A utility to map arbitrary text to the WLO/OEH topics vocabulary based on keyword matching.
- Install Docker.
- Build the Docker container.
sh build.sh
- To test the prediction just execute the script with an arbitrary text.
sh runPrediction.sh "Im Englisch Unterricht behandeln wir heute Verben, Past Perfect und False Friends"
The result is a JSON output containing a tree like:
WLO
├── Englisch (4) [englisch]
│ ├── Grammatik (2)
│ │ └── Verben (2) [verben]
│ │ └── Past (1) [past]
│ └── Sprache und Aussprache (1)
│ └── False friends (1) [false friends]
├── Deutsch als Zweitsprache (3)
│ ├── Grammatik (2)
│ │ ├── Adverbien (1)
│ │ │ └── Temporaladverbien (1) [heute]
│ │ └── Verben (1) [verben]
│ └── Wortschatz (1)
│ └── Schule und Studium (1) [englisch]
├── Spanisch (1)
│ └── Grammatik (1)
│ └── Verben (1) [verben]
├── Deutsch (1)
│ └── Grammatik und Sprache untersuchen (1)
│ └── Wortarten (1)
│ └── Verben (1) [verben]
└── Türkisch (1)
└── Grammatik (1)
└── Verben (1) [verben]
{"WLO": {"children": [{"Englisch (2) [englisch]": {"children": [{"Sprache und Aussprache (1)": {"children": [{"False friends (1) [false friends]": {"data": {"w": 1, "label": "False friends", "match": "false friends"}}}], "data": {"w": 1, "label": "Sprache und Aussprache"}}}], "data": {"w": 2, "uri": "http://w3id.org/openeduhub/vocabs/oehTopics/15dbd166-fd31-4e01-aabd-524cfa4d2783", "label": "Englisch", "match": "englisch"}}}, {"Deutsch als Zweitsprache (1)": {"children": [{"Wortschatz (1)": {"children": [{"Schule und Studium (1) [englisch]": {"data": {"w": 1, "label": "Schule und Studium", "match": "englisch"}}}], "data": {"w": 1, "label": "Wortschatz"}}}], "data": {"w": 1, "uri": "http://w3id.org/openeduhub/vocabs/oehTopics/26a336bf-51c8-4b91-9a6c-f1cf67fd4ae4", "label": "Deutsch als Zweitsprache"}}}], "data": {"w": 3, "uri": "http://w3id.org/openeduhub/vocabs/oehTopics/5e40e372-735c-4b17-bbf7-e827a5702b57"}}}
This tree is a subset of the OEH-Topics taxonomy. The number in brackets indicates the number of matches found in the text. This number gets accumulated along the path of a leaf to the root. The terms in square brackets indicate the matching keywords.
- To run the subject prediction tool as a simple REST based webservice, the following script can be used:
sh runService.sh
-
The scripts deploys a CherryPy webservice in a docker container listening at
http://localhost:8080/topics. -
To retrieve the topics, create a POST request and submit a json document with a text as for example:
curl -d '{"text" : "Im Englisch Unterricht behandeln wir heute Verben, Past Perfect und False Friends"}' -H "Content-Type: application/json" -X POST http://0.0.0.0:8080/topics