Relevance Relationships

Bean, Carol A.; Green, Rebecca

doi:10.1007/978-94-015-9696-1_8

Cited by 13 publications

(10 citation statements)

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“…Green (), Green and Bean (), and Bean and Green () are among the few more recent papers that have challenged the matching assumption, pointing out that “we have little real understanding of how the topics of text segments relate to the topics of user needs to which they are relevant” (Green, , p. 652). Evidence from citation analysis, knowledge discovery and synthesis, and recall failure analysis suggests that topical relationships are not limited to topic matching (Bodoff, ; Green, ).…”

Section: Relevance Relationships: the Central Element Of The Frameworkmentioning

confidence: 99%

“…As discussed above, deductive reasoning (Cooper, ) and inductive reasoning (Wilson, ) both play a role in detecting topical relevance. Through analysis of biblical passages and subject headings assigned to them in a user‐oriented index, Green and Bean () and Bean and Green () identified a broad range of hierarchical and structural (or syntagmatic) relationships beyond “direct [topic] matching” that account for topical relevance. Huang and Soergel (, ), Huang and White (), and Huang () continued this line of research and further synthesized a typology of more than 200 fine‐grained topical relevance relationships, structured within three facets shown in Table .…”

Section: Relevance Relationships: the Central Element Of The Frameworkmentioning

confidence: 99%

“…At the heart of relevance lies topical relevance. Although topical relevance is widely recognized as the most important factor in selecting information, our understanding of the notion is limited and merits further explication building on work by Cooper (), Wilson (, ), Saracevic (), Rees and Saracevic (), Green and Bean (), Bean and Green (), Huang and Soergel (, ), Huang and White (), and Huang (, ). The growth of interest in “user relevance” has shifted attention away from topical relevance (Hjørland, ).…”

Section: Introductionmentioning

confidence: 99%

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Relevance: An improved framework for explicating the notion

Huang

Soergel

2012

J Am Soc Inf Sci Tec

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Synthesizing and building on many ideas from the literature, this article presents an improved conceptual framework that clarifies the notion of relevance with its many elements, variables, criteria, and situational factors. Relevance is defined as a Relationship (R) between an Information Object (I) and an Information Need (N) (which consists of Topic, User, Problem/Task, and Situation/ Context) with focus on R. This defines Relevance-as-is (conceptual relevance, strong relevance). To determine relevance, an Agent A (a person or system) operates on a representation IЈ of the information object and a representation NЈ of the information need, resulting in relevance-as-determined (operational measure of relevance, weak relevance, an approximation). Retrieval tests compare relevance-as-determined by different agents. This article discusses and compares two major approaches to conceptualizing relevance: the entityfocused approach (focus on elaborating the entities involved in relevance) and the relationship-focused approach (focus on explicating the relational nature of relevance). The article argues that because relevance is fundamentally a relational construct the relationshipfocused approach deserves a higher priority and more attention than it has received. The article further elaborates on the elements of the framework with a focus on clarifying several critical issues on the discourse on relevance. Generic reasoning; Reasoning by analogy; Reasoning by contrast;Rule-based reasoning (deduction); Generalization (induction); Causal-based reasoning: forward / backward inference. & Bean, 1995) How information connects to a topic semantically. Semantic-based (adapted from Green Class -Member;Whole -Part (partonomy): process -step, etc.; Object -Attribute: adjectival, adverbial; Class -Subclass (taxonomy).

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Section: Relevance Relationships: the Central Element Of The Frameworkmentioning

confidence: 99%

Section: Relevance Relationships: the Central Element Of The Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Relevance: An improved framework for explicating the notion

Huang

Soergel

2012

J Am Soc Inf Sci Tec

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“…As he foresaw, the advantage of adopting RT is that it is a psychologically plausible theory with sufficient breadth to subsume the many accounts of “relevance,” “pertinence,” and “utility” that presently vie for attention in retrieval evaluation studies (see, e.g., Borlund, 2003; Cosijn & Ingwersen, 2000; Mizzaro, 1997; Schamber, 1994). Under cognitive effects, RT can handle not only topical relevance (as it must in indexing‐based retrieval), but also other important kinds, such as evidentiary and analogical relevance (Bean & Green, 2001). Moreover, RT uniquely ties relevance to processing effort, a very desirable integration given that information scientists routinely find minimization of effort to characterize information‐seekers' behavior (Buckland & Hindle 1969; Case, 2005; Mann, 1993, pp.…”

Section: The New Synthesismentioning

confidence: 99%

Enhanced podcasts for teaching biochemistry to veterinary students

Gough

2011

Biochem Molecular Bio Educ

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The teaching of biochemistry within medical disciplines presents certain challenges; firstly to relay a large body of complex facts and abstract concepts, and secondly to motivate students that this relatively difficult topic is worth their time to study. Here, nutrient biochemistry was taught within a multidisciplinary module as part of an undergraduate veterinary curriculum. The teaching approach was initially focussed on a mixture of didactic lectures and student-centred activities such as directed group/self learning. In subsequent years the core didactic lectures were replaced with enhanced podcasts covering the same material, along with the introduction of student presentations delivered within groups with both peer and facilitator assessment. These changes were accompanied by an increase in the time dedicated to this topic to allow sufficient time for students to work through podcasts and prepare presentations. The combination of these changes resulted in significant improvements in student performance within an in-course biochemistry long essay. These changes in the teaching approach, and particularly the introduction of extensive podcasts, was well received by students who perceived the process of going through the podcasts as time consuming but allowing them flexibility in both the pace that they studied this topic as well as the location and times that they studied it.

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“…Green (2001) suggested that there may be several types of semantic relations underlying these factors, which have not been studied in depth. Green and Bean (1995) and Bean and Green (2001) have explored some of the relations underlying topical relevance.…”

Section: Types Of Semantic Relationsmentioning

confidence: 99%

Semantic relations in information science

Khoo

2006

Annual Review Info Sci & Tec

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Abstract. This chapter examines the nature of semantic relations and their main applications in information science. The nature and types of semantic relations are discussed from the perspectives of linguistics and psychology. An overview of the semantic relations used in knowledge structures such as thesauri and ontologies are provided, as well as the main techniques used in the automatic extraction of semantic relations from text. The chapter then reviews the use of semantic relations in information extraction, information retrieval, question-answering and automatic text summarization applications.

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Relevance Relationships

Cited by 13 publications

References 20 publications

Relevance: An improved framework for explicating the notion

Relevance: An improved framework for explicating the notion

Enhanced podcasts for teaching biochemistry to veterinary students

Semantic relations in information science

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