Naming Functions for the Vector Space Model

Tzitzikas, Yannis; Theoharis, Yannis

doi:10.1007/978-3-540-71496-5_58

Cited by 1 publication

(11 citation statements)

References 8 publications

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“…Concretely, Tzitzikas and Hainaut propose in [120] two PageRank-style algorithms, called EntityRank and BEntityRank, to obtain a rank of entity types according to their relevance in entity-relationship diagrams. The same algorithms, among others, are also included in the paper by Tzitzikas, Kotzinos and Theoaris [121], but applied to RDF schemas. Hsi et al present a similar set of methods in [60].…”

Section: Relevance Methodsmentioning

confidence: 99%

“…This method was introduced in [121] (called m 0 ) and takes into account only the number of directly connected characteristics of each entity type. Formally, the importance I SM (e) of an entity type e is defined as:…”

Section: The Simple Methods (Sm)mentioning

confidence: 99%

“…This is a variation of the simple method taking into account both directly defined features and inherited ones (see m 5 in [121]). For each entity type the method computes the number of ascendants and descendants and all specified attributes and accessible associations from it or any of its ascendants.…”

Section: The Transitive Inheritance Methods (Tim)mentioning

confidence: 99%

“…The relevance of an element is the addition of a proportional portion of the relevance of the elements that point to it. Tzitzikas et al [120,121] 2 PageRank-style algorithms (EntityRank and BEntityRank) to obtain a rank of entity types according to their relevance in ER diagrams. Use the relationships as links (without make a differentiation between generalizations or associations).…”

Section: Contribution Characteristicsmentioning

confidence: 99%

“…In this thesis we deal with schemas written in the UML [84]/OCL [85], although our method can be directly adapted to schemas defined in other modeling languages. Table 4.1 summarizes the notation (inspired by [121,6]) used in the rest of the document.…”

Section: Basic Measuresmentioning

confidence: 99%

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A filtering engine for large conceptual schemas

Villegas Niño

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Nowadays, the need for representation and conceptualization of real world information has dramatically increased. Organizations evolution and diversification require the management and maintenance of large amounts of knowledge from their domains of interest. That growth also has an impact in the size of conceptual schemas of information systems, making them larger. The sheer size of those schemas transforms them into very useful artifacts for the communities and organizations for which they are developed. However, the size of the schemas and their overall structure and organization make it difficult to manually extract knowledge from them, to understand their characteristics, and to change them.There are many information system development activities in which people needs to get a piece of the knowledge contained in the conceptual schema. For example, a conceptual modeler needs to check with a domain expert that the knowledge is correct, a database designer needs to implement that knowledge into a relational database, a software tester needs to write tests checking that the knowledge has been correctly implemented in the system components, or a member of the maintenance team needs to change that knowledge.Dealing with large conceptual schemas is one of the most challenging and long-standing goals in conceptual modeling. The purpose of this thesis is to formally define a new information filtering methodology to help users of very large conceptual schemas to understand the characteristics and knowledge these schemas contain. This thesis analyzes and describes the different phases of an information filtering engine, identifies and studies several properties of relevance for elements of large conceptual schemas, provides a catalog of specific filtering requests to explore a schema in several filtering scenarios, and implements and evaluates the efficiency and effectiveness of a filtering engine prototype with several real case studies.The filtering methodology studies the characteristics of the knowledge contained within a large conceptual schema, and proposes ways to select and represent the user interest in order to specialize the results of a filtering engine. Consequently, a user focus on a fragment of the large schema of interest to her and our methodology automatically obtains a reduced conceptual schema extracted from the large schema and focused on the knowledge that has a closer relation with the focus of the user. Such filtered conceptual schema is a subset of the original one, and because of its reduced size it is more comprehensible to the user.The filtering approach provides knowledge extraction techniques aligned with the user interest representation, and presents such knowledge in an appropriate way to simplify its understanding. Furthermore, using this filtering approach the large conceptual schemas are navigated more quickly, increasing their usability and reducing the user effort. LIST OF FIGURES xiiAnyone who has never made a mistake has never tried something new.

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Section: Relevance Methodsmentioning

confidence: 99%

Section: The Simple Methods (Sm)mentioning

confidence: 99%

Section: The Transitive Inheritance Methods (Tim)mentioning

confidence: 99%

Section: Contribution Characteristicsmentioning

confidence: 99%

Section: Basic Measuresmentioning

confidence: 99%

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