Intelligent Support for Exploration of Data Graphs

This research investigates how to support a user’s exploration through data graphs generated from semantic databases in a way leading to expanding the user’s domain knowledge. To be effective, approaches to facilitate exploration of data graphs should take into account the utility from a user’s point of view. Our work focuses on knowledge utility – how useful exploration paths through a data graph are for expanding the user’s knowledge. The main goal of this research is to design an intelligent support mechanism to direct the user to ‘good’ exploration paths through big data graphs for knowledge expansion. We propose a new exploration support mechanism underpinned by the subsumption theory for meaningful learning, which postulates that new knowledge is grasped by starting from familiar concepts in the graph which serve as knowledge anchors from where links to new knowledge are made. A core algorithmic component for adapting the subsumption theory for generating exploration paths is the automatic identification of Knowledge Anchors in a Data Graph (KADG). Several metrics for identifying KADG and the corresponding algorithms for implementation have been developed and evaluated against human cognitive structures. A subsumption algorithm which utilises KADG for generating exploration paths for knowledge expansion is presented and evaluated in the context of a semantic data browser in a musical instrument domain. The resultant exploration paths are evaluated in a controlled user study to examine whether they increase the users’ knowledge as compared to free exploration. The findings show that exploration paths using knowledge anchors and subsumption lead to significantly higher increase in the users’ conceptual knowledge. The approach can be adopted in applications providing data graph exploration to facilitate learning and sensemaking of layman users who are not fully familiar with the domain presented in the data graph

Information Associated with ‘Intelligent Support for Exploration of Data Graphs’ (PhD thesis)

Examples of interface codes and materials from experimental user studies used in the PhD thesis. An example of how to connect to a triple store and extract the class hierarchy is described. Examples that describe how to extract the closest knowledge anchor and how to use the closest knowledge anchor in generating the narrative scripts of an exploration path are also included. The set of knowledge anchors and human basic level objects identified in MusicPinta and L4All datasets are included. Examples of the generated exploration paths in MusicPinta are included

Emerging Exploration Strategies of Knowledge Graphs

The utilization of semantic web technologies has led to the development of knowledge graphs represented as triples that allow for the exploration of specific and cross-domains. Despite the advantages of semantic links between entities in facilitating user exploration, they can also lead to an overwhelming number of exploration choices that can cause confusion, frustration, uncertainty, and a sense of being lost in the abundant graph, particularly for users who are not familiar with the domain. Thus, identifying exploration strategies is critical to improving user exploration and increasing exploration utility. This study aims to identify exploration strategies that promote knowledge utility (i.e., increase users’ domain knowledge) and exploration experience (i.e., provide users with a positive and pleasant feeling). To accomplish this goal, an experimental user study was conducted, involving lay users in the musical instrument domain, where they were presented with an exploration task and then allowed to freely explore musical instruments. Parameters related to exploration paths were used to analyze the exploration patterns that users follow during their exploration. The findings reveal exploration strategies that promote knowledge utility and exploration experience. This research contributes to the literature on intelligent methods of guiding user exploration through knowledge graphs to enhance exploration effectiveness, which can have broad applications in knowledge graph utilization

Twitter Sentiment Analysis Approaches: A Survey

Twitter is one of the most popular microblogging and social networking platforms where massive instant messages (i.e. tweets) are posted every day. Twitter sentiment analysis tackles the problem of analyzing users’ tweets in terms of thoughts, interests and opinions in a variety of contexts and domains. Such analysis can be valuable for several researchers and applications that require understanding people views about a particular topic or event. The study carried out in this paper provides an overview of the algorithms and approaches that have been used for sentiment analysis in twitter. The reviewed articles are categories into four categories based on the approach they use. Furthermore, we discuss directions for future research on how twitter sentiment analysis approaches can utilize theories and technologies from other fields such cognitive science, semantic Web, big data and visualization