C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... data (e.g. 14, 41] are often based on a paraconsistent reasoning process, such as LFI [13] annotated logics [30, 40] or other non classical proof systems [5, 37] Coherent (consistency base) methods, in which the amalgamated data is revised in order to restore consistency (see, e.g. [6, 8, 11, 25, 31]) In many cases the underlying formalism of these approaches are closely related to the theory of belief revision [1, 23] In the context of database systems the idea is to construct consistent databases that are as close as possible to the original database. These repaired instances of the ....

....that is obtained by running T in the A system together with an i optimizer [respectively, together with a c optimizer] s.t. Insert = Insert . 6 Related works Coherent integration and proper representation of amalgamated data is extensively studied in the literature (see, e.g. [8, 12, 22, 24, 25, 31 34, 38, 41]) Common approaches for dealing with this task are based on techniques of belief revision [31] methods of resolving contradictions by quantitative considerations (such as majority vote [32] or qualitative ones (e.g. de ning priorities on di erent sources of information or preferring certain ....

C.Baral, S.Kraus, J Minker. Combining Multiple Knowledge Bases. IEEE Trans. on Knowledge and Data Enginnering 3(2), pp.208-220, 1991.

....that what is obtained would properly re ect the combination of the distributed data on one hand , and would still be coherent (in terms of consistency) on the other hand. Coherent integration and proper representation of amalgamated data is extensively studied in the literature (see, e.g. [1, 3, 7, 13, 14, 20 23, 26, 29]) Common approaches for dealing with this task are based on techniques of belief revision [20] methods of resolving contradictions by quantitative considerations (such as majority vote [21] or qualitative ones (e.g. de ning priorities on di erent sources of information or preferring certain ....

C.Baral, S.Kraus, J Minker. Combining Multiple Knowledge Bases. IEEE Trans. on Knowledge and Data Enginnering 3(2), 208-220, 1991.

....In fact, the approach of Whang et al. is in the same spirit as that of metalogic programming discussed earlier. Whang et al. do not give a formal semantics for multi databases containing inconsistency and or uncertainty and or non monotonicity and or temporal information. Baral et al. [2, 3] have developed algorithms for combining different logic databases which generalizes the update strategy by giving priorities to some updates (when appropriate) and as well as not giving priorities to updates (which corresponds to combining two theories without any preferences) Combining two ....

....to updates (which corresponds to combining two theories without any preferences) Combining two theories corresponds, roughly, to finding maximally consistent subsets (also called flocks by Fagin et al. 13, 14] As we have shown in [32] our framework can express maximal consistency as well. [2, 3] do not develop a formal model theoretic treatment of combining multiple knowledge bases, whereas our method does provide such a model theory. 2, 3] are unable to handle non monotonicity (in terms of stable well founded semantics) nor uncertainty, nor time stamped information our framework is ....

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C. Baral, S. Kraus and J. Minker. (1991) Combining Multiple Knowledge Bases, IEEE Trans. on Knowledge and Data Engineering, 3, 2, pps 200-220.

....spread considerably, providing a great variety of information on different domains of expertise. In presence of this increasing community of distributed, autonomous, and heterogeneous sources of knowledge, the problem of cooperation between such systems is becoming more and more critical (see e.g. [1, 3, 7, 14]) In particular, a central issue is to enable several knowledge bases to work interactively, grouping their items of knowledge in order to solve complex problems that could not be handled in isolation. Several difficulties arise when we attempt to merge different knowledge bases. First, we need ....

....focused on the problem of consistency. The representation formalisms used to handle conflicting knowledge can broadly be classified in two groups. On the one hand, we have extensions to classical logic which use extra logical features in order to restore consistency. The methods suggested in [1, 3, 14] are based on a similar strategy. Essentially, this consists of finding all maximal consistent subsets from a pool of knowledge, and concluding only the sentences that are in the intersection of all these subsets. This strategy is also used in belief update [13] and belief revision [5] ....

