Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia

Lamprecht, Daniel; Strohmaier, Markus; Helić, Denis; Nyulas, Csongor; Tudorache, Tania; Noy, Natalya F.; Musen, Mark A.

doi:10.3233/sw-140143

Cited by 19 publications

(12 citation statements)

References 32 publications

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“…The benchmark presents a set of information-seeking tasks and metrics for measuring the effectiveness of completing the tasks. The evaluation approach in [77] aims to identify whether the simulated exploration paths information networks are similar to those produced by human exploration. We will adopts the established task-based approach and will utilise an educational taxonomy for assessing conceptual knowledge to assess knowledge utility and usability of the generated exploration paths.…”

Section: Data Exploration Evaluation Approachesmentioning

confidence: 99%

Using knowledge anchors to facilitate user exploration of data graphs

Al-Tawil

Dimitrova

Thakker

2020

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This paper investigates how to facilitate users' exploration through data graphs. The prime focus is on knowledge utility, i.e. increasing a user's domain knowledge while exploring a data graph, which is crucial in the vast number of user-facing semantic web applications where the users are not experts in the domain. We introduce a highly unique exploration support mechanism underpinned by the subsumption theory for meaningful learning. A core algorithmic component for operationalising the subsumption theory for meaningful learning 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. The second key component is a subsumption algorithm that utilises KADG for generating exploration paths for knowledge expansion. The implementation of the algorithm is 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. We provide the first framework that adopts educational theories to inform data graph exploration for knowledge expansion which 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.

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Section: Data Exploration Evaluation Approachesmentioning

confidence: 99%

Using knowledge anchors to facilitate user exploration of data graphs

Al-Tawil

Dimitrova

Thakker

2020

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“…In case no unvisited item is present, the simulation backtracks to the previously visited item. Greedy search has been used in the previous work to analyze navigation dynamics in networks [ 39 , 40 ] and found to produce comparable results to human navigation patterns [ 41 , 42 ].…”

Section: Evaluating Navigabilitymentioning

confidence: 99%

A method for evaluating discoverability and navigability of recommendation algorithms

2017

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Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no clear categorization of items exists. Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any recommendation evaluation measures proposed so far. In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the discoverability and navigability of recommendation algorithms. The proposed method tackles this by means of first evaluating the discoverability of recommendation algorithms by investigating structural properties of the resulting recommender systems in terms of bow tie structure, and path lengths. Second, the method evaluates navigability by simulating three different models of information seeking scenarios and measuring the success rates. We show the feasibility of our method by applying it to four non-personalized recommendation algorithms on three data sets and also illustrate its applicability to personalized algorithms. Our work expands the arsenal of evaluation techniques for recommendation algorithms, extends from a one-click-based evaluation towards multi-click analysis, and presents a general, comprehensive method to evaluating navigability of arbitrary recommendation algorithms.

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“…Most of the existing research in web mining apply traditional data mining techniques [32], [33], [34], [35], [36]. The objectives of these works are to improve web site usages [32], [33] and to create recommender systems [34], [35].…”

Section: Related Workmentioning

confidence: 99%

“…The objectives of these works are to improve web site usages [32], [33] and to create recommender systems [34], [35]. Ontologies are also used to model the human behavior when navigating in information networks [36].…”

Section: Related Workmentioning

confidence: 99%

Process mining for recommender strategies support in news media

Epure

Ingvaldsen

Deneckère

et al. 2016

2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)

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The strategic transition of media organizations to personalized information delivery has urged the need for richer methods to analyze the customers. Though useful in supporting the creation of recommender strategies, the current data mining techniques create complex models requiring often an understanding of techniques in order to interpret the results. This situation together with the recommender technologies deluge and the particularities of the news industry pose challenges to the news organization in making decisions about the most suitable strategy. Therefore, we propose process mining as a high-level, end-toend solution to provide insights into the consumers' behavior and content dynamics. Specifically, we explore if it allows news organizations to analyze independently and effectively their data in order to support them in defining recommender strategies. The solution was implemented in a case study with the third largest news provider in Norway and yielded preliminary positive results. To our knowledge, this is the first attempt to apply a process mining methodology and adapt the techniques to support media industry with the recommender strategies.

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Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia

Cited by 19 publications

References 32 publications

Using knowledge anchors to facilitate user exploration of data graphs

Using knowledge anchors to facilitate user exploration of data graphs

A method for evaluating discoverability and navigability of recommendation algorithms

Process mining for recommender strategies support in news media

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