Kashyap, V., Sheth, A.P.: Semantic and schematic similarities between database objects: A context-based approach. VLDB J. 5 (1996) 276--304  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to generate hypotheses about semantically related structures in two information sources. Structure Enrichment: A logical model is built that resembles the structure of the information source and contains additional definitions of concepts. A detailed discussion of this kind of mapping is given in [Kashyap and Sheth, 1996]. Systems that use structure enrichment for information integration are OBSERVER [Kashyap and Sheth, 1998] KRAFT [Preece et al. 1999] PICSEL [Goasdoue and Reynaud, 1999] and DWQ [Calvanese et al. 1998b] While OBSERVER uses description logics for both structure resemblance and additional ....

Kashyap, V. and Sheth, A. P. (1996). Semantic and schematic similarities between database objects: A context-based approach. VLDB Journal: Very Large Data Bases, 5(4):276--304.

....attribute which can have a value like Pyramidal Cell dendrite and Purkinje Cell , respectively. How are the schemas of synapse and ncmir related Evidently they carry distinctly di#erent information and do not even enter the purview of the schema conflicts usually studied in databases [KS96] To the scientist however, they are related because of the following reason: Like pyramidal neurons, Purkinje cells also possess dendritic spines. Release of calcium in spiny dendrites occurs as a result of neurotransmission and results in changes in spine morphology (sizes and shapes obtained ....

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-based Approach. VLDB Journal, 5(4):276--304, 1996.

....concern in cognitive psychology, linguistics, and computer science. Context has been considered in quite a few formalizations in several areas of computer science (see [17] such as artificial intelligence [10, 15, 9] software development [22, 8, 24, 26, 27, 12, 13] multiple) databases [1, 6, 11, 21], machine learning [16, 31, 14] and knowledge representation [19, 30, 29] However, these formalizations are very diverse and serve different purposes. In the area of knowledge representation, Mylopoulos and Motschnig Pitrik [19] proposed a general mechanism for partitioning information bases ....

....Using this notion of context, the authors define operations check in and check out for hypermedia objects. However, there is no support for name relativism, and neither are generic operations on contexts provided. The notion of context has also appeared in the area of heterogeneous databases [25, 21, 11]. There, the word context refers to the implicit assumptions underlying the manner in which an agent represents or interprets data. To allow exchange between heterogeneous information systems, information specific to them can be captured in specific contexts. Therefore, contexts are used for ....

Vipul Kashyap and Amit Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, December 1996.

....in cognitive psychology, linguistics, and computer science. In computer science, a number of formal or informal definitions of some notion of context have appeared in several areas, such as artificial intelligence [15, 24, 13] software development [31, 12, 34, 35, 36, 19, 20] databases [3, 11, 14, 1, 7, 18, 30], machine learning [25, 44, 23] and knowledge representation [28, 42, 40, 46, 8, 38, 6, 4] See also [26] for a general survey on the subject. However, all these notions of context are very diverse and serve different purposes. In software development the notion of context appears in the form of ....

.... even in the form of workspaces which are used to support cooperative work [19] In machine learning, context is treated as environmental information for concept classification [25, 44, 23] In so called multi bases , context appears as a collection of meta attributes for capturing class semantics [18]. In artificial intelligence the notion of context appears as a means of partitioning knowledge into manageable sets [16] or as a logical construct that facilitates reasoning activities [24, 13] In particular, in the area of knowledge representation, the notion of context appears as an ....

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, Dec. 1996.

....integrated. In Section 2, we provide further examples of complex correspondences between the domains of multiple IS in a federated system. Description Logics are formalisms for the conceptual representation of IS which have been successfully used in the past in the task of IS integration (e.g. [7, 12, 1, 15, 5, 16, 2]) We will therefore carry out our investigations in their context. In Section 3, we introduce Description Logics and Distributed Description Logics (DDL) provide formal semantics for them, and show how the examples are handled by the result. In Section 4, we review some desirable properties of ....

