Search (9 results, page 1 of 1)

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Abstract

A large topic map was created to facilitate understanding of the impacts of the 2010 Gulf of Mexico Oil Spill Incident. The topic map has both a text and graphical interface, which complement each other. A formative evaluation and two summative evaluations were conducted, as qualitative studies, to assess the usefulness and usability of the large topic maps for facilitating self-regulated learning. The topic maps were found useful for knowledge fusion and discovery, and can be useful when undertaking interdisciplinary and multidisciplinary research. Users reported some usability issues about the graphical topic map, including information overload and cluttered display of topics when displaying large number of topics and their associated topics. The text topic map was found easier to use due to displaying topics, relationships and references in a linear view.

Type

a

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Abstract

An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.

Type

a

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Abstract

Previous literature indicates that political cartoons are difficult to index because they have a subjective nature, and indexers may fail to understand the content of a cartoon or may interpret its content subjectively. This study aims to investigate the indexability of historical, political cartoons and the variables that affect the indexing results. It proposes an indexing scheme for describing historical, political cartoons, and uses that indexing scheme to conduct indexing experiments. Through indexing experiments and statistical analysis, three variables, which affect the indexing results, are identified: indexers, indexing fields, and cartoons. There is a statistically significant difference in inter-indexer consistency on indexers, indexing fields, and cartoons. The paper argues that historical, political cartoons can be indexed if knowledgeable indexers are available, and the context of the cartoons is provided. It also proposes a mediated, collaborative indexing approach to indexing such materials.

Type

a

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Abstract

The goal of this study is to develop a taxonomy of earthquake response and recovery using online information re-sources for organizing and sharing earthquake-related online in-formation resources. A constructivist/interpretivist research par-adigm was used in the study. A combination of top-down and bottom-up approaches was used to build the taxonomy. Facet analysis of disaster management, the timeframe of disaster man-agement, and modular design were performed when designing the taxonomy. Two case studies were done to demonstrate the usefulness of the taxonomy for organizing and sharing infor-mation. The facet-based taxonomy can be used to organize online information for browsing and navigation. It can also be used to index and tag online information resources to support searching. It creates a common language for earthquake manage-ment stakeholders to share knowledge. The top three level cate-gories of the taxonomy can be applied to the management of other types of disasters. The taxonomy has implications for earthquake online information management, knowledge manage-ment and disaster management. The approach can be used to build taxonomies for managing online information resources on other topics (including various types of time-sensitive disaster re-sponses). We propose a common language for sharing infor-mation on disasters, which has great social relevance.

Type

a

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Abstract

F1000 recommendations were assessed as a potential data source for research evaluation, but the reasons for differences between F1000 Article Factor (FFa scores) and citations remain unexplored. By linking recommendations for 28,254 publications in F1000 with citations in Scopus, we investigated the effect of research level (basic, clinical, mixed) and article type on the internal consistency of assessments based on citations and FFa scores. The research level has little impact on the differences between the 2 evaluation tools, while article type has a big effect. These 2 measures differ significantly for 2 groups: (a) nonprimary research or evidence-based research are more highly cited but not highly recommended, while (b) translational research or transformative research are more highly recommended but have fewer citations. This can be expected, since citation activity is usually practiced by academic authors while the potential for scientific revolutions and the suitability for clinical practice of an article should be investigated from a practitioners' perspective. We conclude with a recommendation that the application of bibliometric approaches in research evaluation should consider the proportion of 3 types of publications: evidence-based research, transformative research, and translational research. The latter 2 types are more suitable for assessment through peer review.

Type

a

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Abstract

The paper presents the rationale, significance, method and procedure of building a taxonomy of semantic relations in the oil spill domain for supporting knowledge discovery through inference. Difficult problems during the development of the taxonomy are discussed and partial solutions are proposed. A preliminary functional evaluation of the taxonomy for supporting knowledge discovery was performed. Durability an expansibility of the taxonomy were evaluated by using the taxonomy to classifying the terms in a biomedical relation ontology. The taxonomy was found to have full expansibility and high degree of durability. The study proposes more research problems than solutions.

Type

a

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Abstract

Modeling multidimensional relevance in information retrieval (IR) has attracted much attention in recent years. However, most existing studies are conducted through relatively small-scale user studies, which may not reflect a real-world and natural search scenario. In this article, we propose to study the multidimensional user relevance model (MURM) on large scale query logs, which record users' various search behaviors (e.g., query reformulations, clicks and dwelling time, etc.) in natural search settings. We advance an existing MURM model (including five dimensions: topicality, novelty, reliability, understandability, and scope) by providing two additional dimensions, that is, interest and habit. The two new dimensions represent personalized relevance judgment on retrieved documents. Further, for each dimension in the enriched MURM model, a set of computable features are formulated. By conducting extensive document ranking experiments on Bing's query logs and TREC session Track data, we systematically investigated the impact of each dimension on retrieval performance and gained a series of insightful findings which may bring benefits for the design of future IR systems.

Type

a

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Abstract

Two general challenges faced by data analysis are the existence of noise and the extraction of meaningful information from collected data. In this study, we used a multiscale framework to reduce the effects caused by noise and to extract explainable geometric properties to characterize finite metric spaces. We conducted lab experiments that integrated the use of eye-tracking, electrodermal activity (EDA), and user logs to explore users' information-seeking behaviors on search engine result pages (SERPs). Experimental results of 1,590 search queries showed that the proposed strategies effectively predicted query-level user satisfaction using EDA and eye-tracking data. The bootstrap analysis showed that combining EDA and eye-tracking data with user behavior data extracted from user logs led to a significantly better linear model fit than using user behavior data alone. Furthermore, cross-user and cross-task validations showed that our methods can be generalized to different search engine users performing different preassigned tasks.

Type

a

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Abstract

Understanding the behavior and characteristics of web users is valuable when improving information dissemination, designing recommendation systems, and so on. In this work, we explore various methods of predicting the ratio of male viewers to female viewers on YouTube. First, we propose and examine two hypotheses relating to audience consistency and topic consistency. The former means that videos made by the same authors tend to have similar male-to-female audience ratios, whereas the latter means that videos with similar topics tend to have similar audience gender ratios. To predict the audience gender ratio before video publication, two features based on these two hypotheses and other features are used in multiple linear regression (MLR) and support vector regression (SVR). We find that these two features are the key indicators of audience gender, whereas other features, such as gender of the user and duration of the video, have limited relationships. Second, another method is explored to predict the audience gender ratio. Specifically, we use the early comments collected after video publication to predict the ratio via simple linear regression (SLR). The experiments indicate that this model can achieve better performance by using a few early comments. We also observe that the correlation between the number of early comments (cost) and the predictive accuracy (gain) follows the law of diminishing marginal utility. We build the functions of these elements via curve fitting to find the appropriate number of early comments (approximately 250) that can achieve maximum gain at minimum cost.

Type

a