Preliminary Conference Agenda

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Session Overview
Completed Papers 2: Text/Data/Knowledge Mining
Thursday, 23/Mar/2017:
10:30am - 12:00pm

Session Chair: J Stephen Downie, School of Information Sciences
Location: Wuchang Hall
Location: Third Floor Capacity: 150 Size: 217㎡

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Employing bibliometric methods to identify a community, topics and protagonists of digital 3D reconstruction in the humanities

Sander Münster

Technische Universitaet Dresden, Germany

One of the core topics of information studies is quantifying and mapping of new scholarly knowledge. This paper employs methods from bibliometrics to investigate authors, structures and topics in a very particular field, virtual 3D modeling in the humanities. Even if research outcomes are limited reliable due to the relatively small and potentially flawed sample available for investigation, they indicate that 3D modeling is a emerging field of research. Several facilitators, such as people who interconnect groups of researchers, could be identified on a structural level. Automated topic mapping and qualitative methods also identified a range of topics: A scholarly discussion in the field of digital 3D modeling is primarily driven by technologies and from the perspective of digitization of cultural heritage.

Structures and Relations of Knowledge Nodes: Exploring a Knowledge Network of Disease from Precision Medicine Research Publications

Jian Qin, Ning Zou

Syracuse University, United States of America

The vast amount of DNA sequence and protein data are being explored and linked to diseases as causative factors to support clinical and healthcare decision making. These developments in data-intensive biological sciences and clinical practices raised new questions for knowledge organization systems (KOS), and taxonomies in particular. Sitting at the center of these questions is the lagging of KOS’s capabilities in responding to the rapidly changing and emerging biomedical and disease terms due to the static, hierarchical structures and disconnection with new disease data fin traditional KOSs. This paper reports a pilot study that is designed to uncover and identify the types of knowledge nodes and relationships that can help generalize a framework or model for building a Knowledge Network of Disease, or the New Taxonomy envisaged by the National Academy of Science. This pilot study examine a sample of biomedical publications and drew a knowledge map to lay out the main knowledge nodes and their relationships. A preliminary framework for constructing the Knowledge Network of Disease is discussed.

Domain-Independent Term Extraction & Term Network for Scientific Publications

Zheng Chen, Erjia Yan

Drexel University, United States of America

Term extraction is an essential tool for content-based publication analysis, and has a long history dating back to 1970s. However, previous methods are either domain-specific, or need complex model training, or relies on external resources like Wikipedia. Recent rise of cross-domain publication content analyses put forward the demand for simple and efficient domain-independent extraction method. This paper proposes a new rule-based method that adapts C-value method to publication analysis, extends it with two types of frequency lists and sigmoid functions, and develops a prototype term extraction method. Our experiment shows a marked reduction of error with better or competitive keyword recall against the C-Value method and a complex term extraction method provided by We then construct a term network by connecting adjacent terms in paragraphs and demonstrate that rich and meaningful analysis can be done on such networks through a case study on an HCI abstract corpus.

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