V. Kashyap and A. Sheth. Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach. VLDB Journal 5(4): 276-304, 1996.

....semantic context, which consists of a variable set of meta attributes (also represented as semantic objects) that explicitly describe implicit modeling assumptions. Our approach is based on the notion of context as proposed in [7, 4] A more comprehensive notion of context can be found in [3]. In addition, each semantic object has a concept label associated with it that specifies the relationship between the object and the real world aspects it describes. These concept labels are taken from a commonly known ontology. Thus, the concept label and the semantic context of a semantic ....

Kashyap, V.; Sheth, A.: Semantic and Schematic Similarities between Database Objects: A Context-based Approach, VLDB Journal, 5(4) 1996

....describes some semantic aspects of the corresponding data objects, many of the modeling assumptions remain implicit. As a prerequisite, to integrating data from di#erent sources these semantic assumptions have to be made explicit. For this, di#erent approaches have been discussed in the literature [CRE87, SM91, SSR92, ON94, GMS95, RS95, KS96, HMV96], which all fall into the spectrum characterized by the following extremes: the coding of semantic context information by introducing specific data types (e.g. CRE87] and the representation of context information in the form of additional meta attributes outside of the underlying type ....

....outside of the underlying type system. The latter approach corresponds to the specification of variable attribute sets similar to Lisp property lists [Stee90] which describe di#erent aspects of a given data object. These metaattributes may be represented as explicitly stored or virtual attributes [SSR94, KS96, RS95] which can be made available directly as part of the data, or may be given in the form of rules defining the association of data objects with their semantic properties [SM91, ON94, GMS95] By using additional meta attributes for an explicit description of the semantic properties of a data object ....

[Article contains additional citation context not shown here]

Kashyap, V.; Sheth, A.: Semantic and Schematic Similarities between Database Objects: A Context-based Approach, In: The VLDB Journal, Vol.5, No.4, 1996

....approaches based on concepts. In such systems, semantic views (ontologies) are used to provide concise and declarative specification of semantic information. They are also used to describe information content in data repositories independent of the underlying syntactic representation of the data [12]. Each data repository is viewed at the level of the relevant semantic concepts. Users express their information needs using those semantic views and the query processor must obtain the corresponding answer by accessing the underlying data repositories. Semantic views can be described by using ....

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: A context-based approach. The International Journal of Very Large Data Bases (VLDB), 5(4):276-304 (1996).

....in cognitive psychology, linguistics, and computer science. In computer science, a number of formal or informal definitions of some notion of context have appeared in several areas, such as artificial intelligence [51, 73, 46] software development [92, 45, 99, 106, 108, 56, 63] databases [7, 44, 49, 2, 27, 55, 86], machine learning [76, 130, 71] and knowledge representation [84, 122, 126, 121, 135, 31, 116, 26, 18] See also [80] for a general survey on the subject. However, all these notions of context are very diverse and serve different purposes. In software development the notion of context appears in ....

.... form of workspaces which are used to support cooperative work [56] In machine learning, context is treated as environmental information for concept classification [76, 130, 71] In the so called multiple databases, context appears as a collection of meta attributes for capturing class semantics [55]. In artificial intelligence, the notion of context appears as a means of partitioning knowledge into manageable sets [51] or as a logical construct that facilitates reasoning activities [73, 46] In particular, in the area of knowledge representation, the notion of context appears as an ....

[Article contains additional citation context not shown here]

Vipul Kashyap and Amit Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, December 1996.

....psychology, linguistics, and computer science. In computer science, a number of formal or informal definitions of some notion of context have appeared in several areas, such as artificial intelligence [12] 18] software development [25] 11] 27] 28] 16] databases [3] 10] 1] 6] [15], 24] machine learning [19] 17] and knowledge representation [22] 34] 32] 37] 7] 30] 5] 4] See also [20] for a general survey on the subject. However, all these notions of context are very diverse and serve different purposes. In software development the notion of context ....

.... even in the form of workspaces which are used to support cooperative work [16] In machine learning, context is treated as environmental information for concept classification [19] 17] In so called multi bases , context appears as a collection of meta attributes for capturing class semantics [15]. In artificial intelligence the notion of context appears as a means of partitioning knowledge into manageable sets [13] or as a logical construct that facilitates reasoning activities [18] In particular, in the area of knowledge representation, the notion of context appears as an abstraction ....

