SEMANTICS VISUALIZATION FOR FOSTERING SEARCH RESULT ... · SEMANTICS VISUALIZATION FOR FOSTERING SEARCH RESULT COMPREHENSION t Christian Stab Fraunhofer-Institute for Computer Graphics

© Fraunhofer IGD

SEMANTICS VISUALIZATION FOR FOSTERING SEARCH RESULT COMPREHENSION

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Christian Stab Fraunhofer-Institute for Computer Graphics Research IGD Fraunhoferstraße 5 64283 Darmstadt - Germany Tel +49 6151 155 – 557 | Fax – 139 [email protected] www.igd.fraunhofer.de

Semantics Visualization for Fostering Search Result Comprehension – ESWC 2012, 31.05.2012 – Christian Stab

© Fraunhofer IGD

• Introduction

• Visualizing Search Results in Semantic Domains

• Application Scenario

• Evaluation

• Future Work & Conclusion

OVERVIEW

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• Optimal use of data plays a major role

• global competition & competitiveness of industrial companies

• Semantic technologies provide adequate tools for improving data handling

• e.g. linking heterogeneous data, generating broader context, facilitating information access, …

• … and are adopted in daily search processes

• Usually, search results are presented in sorted and mainly textual lists

• Semantic information remains in most cases hidden from the user, though this information supports users in information seeking tasks

INTRODUCTION

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• Novel approach that utilizes information visualization and semantic information

• Support for relevance assessment

• Offers more transparency in the search result generation

• Visualization of Query-Result-Relations

• fine-grained visual overview of search result relevancies

• Visual feedback for query-enhancement

• Allows users to narrow search results and to refine individual search processes

INTRODUCTION

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• Visualization approach is part of the SemaVis-Framework

• adaptable visualization framework for semantics

• contains different and aspect oriented visualization techniques (e.g. geo, time,…)

• used in several application scenarios and use cases

• Presented visualization approach is based and a force-based layout algorithm

• places nodes in a two-dimensional space by assigning different forces to the edges and nodes of the graph

• these forces are adapted in an iterative simulation until a mechanical equilibrium is reached

• Approach uses a weighted model based on similarities emerged during the semantic retrieval process

VISUALIZING SEMANTIC SEARCH RESULTS

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www.semavis.net

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Two different node types:

Attribute Nodes

• are placed in a circular form during the initialization of the visualization

• users can freely move them on the surface

Result Nodes

• represent the results of the search

• are suspended between the attribute nodes

• Positioned by the force-layout algorithm according to their similarities and relations to attribute nodes

VISUALIZING SEMANTIC SEARCH RESULTS

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• Each query term is represented in an attribute node

• Semantic meaning is shown in the node label for unambiguous assessment of the results

• Relations between results and query terms are visualized with directed edges

• User are able to unambiguously determine most relevant results

VISUALIZING SEMANTIC SEARCH RESULTS Query-Result-Relations

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• Search results can be found by considering semantic structures

• e.g. query terms match only to semantically related resources

• To enable users to comprehend why specific results are considered as relevant adequate feedback about the reasons of result generation is needed

• In other words: Resources that are responsible for result retrieval should also be visible to the user

• Solution: Expandable Attribute Nodes

• contains resources from the semantic neighborhood that are of some relevance for result generation

• labels of these expandable attribute nodes are derived from the conjoint concept in the semantic structure

VISUALIZING SEMANTIC SEARCH RESULTS Semantic neighborhood and hierarchical attributes

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VISUALIZING SEMANTIC SEARCH RESULTS Semantic neighborhood and hierarchical attributes

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For improving the visualization different similarity values are utilized and mapped to visual properties (e.g. length, color, size, …)

Partial S imilarities

• A measure between attributes nodes and results

• Represent the relevance of a retrieved resource to a given query term

• adapt the weights of edges between results and attribute nodes

Result S imilarities

• aggregated values of all partial similarities

• Correspond to the overall relevance of a retrieved result

• mapped to size and color of result nodes

VISUALIZING SEMANTIC SEARCH RESULTS Mapping results’ relevance to visual properties

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• Common search strategy includes to reformulate or to enhance the query

• Transferred to the presented visualization approach this corresponds to substitutions or reassignments of attribute nodes

• instantiation of additional attributes defines a more specific search condition

• removal of attribute values results in wider-ranged and less specific search spaces

• Un-instantiated attributes are recommended by the visualization

• these are retrieved by attributes of the current result set

• are visualized as additional attributes

• The effect of changing the search condition is immediately visible

• the representation of query-result-relations reveals which of the current results fulfills new conditions.

