An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems

Li, Ming; Liu, Lu; Li, Chuanbo

doi:10.1016/j.eswa.2011.01.062

Cited by 54 publications

(44 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing literature reveals several approaches on sharing expertise and recommendation, for example, Balog and De Rijke (2007), Li et al (2011, Guy et al (2012), Karimzadehgan et al (2009) and Balog et al (2009). When selecting papers for further analysis, the ones published between 2008 and 2012, meeting the criterion of timeliness, and their assumable scientific relevance (measured by the impact factor of the publisher) were taken into account.…”

Section: Case Selectionmentioning

confidence: 99%

KMS re-contextualization – recognizing learnings from OMIS research

Mühlburger

Oppl

Stary

2017

VJIKMS

View full text Add to dashboard Cite

Purpose Deployment of knowledge management systems (KMSs) suffers from low adoption in organizational reality that is attributed to a lack of perceivable added value for people in actual work situations. Poor task/technology fit in the process of knowledge retrieval appears to be a major factor influencing this issue. Existing research indicates a lack of re-contextualizing stored information provided by KMSs in a particular situation. Existing research in the area of organizational memory information systems (OMISs) has thoroughly examined and widely discussed the topic of re-contextualization. The purpose of this paper, thus, is to examine how KMS design can benefit from OMIS research on approaches for re-contextualization in knowledge retrieval. Design/methodology/approach This paper examines OMIS literature and inductively derives a categorization scheme for KMS according to their strategy of re-contextualizing knowledge. The authors have validated the scheme validated in a multiple case study that examines the differentiatory value of the scheme for approaches with various re-contextualization strategies. Findings The classification scheme allows a step-by-step selection of approaches for re-contextualization of information in KMS design and development derived from OMIS research. The case study has demonstrated the applicability of the developed scheme and shows that the differentiation criteria can be applied unambiguously. Research limitations/implications Because of the chosen case study approach for validation, the validation results may lack generalizability. Practical implications The scheme enables an informed selection of KMSs appropriate for a particular OMIS use case, as the scheme’s attributes serve as design rationale for a certain architecture or constellation of components. Developers can not only select from various approaches when designing re-contextualizaton but also come up with rationales for each candidate because of structured representation. Hence, stakeholders can be supported in a more informed way and design KMSs more effectively along organizational change processes. Originality/value The paper addresses an identified need for systematic characterization of KMS approaches and systems intending to meet the objectives of OMISs. As such, it allows streamlining further research in this field, as approaches can be judged according to their originality and positioned relative to each other.

show abstract

Section: Case Selectionmentioning

confidence: 99%

KMS re-contextualization – recognizing learnings from OMIS research

Mühlburger

Oppl

Stary

2017

VJIKMS

View full text Add to dashboard Cite

show abstract

“…Fuzzy numbers are also used to calculate the value of a patent and the chance of mitigation (Agliardi and Agliardi, 2011), which similar to quality of knowledge in the above example, are also parameters very difficult to measure objectively. Semantics and fuzzy logic are employed in group decision making (Gupta and Mohanty, 2016), consensus building (Li et al, 2017), opinion mining (Martínez-Cruz et al, 2016) and knowledge management (Li et al, 2011).…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Multi-faceted assessment of trademark similarity