C. Baral, S. Kraus, and J. Minker. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Eng., 3(2):200--220, 1991.

....automatically or semi automatically. Our methodology for integrating the schema and extensional part of deductive databases falls into the category of declarative statement approaches. Recently several papers addressing the integration of deductive databases have also appeared in literature [Bar91, Sub94, Sir95]. In [Bar91] an algorithm is presented to combine knowledge bases each of which consists of a finite set of disjunctive clauses, with the assumption that every knowledge base has the same set of integrity constraints. In [Sub94] a method is proposed to amalgamate several deductive databases into a ....

....Our methodology for integrating the schema and extensional part of deductive databases falls into the category of declarative statement approaches. Recently several papers addressing the integration of deductive databases have also appeared in literature [Bar91, Sub94, Sir95] In [Bar91] an algorithm is presented to combine knowledge bases each of which consists of a finite set of disjunctive clauses, with the assumption that every knowledge base has the same set of integrity constraints. In [Sub94] a method is proposed to amalgamate several deductive databases into a single ....

[Article contains additional citation context not shown here]

C. Baral, S. Kraus and J. Minker., "Combining Multiple Knowledge Bases", IEEE Trans. on Knowledge and Data Engineering, Vol.3, No.2, June 1991.

....and procedural statement approaches. Our methodology for integrating the schema and extensional part of deductive databases falls into the category of declarative statement approaches. Recently several papers addressing the integration of deductive databases have also appeared in literature [Bar91, Sub94, Sir95]. Compared with these approaches, we propose a systematic method to compare the semantics of rules, and we integrate not only non recursive derived functions but also recursive derived functions. Some work has recently been done on the integration of integrity constraints [Red95, Alz95] The ....

C. Baral, S. Kraus and J. Minker., "Combining Multiple Knowledge Bases", IEEE Trans. on Knowledge and Data Engineering, Vol.3, No.2, June 1991.

....and procedural statement approaches. Our methodology for integrating the schema and extensional part of deductive databases falls into the category of declarative statement approaches. Recently several papers addressing the integration of deductive databases have also appeared in literature [2, 19, 16]. Compared with these approaches, we propose a systematic method to compare the semantics of rules, and we integrate not only non recursive derived functions but also recursive derived functions. Some work has recently been done on the integration of integrity constraints [13, 1] and our ....

C. Baral, S. Kraus and J. Minker., "Combining Multiple Knowledge Bases", IEEE Trans. on Knowledge and Data Engineering, Vol.3, No.2, June 1991.

....available combinations of media and modalities. Thus, the problem can be thought of as a special case of the problem of resolving ambiguities when combining multiple knowledge sources into a universal consistent theory, or as a Multiple Criteria Decision Making problem. Based on work reported in [18] and [19] we propose a methodology for the combination of PTMMs into a TMM. IV. THE PROPOSED METHODOLOGY Step 1. Define the Adaptivity Design Space IC = ic 1 , ic 2 , ic i I = i 1 , i 2 , i k M = m 1 , m 2 , m x Spaces IC and I contain the elements of the interaction ....

C. Baral, S. Kraus, and J. Minker, "Combining Multiple Knowledge Bases," IEEE Transactions on Knowledge and Data Engineering, vol. 3, no. 2, pp. 208-220, June 1991.

....In fact, the approach of Whang et al. is in the same spirit as that of metalogic programming discussed earlier. Whang et al. do not give a formal semantics for multi databases containing inconsistency and or uncertainty and or non monotonicity and or temporal information. Baral et al. [2, 3] have developed algorithms for combining different logic databases which generalizes the update strategy by giving priorities to some updates (when appropriate) and as well as not giving priorities to updates (which corresponds to combining two theories without any preferences) Combining two ....

