The "Spree" Expert Finding System

Seid, Dawit Yimam; Mehrotra, Sharad

doi:10.1109/icsc.2007.38

Cited by 10 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Knowledge cards are similar to yellow pages in that they do not contain the knowledge itself, but rather describe the path to that knowledge. Additional tools which provide answers to specific questions as yellow pages do, but which use a different approach, are the so-called question and answer systems (Iske & Boersma, 2005) and also forums in which questions are posed and answered (Metze et al, 2007). In these tools, employees do not present a profile with their areas of expertise and do not react to specific and personal requests on the part of a person seeking information.…”

Section: Differences To Other Toolsmentioning

confidence: 99%

Implementation Process of a Knowledge Management Initiative: Yellow Pages

Gretsch¹,

Mandl²,

Schätz³

2012

New Research on Knowledge Management Models and Methods

View full text Add to dashboard Cite

Section: Differences To Other Toolsmentioning

confidence: 99%

Implementation Process of a Knowledge Management Initiative: Yellow Pages

Gretsch¹,

Mandl²,

Schätz³

2012

New Research on Knowledge Management Models and Methods

View full text Add to dashboard Cite

“…The system also suggests the top conferences and papers on this topic. Other examples of systems that make use of the information encoded in an underlying social network include Spree by Metze et al [46] and Expertise Recommender by McDonald et al [45].…”

Section: Expert Location Systemsmentioning

confidence: 99%

A Survey of Algorithms and Systems for Expert Location in Social Networks

Lappas

Liu

Terzi

2011

Social Network Data Analytics

View full text Add to dashboard Cite

Given a particular task and a set of candidates, one often wants to identify the right expert (or set of experts) that can perform the given task. We call this problem the expert-location problem and we survey its different aspects as they arise in practice. For example, given the activities of candidates within a context (e.g., authoring a document, answering a question), we rst describe methods for evaluating the level of expertise for each of them. Often, experts are organized in networks that correspond to social networks or organizational structures of companies. We next devote part of the chapter for describing algorithms that compute the expertise level of individuals by taking into account their position in such a network. Finally, complex tasks often require the collective expertise of more than one experts. In such cases, it is more realistic to require a team of experts that can collaborate towards a common goal. We describe algorithms that identify effective expert teams within a network of experts. The chapter is a survey of different algorithms for expertise evaluation and team identi cation. We highlight the basic algorithmic problems and give some indicative algorithms that have been developed in the literature. We conclude the chapter by providing a comprehensive overview of real-life systems for expert location.

show abstract

“…Our original algorithm for matching user queries to appropriate experts is based on an ontology tree that covers various areas of knowledge. Mapping expert profiles and user queries to subtrees of the ontology allows for addressing the matching problem by means of simple operations in a semantics-induced vector space [6,7].…”

Section: Web Wide Contextmentioning

confidence: 99%

Image retrieval and Web 2.0 — where can we go from here?

Bauckhage

Alpcan

Wetzker

et al. 2008

2008 15th IEEE International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

Compared to only a few years ago, today there is an abundance of annotated image data available on the Internet. For researchers on image retrieval, this is an unforseen but welcome consequence of the rise of Web 2.0 technologies. Popular social networking and content sharing services seem to hold the key to the integration of context and semantics into retrieval. However, at least for now, it appears that this promise has to be taken with a grain of salt. In this paper, we present preliminary empirical results on the tagging behavior of power users of content sharing and social bookmarking services. Our findings suggest different promising research directions for image retrieval and we briefly discuss some of them.

show abstract

The "Spree" Expert Finding System

Cited by 10 publications

References 4 publications

Implementation Process of a Knowledge Management Initiative: Yellow Pages

Implementation Process of a Knowledge Management Initiative: Yellow Pages

A Survey of Algorithms and Systems for Expert Location in Social Networks

Image retrieval and Web 2.0 — where can we go from here?

Contact Info

Product

Resources

About

The "Spree" Expert Finding System

Cited by 10 publications

References 4 publications

Implementation Process of a Knowledge Management Initiative: Yellow Pages

Implementation Process of a Knowledge Management Initiative: Yellow Pages

A Survey of Algorithms and Systems for Expert Location in Social Networks

Image retrieval and Web 2.0 &#x2014; where can we go from here?

Contact Info

Product

Resources

About

Image retrieval and Web 2.0 — where can we go from here?