Collaborative exploratory search for information filtering and large‐scale information triage

Abstract

Modern information seekers face dynamic streams of large-scale heterogeneous data that are both intimidating and overwhelming. They need a strategy to filter this barrage of massive data sets, and to find all of the information responding to their information needs, despite the pressures imposed by schedules and budgets. In this applied research, we present an exploratory search strategy that allows professional information seekers to efficiently and effectively triage all of the data. We demonstrate that exploratory search is particularly useful for information filtering and large-scale information triage, regardless of the language of the data, and regardless of the particular industry, whether finance, medical, business, government, information technology, news, or legal. Our strategy reduces a dauntingly large volume of information into a manageable, high-precision data set, suitable for focused reading. This strategy is interdisciplinary, integrating concepts from information filtering, information triage, and exploratory search. Key aspects include advanced search software, interdisciplinary paired search, asynchronous collaborative search, attention to linguistic phenomena, and aggregated search results in the form of a search matrix or search grid. We present the positive results of a taskoriented evaluation in a real-world setting, discuss these results from a qualitative perspective, and share future research areas.

Comprehensive Review and Future Research Directions on Dynamic Faceted Search

“…The second category included 35 related research articles. This category was divided into five subcategories as follows: hybrid strategy [46]; model structure [47,48]; formal concept analysis (FCA) [49]; lightweight ontology [50]; and partitioning [51]. These works were presented to improve the reachability of relevant information objects and user behavior.…”

Interfaces for Searching and Triaging Large Document Sets: An Ontology-Supported Visual Analytics Approach

“…For this issue, information triage may be required to further reduce the number of documents into a usable collection of task-relevant documents. During information triage, a user's primary objective is to inspect, contextualize, and make timely relevance decisions on search results [22]. For this to occur, existing research [23][24][25] suggests that, while still able to assess document relevance to information-seeking objectives, tools must allow users to encounter and perform rapid triaging on large sets of documents in a non-linear fashion.…”

Keywords-Driven Paper Recommendation Based on Undirected Paper Citation Graph

“…With the increasing maturity of recommender system technology, users use some paper recommender websites (e.g., Google Scholar and Baidu Academic) to seek required paper. Furthermore, some the information filtering tools [1], such as search engines, also help users to search for papers. Generally, since a paper may contain partial keywords that users are requested, the recommender system needs to analyze users' requirement and return a set of papers that collectively contain all query keywords.…”

“…As formally depicted in Figure 1, the common process of paper recommendation [2] mainly consists of three phases: the first phase is manuscript keywords, where users analyze their research requirements and submit some query keywords, e.g., the keyword 1 is the paper recommendation, the keyword 2 is the keyword search, the keyword 3 is the Steiner tree and the keyword 4 is the dynamic programming; the second phase is paper discovery, where the recommender system automatically identifies diverse sets of candidate papers; the third phase is paper selection, where the recommender system recommends the candidate papers that contain query keywords to users. Frankly, these recommended papers that fail to satisfy users' requirements on deep and continuous research on certain content or topic as these papers may belong to the wide variety of research domains or aren't correlated.…”