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Shapira, Bracha; Hanani, Uri; Raveh, Adi; Shoval, Peretz

doi:10.1023/a:1008676625559

Cited by 23 publications

(3 citation statements)

References 10 publications

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“…Shapira et al [103,102] suggest an advanced model for information filtering which is based on a two-phase filtering process and the use of stereotypes. The user profiling in the filtering method is constructed on the basis of the user areas of interest and on sociological parameters about her that are known to the system.…”

Section: Hanani Et Al Explain Inmentioning

confidence: 99%

“…Introduces methods for user profile generation, and proposes a research agenda for their comparison and evaluation. Shapira et al [103,102] Advanced model for information filtering which isbased on a two-phase filtering process. The first phase is cognitive filtering, i.e., a correlation between the contents of the information to be filtered with the areas of interest to the user.…”

Section: Contribution Characteristicsmentioning

confidence: 99%

“…According to the multi-layer model of Hanani et. al [102] to define and classify information filtering approaches, the characteristics of our method are:…”

Section: The Filtering Approachmentioning

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: Hanani Et Al Explain Inmentioning

confidence: 99%

Section: Contribution Characteristicsmentioning

confidence: 99%

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A filtering engine for large conceptual schemas

Villegas Niño

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Stereotype‐based versus personal‐based filtering rules in information filtering systems

Kuflik

Shapira

Shoval

2003

J. Am. Soc. Inf. Sci.

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Rule-based information filtering systems maintain user profiles where the profile consists of a set of filtering rules expressing the user's information filtering policy. Filtering rules may refer to various attributes of the data items subject to the filtering process. In personal rulebased filtering systems, each user has his/her own personal filtering rules. In stereotype rule-based filtering systems, a user is assigned to a group of similar users (his/her stereotype) from which he/she inherits the stereotype's filtering profile. This study compares the effectiveness of the two alternative rule-based filtering methods: stereotype-based rules versus personal rules. We conducted a comparison between filtering effectiveness when using the personal rules or when using the stereotype-based rules. Although, intuitively, personal filtering rules seem to be more effective because each user has his own tailored rules, our comparative study reveals that stereotype filtering rules yield more effective results. We believe that this is because users find it difficult to evaluate their filtering preferences accurately. The results imply that by using a stereotype it is possible not only to overcome the problem of user effort required to generate a manual rule-based profile, but at the same time even provide a better initial user profile.

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Recommending biomedical resources: A fuzzy linguistic approach based on semantic web

Morales-del-Castillo

Peis

Ruiz

2010

Int. J. Intell. Syst.

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One of the key issues in dynamic research areas, such as that of biomedical sciences, is the development of tools capable to retrieve and provide users relevant resources from large repositories according to their information needs. In this paper, we present a filtering and recommender system that applies Semantic Web technologies and fuzzy linguistic modeling techniques to provide users valuable information about resources that fit their interests. To carry out the recommendation process, we have defined three software agents (interface, task, and information agents) that are distributed in a five level hierarchical architecture. The system is also capable of to deal with incomplete information to define enriched user profiles and, therefore, soften the problem of cold start. A simple evaluation has been carried out, and the experimental outcomes reveal a reasonable good performance of the system in terms of precision and recall. C

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Cited by 23 publications

References 10 publications

A filtering engine for large conceptual schemas

A filtering engine for large conceptual schemas

Stereotype‐based versus personal‐based filtering rules in information filtering systems

Recommending biomedical resources: A fuzzy linguistic approach based on semantic web

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