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  1. Lucerne University of Applied Sciences and Arts Lucerne University of Applied Sciences and Arts
  2. Research Research
  3. X-MAS: Cross-Plattform Mediation, Association and Search Engine X-MAS: Cross-Plattform Mediation, Association and Search Engine

X-MAS: Cross-Plattform Mediation, Association and Search Engine

Researching knowledge-based value creation, knowledge sharing, and the automatic structuring of personal data.

Brief information

School:

Computer Science and Information Technology

Status:

Completed

Period:

01.09.2017 - 31.12.2018

Overview

The management of personal data is becoming increasingly important for value-added knowledge work for all economic actors in the 4th sector, as the flood of data grows exponentially and the tool landscape becomes increasingly fragmented. Since no standard solution for personal data management fits all users and every team, there are many isolated solutions.


The goal of Vision X-MAS (cross-mas) is to contextualize business-, public-, shared- and personal data across silo platforms (cross-platform, cross = X) in a meta-service as simple and intuitive as possible by automatically generating context (mediation), liniking (association) and filtering (search). To do so, we investigate automated approaches using machine learning, and social media approaches such as tagging and linking.


The following research questions are answered:
1. Automatic Tagging: How Can the Keyword Extraction Algorithm of Kaufmann et al. (2014) to be extended to keyphrase extraction?
2. Automatic networking: How can relationships between documents be recognized automaticall with relationship extraction techiques?
3. Knowledge sharing: How can distributedly stored knowledge graphs be elaborated collaboratively?
4. User Interaction: How can these algorithms be integrated into the value chain of knowledge work so that the increase in efficiency can be measured quantitatively?

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Facts

Type of project

Forschung

Internal organisations involved
  • Data Intelligence F&E
Funding
  • KTI-HSLU als Hauptgesuchsteller/in
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Persons involved: internal

Project manager
  • Michael Kaufmann
Member of project team
  • Alexander Denzler
  • Jan Eckert
  • Tobias Matter
  • Luca Mazzola
  • Patrick Siegfried
  • Andreas Waldis
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Persons involved: external

External member of project team
  • Stefan Fraefel

Publications

  • Article, review; peer reviewed (1)

    • Waldis, Andreas; Mazzola, Luca & Kaufmann, Michael (2018). Concept Extraction with Convolutional Neural Networks. Proceedings of the 7th International Conference on Data Science, Technology and Applications, 118-129.

  • Chapter/legal commentary/lexicon article (1)

    • Waldis, Andreas; Mazzola, Luca & Kaufmann, Michael (2019). Concept Recognition with Convolutional Neural Networks to Optimize Keyphrase Extraction. In Quix C., Bernardino J. (eds) (Hrsg.), Data Management Technologies and Applications. DATA 2018. Communications in Computer and Information Science, vol 862. (S. 160-188). Cham: Springer Nature Switzerland AG.

  • Theses (Bachelor/Master/Dissertation/Habilitation) (1)

    • Eiholzer, Matthias & Kaufmann, Michael (2019). Method Engineering for Automatic Tagging with Inductive Fuzzy Classification (nicht veröffentlichte Master-/Lizentiats-/Diplomarbeit). Hochschule Luzern – Informatik, Rotkreuz, Switzerland.

Brief information

School:

Computer Science and Information Technology

Status:

Completed

Period:

09/01/2017 - 12/31/2018

Project Head

Prof. Dr. Michael Kaufmann

Lecturer

+41 41 757 68 48

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