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, Dec. 1996.

....are assumed to be snapshots in time of the same structure. We use a similar set of primitives in Section 2.2 to construct congruity and similarity measures, which approximate the corresponding semantic relationships. Some work on the use of context to handle database object semantics is found in [KS96] This work uses a semantic proximity relationship, but does not discuss relevance of source data to a client application. The system in [HM93] uses gradations of a consistency relationship in relating information sources. In considering semantic reconciliation between data source and data ....

Vipul Kashyap and Amit Sheth. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal, 5(4):276-304, October 1996.

....correlations and use the discovery to plan future work in the context of available data. The integration challenge is that source data cannot be joined using simple term matching or comparison operators. Even more sophisticated approaches which use ontologies to enumerate joinable terms [Kas96] are often not sufficient. Instead a join should be performed based on whether the objects satisfy 1 some application specific condition. For complex integration scenarios as our neuroscience application, a more expressive formalism is necessary to specify these semantic join conditions . In ....

A. Kashyap, V.; Sheth. Semantic and Schematic Similarities between Database Objects: A Context-based Approach. VLDB Journal, 5(4):276--304, 1996.

.... techniques including the use of data values [LC94, CRL98] data dictionary information [CRL98] structural properties [PSU98] ontologies [FPNB99] synonyms and other terminological relationships found in dictionaries and thesauri [HR90, BCV99, CA97] and various combinations of these techniques [KS96, KS98, CAFP98] These are the kinds of facets of metadata we wish to exploit, all of which may contribute to the resolution of attribute matching issues. Although we probably have some idea about what metadata is most useful and in what combination and under what circumstances we should use this ....

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: A context-based approach. The VLDB Journal, 5:276--304, 1996.

....ontologies represents the matching parts of two ontologies that are relevant to an application that requires the ontologies. Articulations blend the congruity and similarity relationships we introduce in Section 2. 3 Some work on the use of context to handle database object semantics is found in [10]. This work uses a semantic proximity relationship, but does not discuss relevance of source data to a client application. The system in [7] uses gradations of a consistency relationship in relating information sources. In considering semantic reconciliation between data source and data receiver ....

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal, 5(4):276--304, Oct. 1996.

....might be adapted to allow the definition of new assertions, none of the previous work details how this might be done. In any case, such a technique seems to make the definition of new schema transformation rules more complex than necessary. A similar approach to ours was recently described in [11] where the notion of a database context constrains instances so that schemas can be considered equivalent. 8. CONCLUSIONS We have proposed a unifying framework for the various methodologies for schema integration, which employ different notions of schema equivalence. We have proposed a new ....

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal, 5(4):276--304 (1996).

....importance in cognitive psychology, linguistics, and computer science. In computer science, a number of formal or informal definitions of some notion of context have appeared in several areas, such as artificial intelligence [12, 17, 10] software development [23, 9, 25, 26, 15, 16] databases [2, 8, 11, 1, 5, 14, 22], machine 1 learning [18, 32] and knowledge representation [20, 31, 28, 33, 6, 4, 3] See also [19] for a general survey on the subject. However, all these notions of context are very diverse and serve different purposes. In software development the notion of context appears in the form of ....

.... or even in the form of workspaces which are used to support cooperative work [15] In machine learning, context is treated as environmental information for concept classification [18, 32] In so called multi bases , context appears as a collection of meta attributes for capturing class semantics [14]. In artificial intelligence the notion of context appears as a means of partitioning knowledge into manageable sets [13] or as a logical construct that facilitates reasoning activities [17, 10] In particular, in the area of knowledge representation, the notion of context appears as an ....

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, Dec. 1996.

....then they are likely to contain short identical subsequences. 2.2.2 Domain Independent Work In this section we review two more general models for invoking similarity queries. There are more general models that try to capture the context of comparison or semantic similarity between objects [KS96] We will not discuss those models here. Chapter 2. Similarity Based Queries 10 A General Framework on Similarity Based Queries A general framework for similarity based queries is developed by Jagadish, Mendelzon, and Milo [JMM95] The framework has three components: a pattern language P, a ....

