Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/muety/kit-lod16-knowledge-panel
Linked Open Data-based knowledge panel built during a seminar at Karlsruhe Institute of Technology
https://github.com/muety/kit-lod16-knowledge-panel
dbpedia karlsruhe-institute-of-technology linked-data ranking rdf semantic-web
Last synced: 15 days ago
JSON representation
Linked Open Data-based knowledge panel built during a seminar at Karlsruhe Institute of Technology
- Host: GitHub
- URL: https://github.com/muety/kit-lod16-knowledge-panel
- Owner: muety
- Created: 2017-02-07T14:14:31.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-02-20T08:31:20.000Z (almost 8 years ago)
- Last Synced: 2024-11-04T00:32:46.683Z (about 1 month ago)
- Topics: dbpedia, karlsruhe-institute-of-technology, linked-data, ranking, rdf, semantic-web
- Language: Java
- Homepage:
- Size: 3.02 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-starred - muety/kit-lod16-knowledge-panel - Linked Open Data-based knowledge panel built during a seminar at Karlsruhe Institute of Technology (others)
README
# Linked Open Data Seminar 2016 - Knowledge Panel
### Description
This is a project in the context of the _Linked Open Data_ (LOD) Seminar at [AIFB](http://aifb.kit.edu) at the [Karlsruhe Institute of Technology](http://kit.edu).
Goal was basically to integrate multiple LOD sources (in a first step only [DBPedia](http://dbpedia.org) and [Yago](http://yago-knowledge.org)) to build a knowledge panel or fact box (as known from Google or Wikipedia) on that basis.
A major challenge was how to determine which properties of an entity, e.g. [dbp:Karlsruhe](https://dbpedia.org/resource/Karlsruhe) are relevant and meaningful to be displayed to the user and which are not. Accordingly, a ranking of properties for specific entities or classes (`rdf:type`) of entities had to be elaborated, which is capable of ranking properties among multiple, distinct sources.
While [[1](http://doi.org/10.1016/j.future.2015.04.018)] already presented a good solution (although only working for one dataset, namely DBPedia) based on supervised machine learning, our approach is based of rather naive statistical metrics like [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf).
Our evaluation is based on _rank biased overlap_ (RBO), as described in [[2](http://doi.org/10.1145/1852102.1852106)].
[1] Dessi, A., & Atzori, M. (2016). A machine-learning approach to ranking RDF properties. Future Generation Computer Systems, 54, 366–377. http://doi.org/10.1016/j.future.2015.04.018
[2] Webber, W., Moffat, A., & Zobel, J. (2010). A similarity measure for indefinite rankings. ACM Transactions on Information Systems, 28(4), 1–38. http://doi.org/10.1145/1852102.1852106
### Implementation
The project consist of four software components.
* __Preprocessing scripts__: Responsible for extracting statistics from LOD graphs and calculating TF and IDF on that base
* __Backend__: Responsible for computing entity-specific, multi-source property ranking at runtime as well as constructing a combined JSON-LD serialized RDF graph from DBPedia and Yago on that base. Exposed as a RESTful webservice.
* __Frontend__: Single Page App as user interface, which queries the backend based in a user input and prints a knowledge panel based on the response's RDF graph.
* __Evaluation__: Scripts facilitating "manual" computation of RBO metrics for specific entities.
#### UML component diagram
#### UML sequence diagram
### Team
- Han Che
- Benny Rolle
- [Ferdinand Mütsch](https://ferdinand-muetsch.de)
### License
MIT