@article {712,
	title = {A CBR Decision Support System for Loan Evaluation},
	journal = {Journal of Digital Information Management},
	volume = {17},
	year = {2019},
	month = {04/2019},
	pages = {75-86},
	type = {Research},
	chapter = {75},
	abstract = {<p>Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come to a decision, particularly, in a critical situation. This shared knowledge constitutes the group memory. In this paper, we develop a Case-Based Reasoning Decision Support System called CBR-DSS that makes use of the rules, policies and standard procedures of prior groups embedded in the group memory. The CBR process is evolved by using the Analytic Hierarchy Process (AHP). In an effort to enhance utility and show the feasibility, we experiment our approach in a bank loan analysis domain, a semi-structured multiple attributes decision-making environment, and compare CBR-DSS with a conventional case based reasoning system. CBR-DSS will compensate for lack of experience of young bank consultants, and allow disseminating and distributing available experience to different bank sites.</p>
},
	keywords = {Case-based Reasoning, Group Decision Making, Group Memory, Information retrieval, Recommender system},
	url = {https://doi.org/10.6025/jdim/2019/17/2/75-86},
	author = {Abdelkader Adla and Noureddine Benmessaoud}
}
@article {361,
	title = {Effective implementation of basic operations for information retrieval},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {389 - 398},
	abstract = {In the article we describe the approach to parallel implementation of elementary operations for textual data categorization. In the experiments we evaluate parallel computations of similarity matrices and k-means algorithm. The test datasets have been prepared as graphs created from Wikipedia articles related with links. W also present the approach to computing pairs of eigenvectors and eigenvalues for visualizations of the datasets. The implemented basic operations: computing similarity matrix, data clustering and spectral analysis have been used in our system for visualization of the Wikipedia categories on SOM as well as in a system for categorization search results in Wikipedia.},
	keywords = {Documents Categorization, Information retrieval, PCA, SOM, Text clustering},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84874861039\&partnerID=40\&md5=f0cc1565c8fce706ec0125e688e6474a},
	author = {Szymanski, J.}
}
@article {243,
	title = {Exploiting multi-word similarity for retrieval in medical document collections: The TSRM approach},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2010},
	pages = {315 - 321},
	abstract = {In this paper, we investigate on potential improvements to Information Retrieval (IR) models related to document representation and conceptual, topic retrieval, in medical document collections. We propose the TSRM1 (Term Similarity and Retrieval Model) approach, where document representations are based on multi-word domain terms, rather than mere single key-words, typically applied in traditional IR. The proposed representation is semantically compact and more efficient, being reduced to a limited number of meaningful multi-word terms (phrases), rather than large vectors of single-words, part of which may be void of distinctive content semantics. In computing document similarity, contrary to other state-of-theart methods examined in this work, TSRM adopts a knowledge poor solution, namely an approach which does not require any existing knowledge resources, such as ontologies, or thesauri. The evaluation of TSRM is based on OHSUMED, a standard TREC collection of medical documents and illustrated the effciency of TSRM over other well established general purpose IR models.},
	keywords = {Document representation, Information retrieval, Medical information retrieval, Term extraction, Term Similarity},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960229245\&partnerID=40\&md5=f9f58b511661960f2a01ed0691084e33},
	author = {Drymonas, E. and Zervanou, K. and Petrakis, E.G.M.}
}
@article {237,
	title = {A fuzzy grassroots ontology for improving weblog extraction},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):3Export Date: 10 July 2014},
	month = {2010},
	pages = {276 - 284},
	abstract = {This paper presents fuzzy clustering algorithms to establish a grassroots ontology - a machine-generated weak ontology - based on folksonomies. Furthermore, it describes a search engine for vaguely associated terms and aggregates them into several meaningful cluster categories, based on the introduced weak grassroots ontology. A potential application of this ontology, weblog extraction, is illustrated using a simple example. Added value and possible future studies are discussed in the conclusion.},
	keywords = {Fuzzy data clustering, Information retrieval, Ontology, Search engine, Semantic web, Weblog},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955703451\&partnerID=40\&md5=e96f607ac097bae04e70c3dc44c841f3},
	author = {Portmann, E. and Meier, A.}
}
@article {234,
	title = {Harvesting pertinent resources from Linked Open Data},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):5Export Date: 10 July 2014},
	month = {2010},
	pages = {205 - 212},
	abstract = {Linked Open Data (LOD) is becoming an essential part of the Semantic Web. Although LOD has amassed large quantities of structured data from diverse, openly available data sources, there is still a lack of user-friendly interfaces and mechanisms for exploring this huge resource. In this paper, we describe a methodology for harvesting relevant information from the gigantic LOD cloud. The methodology is based on combination of information: identification, extraction, integration and presentation. Relevant information is identified by using a set of heuristics. The identified information resource is extracted by employing an intelligent URI discovery technique. The extracted information is further integrated with the help of a Concept Aggregation Framework. Then the information is presented to end users in logical informational aspects. Thereby, the proposed system is capable of hiding complex underlying semantic mechanics from end users and reducing the users{\textquoteright} cognitive load in locating relevant information. In this paper, we describe the methodology and its implementation in the CAF-SIAL system, and compare it with the state of the art.},
	keywords = {CAF-SIAL, Information presentation, Information retrieval, Linked Open Data (LOD), Semantic web, URI, User interfaces},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960685735\&partnerID=40\&md5=cc886d4db44fdc4f20722b7621aca818},