Vipul Kashyap and Amit P. Sheth. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal, 5(4):276-- 304, 1996.

....has been widely recognized. E#orts, such as XML [22] try to provide a framework for additional semantic information through tags that provide hints concerning the intended meaning of the data. The use of semantic metadata for the integration of relational databases is advocated, among others, in [19, 11]. In our approach we advocate the use of existing common vocabularies or ontologies as a basis for the interpretation of Web based data. In the travel industry these are the common three letter codes or the UNICORN protocol. Ideally, providers should adhere to those. However, in an imperfect real ....

....4.2 Simple semantic objects A semantic object may be understood as a data item with additional context information attached to support its correct interpretation. For the explicit representation of context information (mainly in databases) di#erent approaches have been discussed in the literature [18, 19, 14, 8, 16, 11]. We prefer to represent this additional information on an extensional level, because semistructured sources provide no explicitly specified data schema to which meta information may refer. Simple semantic objects represent atomic values, like simple number values or character strings. Based on a ....

Kashyap, V.; Sheth, A.: Semantic and Schematic Similarities between Database Objects: A Context-based Approach, VLDB Journal, 5(4), 1996

....the inter operability between different types of DB management systems (DBMS) which is caused by syntactic and semantic heterogeneity in data models and in access models. For instance, relational data and objects [Fang et al. 9 ] Mehrotra et al. 91] different conceptual schema representations [Kashyap et al. 95] Hammer 94] guaranteed and relaxed DB integrity [Mehrotra et al. 91] Stonebraker et al. 96] and different DB tools need to be homogenised in a DDBMS. In order to cope with various types of heterogeneity, successful solutions should attack the problem from two angles: algorithmic and ....

Kashyap, Vipul; Sheth, Amit; 1995; "Semantic and Schematic Similarities between Database Objects: A Context-based approach"; The VLDB Journal

....semantic conflicts, since data models typically contain only a handful of basic concepts. For instance, mediator based systems require that wrappers hide data model diversity, but not semantic heterogeneity; the classical 54. See e.g. Wie 93] for an overview, and [Con 97] Kim 95] SP 91] KS 95] VJB 97] as classifications of logical heterogeneity. Information Systems 5 Federated Information Systems April 1999 layer architecture for federated databases has a separate layer to transform data models (the component schema) Logical Heterogeneity . Semantic heterogeneity concerns the ....

V. Kashyap, A. Sheth, Semantic and Schematic Similarities between Database Objects: A Context-based approach, Sept. 1995; an abridged version appears in the VLDB Journal, Vol. 5, No. 4, pp. 276-304, Oct. 1996; 1995.

....for the schema transformation process by defining a very general notion of schema equivalence, together with a set of primitive transformations that can be used to formally define more complex schema transformations. Previous work on schema transformation has either been to some extent informal [2, 7, 6], has formalised only transformations that are independent of database content [12, 13] or is limited to certain types of transformation only [3, 8, 17, 5] The latter cases assume that specific types of dependency constraints are employed to limit the instances of schemas (or real world states ....

....Thus, constructing transformations is a relatively simple task of programming a sequence of primitive transformations, stating conditions on these where they are dependent on instances satisfying certain constraints in order to output a valid model. A similar approach has recently been adopted in [6] where the notion of a database context constrains instances so that schemas can be considered equivalent. A further distinctive feature of the work described here is that our underlying CDM is a very simple one. This makes it straightforward to formalise a variety of higher level CDMs and their ....

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal, 5(4):276--304, 1996.

....and the weakest types of semantic similarity. Semantic similarity between elements is relative, in that it depends on the context in which they are being compared. Attempts have been made to represent context of comparison , for example, as relevant behaviour [LB93] and meta attributes [KS96]. In the latter case, the authors suggest pre existing ontologies for choosing contextual coordinates and their values. GSC96] identifies, as a further complication, that the definition context of an object may be different from the context in which it is compared with another object for the ....

