Using Knowledge Anchors to Facilitate User Exploration of Data Graphs

YesThis paper investigates how to facilitate users’ exploration through data graphs for knowledge expansion. Our work focuses on knowledge utility – increasing users’ domain knowledge while exploring a data graph. We introduce a novel exploration support mechanism underpinned by the subsumption theory of 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 operationalising the subsumption theory for meaningful learning to generate exploration paths for knowledge expansion is the automatic identification of knowledge anchors in a data graph (KADG). We present several metrics for identifying KADG which are evaluated against familiar concepts in human cognitive structures. A subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion is presented, and applied in the context of a Semantic data browser in a music domain. The resultant exploration paths are evaluated in a task-driven experimental user study compared to free data graph exploration. The findings show that exploration paths, based on subsumption and using knowledge anchors, lead to significantly higher increase in the users’ conceptual knowledge and better usability than free exploration of data graphs. The work opens a new avenue in semantic data exploration which investigates the link between learning and knowledge exploration. This extends the value of exploration and enables broader applications of data graphs in systems where the end users are not experts in the specific domain

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

Combining Flexible Queries and Knowledge Anchors to facilitate the exploration of Knowledge Graphs

Semantic web and information extraction technologies are enabling the creation of vast information and knowledge repositories, particularly in the form of knowledge graphs comprising entities and the relationships between them. Users are often unfamiliar with the complex structure and vast content of such graphs. Hence, users need to be assisted by tools that support interactive exploration and flexible querying. In this paper we draw on recent work in flexible querying for graph-structured data and identifying good anchors for knowledge graph exploration in order to demonstrate how users can be supported in incrementally querying, exploring and learning from large complex knowledge graphs. We demonstrate our techniques through a case study in the domain of lifelong learning and career guidance

Evaluating Knowledge Anchors in Data Graphs against Basic Level Objects

The growing number of available data graphs in the form of RDF Linked Da-ta enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they in-teract with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by us-ers who are not domain experts. We propose a new navigation support ap-proach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cog-nitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two ap-plication domains - semantic browsing (Music) and semantic search (Ca-reers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts

Combining flexible queries and knowledge anchors to facilitate the exploration of knowledge graphs

Semantic web and information extraction technologies are enabling the creation of vast information and knowledge repositories, particularly in the form of knowledge graphs comprising entities and the relationships between them. Users are often unfamiliar with the complex structure and vast content of such graphs. Hence, users need to be assisted by tools that support interactive exploration and flexible querying. In this paper we draw on recent work in flexible querying for graph-structured data and identifying good anchors for knowledge graph exploration in order to demonstrate how users can be supported in incrementally querying, exploring and learning from large complex knowledge graphs. We demonstrate our techniques through a case study in the domain of lifelong learning and career guidance

Dynamic Influence Networks for Rule-based Models

We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres