Development of a semantic-based content mapping mechanism for information retrieval

Pai, Mao-Yuan; Chen, Ming-Yen; Chu, Hui-Chuan; Chen, Yuh‐Min

doi:10.1016/j.eswa.2012.10.056

Cited by 16 publications

(9 citation statements)

References 13 publications

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“…Semantic retrieval employs external knowledge sources, such as ontologies, to overcome the limitations of keyword-based systems [19][20][21][22]. Ontologies, which form structural frameworks for organizing information, provide underlying domain-specific technical support, together with a theoretical basis for knowledge representation and organization [23].…”

Section: A Existing Trademark Search Systemsmentioning

confidence: 99%

Semantic Retrieval of Trademarks Based on Conceptual Similarity

Anuar

Setchi

Lai

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. The paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky's theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1,400 disputed cases and a database of 378,943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the R-precision score, which is commonly used in information retrieval, and human judgment/collective human opinion, which is used in human-machine systems.

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Section: A Existing Trademark Search Systemsmentioning

confidence: 99%

Semantic Retrieval of Trademarks Based on Conceptual Similarity

Anuar

Setchi

Lai

2016

IEEE Trans. Syst. Man Cybern, Syst.

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“…Hence, this approach is not suitable for the situational differentiator of Criterion F. Pai et al (2013) does not require upfront modelling of information sources, as all semantic enhancements are executed automatically within the system. As the differentiators rely on generic differentiators like semantic similarity of content maps, this approach is suitable for the situational differentiator for Criterion H.…”

Section: Resultsmentioning

confidence: 99%

“…Two approaches identified in literature can be considered light-weight in term of information delivery and scientific relevance (Holz et al, 2006;Pai et al, 2013). According to its more recent publication date and the impact factor of the publication, the approach introduced by Pai et al (2013) has been selected for validation.…”

Section: Case Selectionmentioning

confidence: 99%

KMS re-contextualization – recognizing learnings from OMIS research

Mühlburger

Oppl

Stary

2017

VJIKMS

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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.

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“…However, the conceptual formalism supported by the typical ontology may not be sufficient enough to represent the uncertainty of the information commonly found in many application domains due to the lack of clear-cut boundaries between concepts of the domains [13][14][15][16][17][18]. The issue of tackling this type of problems, has attracted world-wide attention for further research and development, resulting in various extensions to the original ontology theory and increasingly widening the fields of its applications.…”

Section: Related Workmentioning

confidence: 99%

A Semantic Information Retrieval Approach Based on Rough Ontology

Hu¹,

Lu²,

Guan³

2014

TOCSJ

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With the emergence of the semantic web, ontology has attracted a great deal of attention in field of information retrieval. But the conceptual formalism supported by typical ontology is not sufficient for handling incomplete information that is confronted in the real world knowledge. To tackle this problem, a semantic information retrieval approach based on a rough ontology is proposed. Rough ontology in this paper is in the form of an ontology information system. Given a keyword based query, our approach infers the individuals and properties correlated to the query through a procedure of association searches in the rough ontology, and takes properties as equivalence relations to construct an approximation space of rough ontology. Afterward, an algorithm of computing similarity in rough ontology is presented, and approximation space is employed to compute similarity for ranking documents in semantic document indexing space. The proposed approach has been compared with two other information retrieval techniques, and the experiments conducted on CNKI collections, support the better efficacy which results from our approach.

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Development of a semantic-based content mapping mechanism for information retrieval

Cited by 16 publications

References 13 publications

Semantic Retrieval of Trademarks Based on Conceptual Similarity

Semantic Retrieval of Trademarks Based on Conceptual Similarity

KMS re-contextualization – recognizing learnings from OMIS research

A Semantic Information Retrieval Approach Based on Rough Ontology

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