VISUALIZING SEMANTIC SEARCH RESULTS Visual feedback for query enhancement

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VISUALIZING SEMANTIC SEARCH RESULTS Visual feedback for query enhancement

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• The visualization approach is applied in the field of mechanical engineering

• highly complex processes (from initial development over construction to production steps and consumer services)

• diverse user groups

• Same resource is treated in different context

• Semantic technologies are used to link different knowledge bases and to provide a novel way to access information

• Objective of the presented visualization:

• provide homogeneous access to a combined knowledge base

• providing more transparency in the search processing

• enable users to unambiguously assess the results of individual search processes

• demonstrator is available at http://athena.igd.fraunhofer.de/Processus/semavis.html

APPLICATION SCENARIO

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http://athena.igd.fraunhofer.de/Processus/semavis.html

© Fraunhofer IGD

• Comparison of the visualization with a common list presentation

• Goal of the evaluation is to answer questions like

• Is the user really supported in assessing search results?

• Satisfies our approach the needs of users?

• H1: There is a difference in task completion time between the list representation and the visualization in assessing search results

• Additionally we measured the user satisfaction as a subjective evaluation criteria

EVALUATION

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• Basic within-group design

• Each participant accomplishes the given tasks in each condition (list presentation and visualization)

• Possible learn-effects when switching between conditions are controlled by

• systematic randomization of condition- and task-ordering

• advising participants to disregard knowledge from previous conditions and explicitly showing the solutions

• Three tasks; accomplished from each participant in each condition

• Pre-assigned queries to ensure that each participant retrieves the same visual representation

• Task 1: Identify relations between each search result and the terms of the given query

• Task 2: Similar to task 1 but with hierarchical structured attributes in the result set

• Task 3: Identify most relevant item for a specific search situation

• Time limit for each task: 3 Minutes

• Wrong answers were also estimated with 3 minutes

EVALUATION Experimental Design

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• 17 participants attended the evaluation

• mainly graduates and students

• average age 24 - 29 years

• participants were mainly involved in computer science (M = 4.65; SD = 0.6)1

• no previous knowledge of the engineering domain

• Procedure for each participant

• 1. General Introduction

• 2. Brief introduction to both systems

• 3. Task completion; after each task satisfaction ranking and five additional questions concerning the subjective opinion

• 4. brief demographic questionnaire

1 measured on a five point Likert scale (5 = very much experience; 1 = very little experience) in the demographic part of the questionnaire

EVALUATION Procedure

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• Participants performed better with the visualization

• List Presentation: avg(t) = 88.1 sec; SD = 30.1

• Visualization: avg(t) = 51.3 sec; SD = 25.8

• Significant difference in task completion times (t(50) = 7.8028; p<0.05)

• User Satisfaction ratings2

• List Presentation: 5.31; SD = 1.91

• Visualization: 7.57; SD = 1.1

• Participants prefer the visualization 3

• Question: “Would you use the system in the future for similar searching tasks?”

• List Presentation: 3.24; SD = 0.87

• Visualization: 4.25; SD = 0.77

2 Measured on a scale from 1 - 9 after each task

3 Measured on a five-point Likert scale

EVALUATION Results

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EVALUATION Results

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• Transfer to other domains

• Visualization was applied in the field of mechanical engineering

• in this domain data diversity, user roles and processes are controllable

• data access methods can be accurately aligned to specific tasks

• further investigations are needed to prove if the proposed approach is also transferable to other domains

• User acceptance

• most search user interfaces are based on result list presentations

• cause of the public’s great familiarity there is a certain degree of risk with the introduction of novel technologies

• even if novel approaches provide a variety of extended features and easier information access it is not sure if user accept a novel technology

DISCUSSION

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• Novel approach for search result visualization that utilizes semantics

• Successfully fosters search result comprehension

• Supports users in assessing retrieved resources

• Imparts different semantic interpretations of query terms

• Visual feedback for query enhancement fosters common search strategies and offers more transparency in search result processing

• Future Work

• Extension of query refinement feature: removal / change and multiple instantiation of attribute nodes

• Integration and transfer to other domains

CONCLUSION & FUTURE WORK

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THANK YOU FOR LISTENING

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Christian Stab Fraunhofer-Institute for Computer Graphics Research Fraunhoferstraße 5 64283 Darmstadt - Germany Tel +49 6151 155 – 557 | Fax – 139 [email protected] www.igd.fraunhofer.de

Semantics Visualization for Fostering Search Result Comprehension – ESWC 2012, dd.05.2012 – Christian Stab

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SEMANTICS VISUALIZATION FOR FOSTERING SEARCH RESULT ... · SEMANTICS VISUALIZATION FOR FOSTERING SEARCH RESULT COMPREHENSION t Christian Stab Fraunhofer-Institute for Computer Graphics