Kashyap, V., Sheth, A., `Semantic and Schematic Similarities between Database Objects: A Context Based approach', 22nd VLDB, Bombay, 1996

....1990) instead of the common thesaurus. Or, if a formal ontology was available, they could use the measures of description compatibility described in this paper, rather than their semantic distance metric based on path distance, which is an additive version of our LCSR measure. Kashyap and Sheth (Kashyap and Sheth 1996), like us, seek to describe the similarities and differences between intensional definitions of two concepts (which they call objects ) Their semantic proximity is defined by a tuple, including: the context of comparison which they define most closely as an attribute value space, the ....

Kashyap, V. and A. Sheth (1996). Semantic and Schematic Similarities between Database Objects: A Context-based approach. International Journal on Very Large Data Bases 5(4): 276-304.

....e.g. 9] This type of mapping provides little support for semantics, but rather relies on the structure of the data. ffl Ontology based: A common ontology is defined with well specified semantics for the concepts it describes. Mapping between a schema and an ontology is done on a semantic level [12]. Since the relationship between data items in a schema and semantic concepts in an ontology is often sloppy, the study of how to do this also addresses issues such as hyponyms and hypernyms, and the uncertainty that they introduce. ffl Reasoning: Sometimes the conversion of values requires ....

....the semantic concepts (ontology) Individual information systems then map their information onto this ontology. Naturally, this process is an uncertain one, as the structure and implied semantics of the information in the local source may not necessarily match the semantics of the ontology [12]. Unfortunately, using ontologies does not completely solve all issues of merging information, specifically falling short in places where semantic information is incomplete, uncertain, or changing [14] Wrestling with these issues in the EDEN project motivated us to develop agents designed ....

Vipul Kashyap and Amit Sheth. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal, 5, 1996.

....concern in cognitive psychology, linguistics, and computer science. Context has been considered in quite a few formalizations in several areas of computer science (see [17] such as artificial intelligence [10, 15, 9] software development [22, 8, 24, 26, 27, 12, 13] multiple) databases [1, 6, 11, 21], machine learning [16, 31, 14] and knowledge representation [19, 30, 29] However, these formalizations are very diverse and serve different purposes. In the area of knowledge representation, Mylopoulos and Motschnig Pitrik [19] proposed a general mechanism for partitioning information bases ....

....Using this notion of context, the authors define operations check in and check out for hypermedia objects. However, there is no support for name relativism, and neither are generic operations on contexts provided. The notion of context has also appeared in the area of heterogeneous databases [25, 21, 11]. There, the word context refers to the implicit assumptions underlying the manner in which an agent represents or interprets data. To allow exchange between heterogeneous information systems, information specific to them can be captured in specific contexts. Therefore, contexts are used for ....

Vipul Kashyap and Amit Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, December 1996.

....they are working on the specification of a core model for metadata 11 Paper Session: Newmetadataand novelApproaches The first one (11.1) may be interesting the references are good. The second one belongs to Amit Sheth s list of papers, but I think there are others much more interesting [5], 4] 11.1 Metadata for Distributed Visual Database Access by W . Chang et al. It is a kind of GLOSS for images. They search for the data source which is the most suitable to answer to a certain query. And they look for images similarities. Related work is in the area of information retrieval ....

Vipul Kashyap and Amit Sheth. Semantic and Schematic Similarities between Databases Objects: A Context-based Approach. The VLDB Journal, 5(4), October 1996.

....ontologies there is no standard basis for the nature of the classification (Roy Hafner 1997) Hence an ontology based on philosophical foundations (Sowa 1998) is quite different from the CYC ontology. Sheth considers the use of contexts to manage semantic heterogeneity in database objects (Kashyap Sheth 1996). Wiederhold introduces the notion of an algebra over ontologies in (Wiederhold 1994) Hovy s (Chalupsky, Hovy, Russ 1997) work on ontology alignment indicates that a low percentage of top level concepts are matched using semi automated tools. Some researchers argue that differences in ....

Kashyap, V., and Sheth, A. 1996. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal 5(4):276--304.

No context found.

V. Kashyap, and A. P. Sheth, Semantic and schematic similarities between database objects: A context-based approach. VLDB Journal, 5(4):276---304, 1996.

