User-controlled mapping of significant literatures

Abstract

We apply a version of our web-based literature-mapping system to PNAS for 1971-2002, as indexed by the National Library of Medicine and the Institute for Scientific Information. Given a single input term from a user, a medical subject heading, a cocited author, or a cocited journal, PNASLINK rapidly displays views in which that term and the other 24 terms that most frequently co-occur with it in a bibliographic database are interrelated in ways suggesting fruitful combinations for document retrieval. The interrelationships are produced by two algorithms, pathfinder networks and Kohonen-style self-organizing maps. PNASLINK displays are themselves interactive interfaces that can retrieve documents from digital libraries (e.g., PNAS Online). This style of visualizing knowledge domains is called ''localized'' because it does not attempt to map the indexing of literatures in full but concentrates on the top terms in an ''associative thesaurus'' reflecting user interests. It also permits swift remappings, as the user recognizes terms worth pursuing. PNASLINK is illustrated with maps drawn from the literature of population genetics. Some comparative and evaluative comments are added, one from a domain expert indicating that the face validity of the system may be tempered by insufficient specificity in the indexing terms being mapped.

Knowledge Visualization of Most Prolific Countries and Hot-Topics of Enterprise Risk Management

“…Software Citespace by Chaomei Chen [14][15][16] in 2006, is a new tool to show the structure of research group networks, which adopted an integrated procedure of cooccurrence analysis, author co-citation analysis, pathfinder network scaling, social network analysis (SNA) and frequency analysis of topic terms [17][18][19][20][21][22][23][24]. In this paper, we try to use Citespace to map most prolific countries and current core research groups and hot topics of enterprise risk management to identify the current international situation of this hot field.…”

A few special cases: scientific creativity and network dynamics in the field of rare diseases

“…We analyzed the dynamics of semantic and social networks and identified the connections between the two, tracing the relationship between authors and their creative concepts. We developed a tool similar to ConceptLink (White et al 2004) to generate concept maps with the Unified Medical Language System (UMLS) co-occurrence database of MeSH descriptors. Thus, one node in our semantic network is a concept (MeSH term) and links among concepts are computed according to the number of co-occurrences of a pair of concepts in PubMed abstracts.…”

The centrality of pivotal points in the evolution of scientific networks

“…However, traditional co-citation analysis does not incorporate much intelligent analysis components beyond clustering. When a body of scientific literature is visualized, the support for interactive exploration tends to be limited and often isolated from real-world repositories of scientific papers, with exceptions of few systems [27].…”