Mining for Topics to Suggest Knowledge Model Extensions

Abstract

Electronic concept maps, interlinked with other concept maps and multimedia resources, can provide rich knowledge models to capture and share human knowledge. This article presents and evaluates methods to support experts as they extend existing knowledge models, by suggesting new context-relevant topics mined from Web search engines. The task of generating topics to support knowledge model extension raises two research questions: first, how to extract topic descriptors and discriminators from concept maps; and second, how to use these topic descriptors and discriminators to identify candidate topics on the Web with the right balance of novelty and relevance. To address these questions, this article first develops the theoretical framework required for a “topic suggester” to aid information search in the context of a knowledge model under construction. It then presents and evaluates algorithms based on this framework and applied in E xtender , an implemented tool for topic suggestion. E xtender has been developed and tested within CmapTools, a widely used system for supporting knowledge modeling using concept maps. However, the generality of the algorithms makes them applicable to a broad class of knowledge modeling systems, and to Web search in general.

A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval

“…Building knowledge models is a difficult and costly task. There are several initiatives aimed at providing intelligent support to facilitate the construction of knowledge models, as is the case of the family of intelligent suggesters for concept mapping described in [17,20].…”

Mining Event-Oriented Topics in Microblog Stream with Unsupervised Multi-View Hierarchical Embedding

“…To overcome this problem, coherent topic models are developed to improve the coherence of the LDA's latent topics. Usually, the coherent topic models are linked to the knowledge bases (Chen and Liu 2014a,b;Lorenzetti et al 2016), because the domain information is helpful to enhance their semantic interpretation. The coherent topics, together with their hierarchical constructions, have been extensively applied in social media user generated content analysis (Zhu et al 2014), achieving reasonably sound performance.…”

Searching in the Context of a Task: A Review of Methods and Tools

“…A survey of various techniques for detecting query intents using click-through and implicit relevance feedback data is presented in [84]. Accessing digital libraries based on topical context [106,107,108] Implicit feedback from user interaction with software tools [67,66,109,110] Query expansion and refinement with explicit user intervention [62,63] Query generation, augmentation and/or refinement from context without user intervention [24,27,36,37,49,111,112] Context sensitive query autocompletion [59,60,61] Query generation and rank-biasing based on context [49,50,113] Query understanding or disambiguation based on context [58,64,114] Implicit feedback from cursor movement, vertical scrolling, interactions in the areas of interest and/or eyetracking [71,72,73] Touch interaction data on mobile devices [74,75,76]…”

“…This approach allows assessing the performance of any topic-based retrieval method when tested on the given set of topics and web pages. The framework was successfully applied in the evaluation of different topic-based search methods in terms of precision, semantic precision and recall [24,37,112]. While precision and recall are important goals in topical search, some other priorities should also be considered depending on the task at hand.…”

“…Another context-based search tool, the EXTENDER system [135,136,111,112], searches the Web based on a knowledge model (in the form of a concept map) that is being constructed by a user. EXTENDER applies an incremental technique to build up context descriptions.…”