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Databases Objects: A Context-based approach. The VLDB Journal. To appear; http://www.cs.uga.edu/LSDIS/amit/66b-VLDB.ps.

....an informal classification. We identify and propose domain specific metadata as the key for solving the semantic heterogeneity problem [KSS95] Section 3 discusses the construction of c contexts from domain specific ontologies and their representation in a formalism that can be easily mapped [KS96] to a description logic (DL) expression. Issues of language and ontology involved in the above are also discussed. These c contexts are used to represent extra knowledge about the information content of the database which may not be represented in the schema of the database. A user query can also ....

....and ontology involved in the above are also discussed. These c contexts are used to represent extra knowledge about the information content of the database which may not be represented in the schema of the database. A user query can also be represented as a c context. Schema correspondences [KS96] that capture the associations between c contexts and the underlying data are also discussed. The key to interoperability is vocabulary sharing among the intensional m context and c context descriptions associated with the various databases. Different concepts may be used to design contextual ....

[Article contains additional citation context not shown here]

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Databases Objects: A Context-based approach. The VLDB Journal, 5(4), October 1996. To appear; http://www.cs.uga.edu/LSDIS/~amit/66b-VLDB.ps.

....ontologies. Domain specific ontologies thus enable the re use, organization and communication of knowledge and semantics between information users and providers. Approaches for mapping database objects to semantic contextual expressions constructed from ontological terms have been proposed in [8, 5, 2]. Our query processing approach enables the user to subscribe to the vocabulary (characterized by a domain ontology) he is familiar with. Even when the user poses a query using terms from one ontology, relevant concepts may be described in other ontologies, and the corresponding information may be ....

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: A context-based approach. The International Journal of Very Large Data Bases (VLDB), 5(4), December 1996.

No context found.

Kashyap, V., Sheth, A.P.: Semantic and schematic similarities between database objects: A context-based approach. VLDB J. 5 (1996) 276--304

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, Dec. 1996.

No context found.

V. Kashyap and A. Sheth. "Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach." VLDB Journal 5, no. 4 (1996): 276--304. 367

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. The VLDB Journal, 5(4):276--304, Dec. 1996.

No context found.

Vipul Kashyap and Amit Sheth. Semantic and schematic similarities between database objects: a context-based approach. The VLDB Journal, 5(4):276--304, 1996.

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. J. VLDB, 5(4):276--304, Dec. 1996.

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Kashyap, V.; Sheth, A.: Semantic and Schematic Similarities between Database Objects: A Context-based Approach, In: The VLDB Journal, Vol.5, No.4, 1996

No context found.

V. Kashyap and A. Sheth. "Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach." VLDB Journal 5, no. 4 (1996): 276--304. 367

No context found.

Kashyap, V., Sheth, A. P.: Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach. In VLDB Journal 5(4): 276-304(1996).

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach. VLDB Journal 5(4): 276-304, 1996.

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach. VLDB Journal 5(4): 276-304, 1996.

No context found.

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: A context-based approach. The International Journal of Very Large Data Bases (VLDB), 5(4):276-304 (1996).

No context found.

V. Kashyap and A. Sheth. "Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach." VLDB Journal 5, no. 4 (1996): 276--304. 367

No context found.

Vipul Kashyap and Amit P. Sheth. Semantic and schematic similarities between database objects: A context-based approach. VLDB Journal: Very Large Data Bases, 5(4):276--304, 1996.

No context found.

Vipul Kashyap and Amit Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, December 1996.

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities between Database Objects: A Context-Based Approach. VLDB Journal, 5(4):276--304, Dec. 1996.

No context found.

V. Kashyap and A. Sheth. Semantic and Schematic Similarities Between Database Objects: A Context-Based Approach. VLDB Journal 5(4): 276-304, 1996.

No context found.

V. Kashyap and A. Sheth. Semantic and schematic similarities between database objects: A context-based approach. The VLDB Journal, 5:276--304, 1996.

No context found.

V. Kashyap and A. Sheth, Semantic and Schematic Similarities between Database Objects: A Context-based approach, VLDB Journal, 5 (1996) 276--304.

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