....to updates (which corresponds to combining two theories without any preferences) Combining two theories corresponds, roughly, to finding maximally consistent subsets (also called flocks by Fagin et al. 13, 14] As we have shown in [31] our framework can express maximal consistency as well. [2, 3] do not develop a formal model theoretic treatment of combining multiple knowledge bases, whereas our method does provide such a model theory. 2, 3] are unable to handle non monotonicity (in terms of stable well founded semantics) nor uncertainty, nor time stamped information our framework is ....

[Article contains additional citation context not shown here]

C. Baral, S. Kraus and J. Minker. (1991) Combining Multiple Knowledge Bases, IEEE Trans. on Knowledge and Data Engineering, 3, 2, pps 200-220.

.... can be seen as a particular case of the problem which consists in collecting beliefs that some agents have about the real world: each agent has an incomplete perception of a real situation and it is sometimes interesting to collect all these perceptions in order to reason about the situation, [1], 2] The classic example is a police inspector who questions different witnesses. Each witness has his own beliefs concerning the crime and the inspector has to collect all of them in order to find the clue. For instance, one witness has said that he saw a dark blue car on the crime place, ....

....the logic which is obtained in this case on a small example, and consider two agents a1 and a2 . a1 says that it rained between the date 1 and the date 3 and a2 says that it did not rain sometimes between date 2 and date 3, but if it did not rain at 3 then it did not rain at 2. Then, a1 = f [1 3]raing a2 = f 23 :rain; 3] rain [2] rain g 3 Here are again some theorems that can be deduced by FUSION in this case: ffl FUSION Ba 1 a 2 [1]rain [2]rain [3]rain That is, if agent a1 is considered as the most important (reliable for instance) then one can deduce that it rained ....

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C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.

....set of multiple views. Information fusion is the process of deriving this single consistent view. Whilst theoretical approaches such as belief revision [AGM85,Gar88,DP97] databases and knowledgebase updating [FKUV86,KM89,Win90,Som94] and combining knowledgebases (for example [DLP92,Mot93,BKMS92,BKMS91] are relevant, information fusion addresses a wider range of issues raised by practical imperatives in applications such as requirements engineering. The problem of information fusion appears in many fields, such as gathering beliefs or evidence, developing specifications, and merging ....

C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.

....minimal models. We define the consistency of a set of DATALOG databases as follows. A database D (which may be disjunctive) is said to be consistent with respect to a set of integrity constraints IC iff every minimal model of D satisfies IC. An algorithm to combine DATALOG databases is given in [Baral et al. 1991]. We provide an example that illustrates the problem, and we present briefly the steps needed to combine the databases in the example. Example 3. Baral et al. 1991] Consider the integrity constraint ( p (X) p (Y) X 6= Y) and two databases D 1 and D 2 as follows. D 1 r (a) p (X) q ....

....a set of integrity constraints IC iff every minimal model of D satisfies IC. An algorithm to combine DATALOG databases is given in [Baral et al. 1991] We provide an example that illustrates the problem, and we present briefly the steps needed to combine the databases in the example. Example 3. [Baral et al. 1991] Consider the integrity constraint ( p (X) p (Y) X 6= Y) and two databases D 1 and D 2 as follows. D 1 r (a) p (X) q (X) D 2 q (b) p (X) r (X) Each database alone is consistent with the integrity constraint. However, the union of D 1 and D 2 together with the integrity ....

[Article contains additional citation context not shown here]

Baral, C., Kraus, S., and Minker, J. (1991). Combining multiple knowledge bases. IEEE Transactions on Knowledge and Data Engineering, 3(2):208--220.

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C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.

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C. Baral and S. Kraus and J. Minker and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991

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C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.

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C. Baral and S. Kraus and J. Minker and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.

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C. Baral, S. Kraus, J. Minker, and V.S. Subrahmanian. Combining multiple knowledge bases. IEEE Trans. on Knowledge and Data Engineering, 3(2), 1991.

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C. Baral, S.Kraus, and J. Minker, (1991). Combining Multiple Knowledge Bases, IEEE Trans. on Knowledge and Data Engineering, Vol. 3, 208-220

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