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Saman Kamran

dblp:34/3309 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 3 · 3 first-authorSoftware engineering, systems software and programming languages · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology › ontology engineering
collaborative ontology building
0.112011
Defining ontology by using users collaboration on social media · CSCW 2011
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology
ontology construction
0.112011
Defining ontology by using users collaboration on social media · CSCW 2011

Methods — techniques the papers use, named apart from their topics

user collaboration · 0.2
YearPublicationVenuePosition
2017 Enhancing Accuracy of Dynamic Collaborative Labeling and Matching Through Semantic Augmentation Method (SAM)
Saman Kamran, Zaikun Xu, Mehdi Jazayeri
CDVE1
2015 Dynamic Content and User Identification in Social Semantic Tagging Systems
Saman Kamran, Mehdi Jazayeri
CDVE1
2011 Defining ontology by using users collaboration on social media
abstract
This novel method is proposed for building a reliable ontology around specific concepts, by using the immense potential of active volunteering collaboration of detected knowledgeable users on social media.
Saman Kamran, Fabio Crestani
CSCW1
2008 Defining a Strategy to Select Either of Closed/Open World Assumptions on Semantic Robots
abstract
Semantic Web technology is based on the Open World Assumption (OWA) where absence of entities being searched does not entail negative response rather simply treated as facts "not available at the moment." On the one hand that indicates anticipation of future enhancements of the fact store while in other cases it is not preferred in situations where an authoritative answer is needed. Closed World Assumption (CWA) on the other hand returns definitive yes/no answers even in situations where future enhancements are inevitable. We wish to distinguish among the situations where one or the other assumption would be advantageous to use. Such distinction could cater for faster services discovery and more wholesome services orchestration. We apply this approach to discovering semantic Web services being offered by autonomous semantic robots out in the field and for building the unified worldview common to all. Consequently, the OWA-based paradigm alone is not suitable for most robotics platforms where cardinality and negation is not sound as well as in CWA. Therefore, we argue and display that a strategy to draw and utilize benefits of both world assumptions is beneficial.
Atilla Elçi, Behnam Rahnama, Saman Kamran
COMPSAC3