinct standards – OWL and RIF – and over the last decade a considerable research effort has been put into trying to provide a framework that combines the two. Yet, the considerable number of theoretical approaches resulted, so far, in very few practical reasoners, while realistic use-cases are scarce. In fact, there is little evidence that developing applications with combinations of ontologies and rules is actually viable. In this paper, we present a tool called NoHR that allows one to reason over ontologies and non-monotonic rules, illustrate its use in a realistic application, and provide tests of scalability of the tool, thereby showing that this research effort can be turned into practice. 1
Extending NoHR for OWL 2 QL∗
"... The Protége ́ plug-in NoHR allows the user to com-bine an OWL 2 EL ontology with a set of non-monotonic (logic programming) rules – suitable, e.g., to express defaults and exceptions – and query the combined knowledge base (KB). The formal approach realized in NoHR is polynomial (w.r.t. data comple ..."
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The Protége ́ plug-in NoHR allows the user to com-bine an OWL 2 EL ontology with a set of non-monotonic (logic programming) rules – suitable, e.g., to express defaults and exceptions – and query the combined knowledge base (KB). The formal approach realized in NoHR is polynomial (w.r.t. data complexity) and it has been shown that even very large health care ontologies, such as SNOMED CT, can be handled. As each of the tractable OWL profiles is motivated by different ap-plication cases, extending the tool to the other pro-files is of particular interest, also because these pre-serve the polynomial data complexity of the com-bined formalism. Yet, a straightforward adaptation of the existing approach to OWL 2 QL turns out to not be viable. In this paper, we provide the non-trivial solution for the extension of NoHR to OWL 2 QL by directly translating the ontology into rules without any prior pre-processing or classification. We have implemented our approach and our evalu-ation shows encouraging results. 1
