Search (27 results, page 2 of 2)

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Abstract

It is vital for library and information science to understand the subject indexing process thoroughly. However, document analysis, the first and most important step in indexing, has not received sufficient attention. As this is an exceptionally hard problem, we still do not dispose of a sound indexing theory. Therefore we have difficulties in teaching indexing and in explaining why a given subject representation is "better" than another. Technological advancements have not helped to close this fundamental gap. To proceed, we should ask the right questions instead. Several types of indexer inconsistencies can be explained as acceptable, yet different conceptualizations which resulting of the variety of groups dealing with a problem from their respective viewpoints. Multiple indexed documents are regarded as the normal case. Intersubjectively replicable indexing results are often questionable or do not constitute interesting cases of indexing at all. In the context of my ongoing dissertation in which I intend to develop an enhanced indexing theory by investigating improvements within a social sciences domain, this paper explains user-oriented selective depth analysis and why I chose that configuration. Strongly influenced by Mai's dissertation, I also communicate my first insights concerning current indexing theories. I agree that I cannot ignore epistemological stances and philosophical issues in language and meaning related to indexing and accept the openness of the interpretive nature of the indexing process. Although I present arguments against the employment of an indexing language as well, it is still indispensable in situations which demand easier access and control by devices. Despite the enormous difficulties the user-oriented and selective depth analysis poses, I argue that it is both feasible and useful if one achieves careful guidance of the possible interpretations. There is some hope because the number of useful interpretations is limited: Every summary is tailored to a purpose, audience and situation. Domain, discourse and social practice entail additional constraints. A pluralistic method mix that focusses on ecologically valid, holistic contexts and employs qualitative methods is recommended. Domain analysis urgently has to be made more practical and applicable. Only then we will be able to investigate empirically domains in order to identify their structures shaped by the corresponding discourse communities. We plan to represent the recognized problem structures and indexing questions of relevance to a small domain in formal, ontological computer models -- if we can find such stable knowledge structures. This would allow us to tailor dynamically summaries for user communities. For practical purposes we suggest to assume a less demanding position than Hjorland's "totality of the epistemological potential". It is sufficent that we identify and represent iteratively the information needs of today's user groups in interactive knowledge-based systems. The best way to formalize such knowledge gained about discourse communities is however unknown. Indexers should stay in direct contact with the community they serve or be part of it to ensure agreement with their viewpoints. Checklist/request-oriented indexing could be very helpful but it remains to be demonstrated how well it will be applicable in the social sciences. A frame-based representation or at least a sophisticated grouping of terms could help to express relational knowledge structures. There remains much work to do since in practice no one has shown yet how such an improved indexing system would work and if the indexing results were really "better".

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Date

22. 1.2012 13:02:10

Source

Knowledge organization. 39(2012) no.1, S.13-22

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Abstract

In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.

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Source

Cataloging and classification quarterly. 22(1996) no.2, S.5-18

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Date

27. 9.2005 14:22:19

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Date

22. 9.1997 19:16:05