14,254 research outputs found
Towards hierarchical affiliation resolution: framework, baselines, dataset
Author affiliations provide key information when attributing academic performance like publication counts. So far, such measures have been aggregated either manually or only to top-level institutions, such as universities. Supervised affiliation resolution requires a large number of annotated alignments between affiliation strings and known institutions, which are not readily available. We introduce the task of unsupervised hierarchical affiliation resolution, which assigns affiliations to institutions on all hierarchy levels (e.g. departments), discovering the institutions as well as their hierarchical ordering on the fly. From the corresponding requirements, we derive a simple conceptual framework based on the subset partial order that can be extended to account for the discrepancies evident in realistic affiliations from the Web of Science. We implement initial baselines and provide datasets and evaluation metrics for experimentation. Results show that mapping affiliations to known institutions and discovering lower-level institutions works well with simple baselines, whereas unsupervised top-level- and hierarchical resolution is more challenging. Our work provides structured guidance for further in-depth studies and improved methodology by identifying and discussing a number of observed difficulties and important challenges that future work needs to address
Large Graph Analysis in the GMine System
Current applications have produced graphs on the order of hundreds of
thousands of nodes and millions of edges. To take advantage of such graphs, one
must be able to find patterns, outliers and communities. These tasks are better
performed in an interactive environment, where human expertise can guide the
process. For large graphs, though, there are some challenges: the excessive
processing requirements are prohibitive, and drawing hundred-thousand nodes
results in cluttered images hard to comprehend. To cope with these problems, we
propose an innovative framework suited for any kind of tree-like graph visual
design. GMine integrates (a) a representation for graphs organized as
hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b)
a graph summarization methodology - CEPS. Our graph representation deals with
the problem of tracing the connection aspects of a graph hierarchy with sub
linear complexity, allowing one to grasp the neighborhood of a single node or
of a group of nodes in a single click. As a proof of concept, the visual
environment of GMine is instantiated as a system in which large graphs can be
investigated globally and locally
Biases in human behavior
The paper shows that biases in individualâs decision-making may result from the process of mental editing by which subjects produce a ârepresentationâ of the decision problem. During this process, individuals make systematic use of default classifications in order to reduce the short-term memory load and the complexity of symbolic manipulation. The result is the construction of an imperfect mental representation of the problem that nevertheless has the advantage of being simple, and yielding âsatisficingâ decisions. The imperfection origins in a trade-off that exists between the simplicity of representation of a strategy and his efficiency. To obtain simplicity, the strategyâs rules have to be memorized and represented with some degree of abstraction, that allow to drastically reduce their number. Raising the level of abstraction with which a strategyâs rule is represented, means to extend the domain of validity of the rule beyond the field in which the rule has been experimented, and may therefore induce to include unintentionally domains in which the rule is inefficient. Therefore the rise of errors in the mental representation of a problem may be the "natural" effect of the categorization and the identification of the building blocks of a strategy. The biases may be persistent and give rise to lock-in effect, in which individuals remain trapped in sub-optimal strategies, as it is proved by experimental results on stability of sub-optimal strategies in games like Target The Two. To understand why sub-optimal strategies, that embody errors, are locally stable, i.e. cannot be improved by small changes in the rules, it is considered Kauffmanâ NK model, because, among other properties, it shows that if there are interdependencies among the rules of a system, than the system admits many sub-optimal solutions that are locally stable, i.e. cannot be improved by simple mutations. But the fitness function in NK model is a random one, while in our context it is more reasonable to define the fitness of a strategy as efficiency of the program. If we introduce this kind of fitness, then the stability properties of the NK model do not hold any longer: the paper shows that while the elementary statements of a strategy are interdependent, it is possible to achieve an optimal configuration of the strategy via mutations and in consequence the sub-optimal solutions are not locally stable under mutations. The paper therefore provides a different explanation of the existence and stability of suboptimal strategies, based on the difficulty to redefine the sub-problems that constitute the building blocks of the problemâs representation
Graph Grammars for Knowledge Representation
This report consists of two papers presented at the March 1990 GRAGRA meeting in Bremen: the more general ''Representation of knowledge using graph grammars'' which argues for graphs as the universal KR formalism and the more specific ''The four musicians: analogies and expert systems -- a graphic approach'' which demonstrates the use of graphics for type inheritance and analogical reasoning
Situated and distributed cognition in artifact negotiation and trade-specific skills: A cognitive ethnography of Kashmiri carpet weaving practice
This article describes various ways actors in Kashmiri carpet weaving practice deploy a range of artifacts, from symbolic, to material, to hybrid, in order to achieve diverse cognitive accomplishments in their particular task domains: information representation, inter and intra-domain communication, distribution of cognitive labor across people and time, coordination of team activities, and carrying of cultural heritage. In this repertoire, some artifacts position themselves as naĂŻve tools in the actorsâ environment to the point of being ignored; however, their usage-in-context unfolds their cognitive involvement in the tasks. These usages-in-context are shown through artifact analysis of their routine, improvised, and opportunistic uses, where cognitive artifacts like talimâthe central artifact of this practiceâare shown to play not only multifunctional roles beyond representation, but are also complemented by trade-specific skills bearing strong cognitive implications in a task
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