Setchi

Anuar

2016

Expert Systems with Applications

View full text Add to dashboard Cite

Highlights A novel method for the assessment of trademark similarity is proposed. The method blends together visual, semantic and phonetic similarity. It produces an aggregated score based on the individual assessments. Evaluation using information retrieval measures and human judgment.ACCEPTED MANUSCRIPT One of the most influential factors in this test is establishing similarity in appearance, meaning or sound. However, even though the trademark registration process suggests a multi-faceted similarity assessment, relevant research in expert systems mainly focuses on computing individual aspects of similarity between trademarks. Therefore, this paper contributes to the knowledge in this field by proposing a method, which, similar to the way people perceive trademarks, blends together the three fundamental aspects of trademark similarity and produces an aggregated score based on the individual visual, semantic and phonetic assessments. In particular, semantic similarity is a new aspect, which has not been considered by other researchers in approaches aimed at providing decision support in trademark similarity assessment. Another specific scientific contribution of this paper is the innovative integration, using a fuzzy engine, of three independent assessments, which collectively provide a more balanced and human-centered view on potential infringement problems. In addition, the paper introduces the concept of degree of similarity since the line between similar and dissimilar trademarks is not always easy to define especially when dealing with blending three very different assessments. The work described in the paper is evaluated using a database comprising 1,400 trademarks compiled from a collection of real legal cases of trademark disputes. The evaluation involved two experiments. The first ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T experiment employed information retrieval measures to test the classification accuracy of the proposed method while the second used human collective opinion to examine correlations between the trademark scoring/rating and the ranking of the proposed method, and human judgment. In the first experiment, the proposed method improved the F-score, precision and accuracy of classification by 12.5%, 35% and 8.3%, respectively, against the best score computed using individual similarity. In the second experiment, the proposed method produced a perfect positive Spearman rank correlation score of 1.00 in the ranking task and a pairwise Pearson correlation score of 0.92 in the rating task. The test of significance conducted on both scores rejected the null hypotheses of the experiment and showed that both scores correlated well with collective human judgment. The combined overall assessment could add value to existing support systems and be beneficial for both trademark examiners and trademark applicants. The method could be further used in addressing recent cyberspace phenomena related to trademark infringement such as customer hijacking and cybersquatting.

show abstract

“…According to Yang and Pedersen (1997) the process of retrieving documents in properly classified databases is more efficient and the search scope is reduced even if a large volume of information is available. Li et al (2011) further state that the goal of text classification is to label textual documents with thematic classes from a predefined set. Also according to these authors, many different methods have been applied to text classification tasks including the K-Nearest Neighbor (KNN) approach (Bang et al, 2006;Tan, 2006), Naïve Bayesian approaches (Baker and McCallum, 1998;Lewis, 1998;Yang and Liu, 1999), support vector machine (Dumais and Chen, 2000) and decision trees (Lewis and Ringuette, 1994;Quinlan, 1993).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Modeling for Multi-Label Text Classification Supported by Classification Algorithms

Wilges¹,

Mateus²,

Nassar³

et al. 2016

Journal of Computer Science

View full text Add to dashboard Cite

Abstract:The ever-increasing amount of information on the Web is organized in structured, semi-structured and unstructured data. Text classification systems, capable of handling such different structures, may facilitate the work of important tasks such as indexation and information retrieval in search engines. The objective of this research is to develop a method for the classification of documents into multiple categories with fuzzy logic. This method was built from a process of pattern recognition and, also, two variables called similarity and accuracy were used. The proposed fuzzy classification method uses variables that express the ability to analyze the similarity and accuracy of a document through a database of terms. The database of terms is generated by a collection of pre-classified documents in categories of interest. The documents processed according to the similarity and accuracy in the database of terms composes a training set also called knowledge base. From this database, it is possible to identify a pattern that specifies a set of rules through a knowledge discovery process. This process involves the data mining of the knowledge base. Thus, it was possible to define a general model that is used in the creation of rules and membership functions of the fuzzy model for the classification of documents into multiple categories. The general model of the rules identified in the data mining process and implemented in fuzzy model considers the most significant variables and also contributes to the specification of the membership functions, such as the definition of linguistic terms of fuzzy sets. Thus, it was possible to implement a more deterministic approach regarding the input, membership functions and inference rules of the fuzzy model. The results of the proposed method for classification of documents are relevant because they have a satisfactory accuracy rate.

show abstract

An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems

Cited by 54 publications

References 51 publications

KMS re-contextualization – recognizing learnings from OMIS research

KMS re-contextualization – recognizing learnings from OMIS research

Multi-faceted assessment of trademark similarity

Fuzzy Modeling for Multi-Label Text Classification Supported by Classification Algorithms

Contact Info

Product

Resources

About