	author = {Latif, A. and Afzal, M.T. and Saeed, A.U. and Hoefler, P. and Tochtermann, K.}
}
@article {250,
	title = {Towards building ranking models with annual reports},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2010},
	pages = {338 - 343},
	abstract = {The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general re search question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: 1) the feasibility of build ing ranking models with annual reports to rank future firm performance and 2) the effect of integrating meta seman tic features to help improve and support our prediction. We compare models built with different ranking algorithms and document models. We evaluate our models with a simulated portfolio. Our results show significantly positive average returns over 5 years with a power law trend as we increase the ranking threshold. Adding meta features to document model has shown to improve ranking performance. The SVR \& Meta-augemented model outperforms the others and pro vides potential for explaining the textual factors behind the prediction.},
	keywords = {Annual reports, Documentation models, Information retrieval, Ranking models},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960663639\&partnerID=40\&md5=391fa47605e7e864c26988ad0b95ab63},
	author = {Qiu, X.Y.}
}
@article {166,
	title = {Design and development of a Query Graph Visualization system},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Export Date: 10 July 2014},
	month = {2009},
	pages = {21 - 29},
	abstract = {Collaborative querying is a technique that harnesses the collective search experiences of users to assist in query formulation. We present the Query Graph Visualizer (QGV), a visual collaborative querying system that recommends related queries to a user{\textquoteright}s submitted query through a network visualization scheme. Users are able to explore the query network and select queries for execution on an information retrieval (IR) system. the QGV is not meant to be a replacement for IR systems but as a value-added module to help users search more effectively. Consequently, the QGV is not an IR system but works in tandem with existing IR systems. the design of the QGV is discussed, focusing on its architecture, the interaction between the QGV{\textquoteright}s main components and the implementation of the user interface. An evaluation of the QGV was also conducted to assess the performance of the system against to a conventional search engine. Results indicate that the evaluators who used the QGV completed their tasks much faster compared to those using a search engine alone. A usability evaluation also showed that the system complied with standard user interface heuristics.},
	keywords = {Collaborative querying, Information retrieval, Query Graph Visualize, Query network},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350660828\&partnerID=40\&md5=33ccaf28cf0dad32e0b47d01f84a8707},
	author = {Goh, D.H.-L. and Lee, C.S. and Chua, A.Y.K. and Luyt, B.}
}
@article {188,
	title = {Query-by-multiple-examples using support vector machines},
	journal = {Journal of Digital Information Management},
	volume = {7},
	year = {2009},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2009},
	pages = {202 - 210},
	abstract = {We identify and explore an Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information need is described not by a set of terms but by a set of documents. Intuitive ideas for QBME include using the centroid of these documents or the well-known Rocchio algorithm to construct the query vector. We consider this problem from the perspective of text classification, and find that a better query vector can be obtained through learning with Support Vector Machines (SVMs). For online queries, we show how SVMs can be learned from one-class examples in linear time. For offline queries, we show how SVMs can be learned from positive and unlabeled examples together in linear or polynomial time, optimising some meaningful multivariate performance measures. The effectiveness and efficiency of the proposed approaches have been confirmed by our experiments on four real-world datasets.},
	keywords = {Information retrieval, Machine learning, Supportvector machine, Text classification},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77953247724\&partnerID=40\&md5=8fea0823a5d70e478bf228f315a5ef89},
	author = {Zhang, D. and Lee, W.S.}
}
@article {79,
	title = {Biological model for information retrieval},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {123 - 129},
	abstract = {Heuristic methods based on biological aspects have been successfully used in many computer science areas of research, including information retrieval (IR). This let us ask: why there is no biological environment for the problem of information retrieval. In this paper, we tried to introduce a new biological environment and model for information retrieval that broad the modeling concept from the mathematical formula that simulates the basic elements of IR problem to their dual schemas in biology. It{\textquoteright}s a new way of thinking of, and dealing with, the problem of information retrieval.},
	keywords = {Biological environment, Biological model, Genetic algorithms, Information retrieval},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350691855\&partnerID=40\&md5=22f8442d6b340f89f18dd39e3c99ed99},
	author = {Girgis, M.R. and Aly, A.E.-M.A. and Latef, B.A.A. and El-Gamil, B.R.}
}
@article {65,
	title = {Optimization of Boolean queries in information retrieval systems using genetic algorithms - Genetic programming and fuzzy logic},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Export Date: 10 July 2014},
	month = {2006},
	pages = {249 - 254},
	abstract = {This paper proposes to use two information retrieval system models (Boolean information retrieval model and extended Boolean (fuzzy) information retrieval model). These models differ by using Boolean queries or fuzzy weighted queries. It also proposes a way for optimizing user query for the two models by using genetic programming and fuzzy logic. And proposes to use more number of Boolean operators (AND, OR, XOR, OF, and NOT) instead of the standard Boolean operators (AND, OR, and NOT), and use weights for Boolean operators and for terms in fuzzy models.},
	keywords = {Boolean operator weights, Boolean query, Fuzzy logic, Genetic algorithms, Genetic programming, Information retrieval, Term weights},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33845945357\&partnerID=40\&md5=bb2fe8678839544324f8ce90e9dca876},
	author = {Owais, S.S.J.}
}
