An Alignment Equation for Using Mind Maps to Filter Learning Queries from Google

Zualkernan, Imran A.; Abujayyab, Mohammed; Ghanam, Yaser

doi:10.1109/icalt.2006.1652393

Cited by 4 publications

(3 citation statements)

References 5 publications

(4 reference statements)

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“…In [8] and [9], the usage of Mind Maps for computer-aided tasks is suggested, but the applications are limited to tree-based Mind Maps instead of using more general graph structures. Furthermore, multiple publications exist that use Mind Maps for other tasks except pure information visualisation ( [10], [11], [12]). However none of these leverage the additional expressiveness of extending the data structure to a graph.…”

Section: A Research Management Toolsmentioning

confidence: 99%

Email triage with Mind Meshes

Harbach

Muders

Groeper

et al. 2011

5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011)

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While efficient email triage is a well-known problem, the currently used email filtering solutions are still rudimentary and machine centric. Most filtering solutions work best if humans add machine interpretable keywords to the subject line of emails. More natural filtering options, such as project membership lists or content-based filtering, are very time consuming to set up and maintain. Especially in research ecosystems, significant fluctuation in team members, organisations and projects themselves is common. Research projects are also usually managed in an adhoc manner, making static filtering rules a sub-optimal solution. In this paper we therefore introduce the Mind Mesh paradigm, a novel human-friendly information management approach. Based on a self-describing graph structure, organisational structures, projects, and collaborations are modelled. Leveraging this information, the system can automate and support administrative processes such as email filtering, based on an understanding of the project structures. It relieves users of the necessity to maintain machine interpretable rules by hand and adapts to new situations by itself.

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Section: A Research Management Toolsmentioning

confidence: 99%

Email triage with Mind Meshes

Harbach

Muders

Groeper

et al. 2011

5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011)

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“…Furthermore, Internet search queries and online social media reflect the collective consciousness of the society [ 1 , 11 , 12 ]. Google’s page rank algorithm is also based on collective consciousness [ 13 ]. Preis et al [ 14 ] developed the future orientation index of the people of a country by utilizing search query data and calculating the ratio of the future year phrase of the Google search queries index to that of the past year.…”

Section: Introductionmentioning

confidence: 99%

Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries

Lee¹,

Lee²,

Choi³

2016

J Med Internet Res

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BackgroundInternet search query data reflect the attitudes of the users, using which we can measure the past orientation to commit suicide. Examinations of past orientation often highlight certain predispositions of attitude, many of which can be suicide risk factors.ObjectiveTo investigate the relationship between past orientation and suicide rate by examining Google search queries.MethodsWe measured the past orientation using Google search query data by comparing the search volumes of the past year and those of the future year, across the 50 US states and the District of Columbia during the period from 2004 to 2012. We constructed a panel dataset with independent variables as control variables; we then undertook an analysis using multiple ordinary least squares regression and methods that leverage the Akaike information criterion and the Bayesian information criterion.ResultsIt was found that past orientation had a positive relationship with the suicide rate (P≤.001) and that it improves the goodness-of-fit of the model regarding the suicide rate. Unemployment rate (P≤.001 in Models 3 and 4), Gini coefficient (P≤.001), and population growth rate (P≤.001) had a positive relationship with the suicide rate, whereas the gross state product (P≤.001) showed a negative relationship with the suicide rate.ConclusionsWe empirically identified the positive relationship between the suicide rate and past orientation, which was measured by big data-driven Google search query.

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“…Mindmaps received attention in various research fields. They have been used to implement a lambda calculator [2], to filter search results from Google [3], to present software requirements [4], to research how knowledgeable business school students are, and there are numerous studies about the effectiveness of mind-maps as a learning tool [5]. In the field of document engineering and text mining, mind maps were created automatically from texts [6], and have been utilized to model XML files [7].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of User Modeling Based on Mind Maps

Beel¹,

Langer²,

Kapitsaki

et al. 2015

Lecture Notes in Computer Science

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Abstract. Mind maps have not received much attention in the user modeling and recommender system community, although mind maps contain rich information that could be valuable for user-modeling and recommender systems. In this paper, we explored the effectiveness of standard user-modeling approaches applied to mind maps. Additionally, we develop novel user modeling approaches that consider the unique characteristics of mind maps. The approaches are applied and evaluated using our mind mapping and referencemanagement software Docear. Docear displayed 430,893 research paper recommendations, based on 4,700 user mind maps, from March 2013 to August 2014. The evaluation shows that standard user modeling approaches are reasonably effective when applied to mind maps, with click-through rates (CTR) between 1.16% and 3.92%. However, when adjusting user modeling to the unique characteristics of mind maps, a higher CTR of 7.20% could be achieved. A user study confirmed the high effectiveness of the mind map specific approach with an average rating of 3.23 (out of 5), compared to a rating of 2.53 for the best baseline. Our research shows that mind map-specific user modeling has a high potential, and we hope that our results initiate a discussion that encourages researchers to do research in this field and developers to integrate recommender systems to their mind mapping tools.Keywords: mind map, user modeling, recommender systems IntroductionMind mapping is a visual technique to record and organize information, and to develop new ideas. As such, mind-maps are used for tasks like brainstorming, knowledge management, note taking, presenting, project planning, decision-making, and career planning. Originally, mind mapping was performed using pen and paper, but since the 1980s, more than one hundred software tools for aiding users in creating mind maps have evolved. These tools are used by an estimated two million users who create around five millions mind-maps every year [1]. Mindmaps received attention in various research fields. They have been used to implement a lambda calculator [2], to filter search results from Google [3], to present software requirements [4], to research how knowledgeable business school students are, and there are numerous studies about the effectiveness of mind-maps as a learning tool [5]. In the field of document engineering and text mining, mind maps were created automatically from texts [6], and have been utilized to model XML files [7]. In the field of user-modeling and recommender-systems research, mind maps have thus far received no attention. However, mind-maps typically contain structured information that reflects the interests, knowledge, and information needs of their users. In this regard, the content of mind-maps is comparable to the content of emails, web pages, and research articles [1]. Hence, we believe that mind maps should be equally well suited for user modeling as are other documents, such as emails [8], web-pages [9], and research articles [10].

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An Alignment Equation for Using Mind Maps to Filter Learning Queries from Google

Cited by 4 publications

References 5 publications

Email triage with Mind Meshes

Email triage with Mind Meshes

Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries

Exploring the Potential of User Modeling Based on Mind Maps

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