File size: 4,203 Bytes
afa7452
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8b3dcb1
24f28c6
 
 
 
 
 
afa7452
 
 
 
 
 
 
24f28c6
 
 
 
 
afa7452
 
 
 
 
24f28c6
 
 
afa7452
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
---
license: apache-2.0
language:
  - en
pretty_name: CQ2Onto
size_categories:
  - n<1K
task_categories:
  - text-generation
tags:
  - ontology-engineering
  - ontology-generation
  - term-extraction
  - benchmark
configs:
  - config_name: cq2onto
    data_files:
      - split: wine
        path: wine/cq_to_onto_wine.json
      - split: awo
        path: awo/cq_to_onto_awo.json
      - split: odrl
        path: odrl/cq_to_onto_odrl.json
      - split: water
        path: water/cq_to_onto_water.json
      - split: vgo
        path: vgo/cq_to_onto_vgo.json
      - split: swo
        path: swo/cq_to_onto_swo.json
  - config_name: cq2term
    data_files:
      - split: wine
        path: wine/cq_to_terms_wine.json
      - split: awo
        path: awo/cq_to_terms_awo.json
      - split: odrl
        path: odrl/cq_to_terms_odrl.json
      - split: water
        path: water/cq_to_terms_water.json
      - split: vgo
        path: vgo/cq_to_terms_vgo.json
      - split: swo
        path: swo/cq_to_terms_swo.json
---

# CQ2Onto Benchmark & Dataset

Benchmark for evaluating LLM-assisted ontology generation from competency questions, across six domains. For each domain the dataset provides a gold OWL ontology, two CQ files (one per evaluation task), and the annotation spreadsheet used during construction. Each Ontology contains a set of CQs for `CQ2Term`, a set of CQs for `CQ2Onto`, a owl source file that representing all CQs for `CQ2Onto`, and an excel contains all annotation process. More details can be found [here](https://github.com/oeg-upm/OntologyEngineeringBenchmark).

Two tasks:

- **CQ2Term**: given a CQ, extract all possible classes and properties.
- **CQ2Onto**: given a set of CQs, produce a full OWL ontology.



## Dataset Construction

We have selected six ontologies in three diferent scales:


| Ontology | Tier | Source CQs | Retained | New ⋆ | CQ2Onto set | CQ2Term set |
|----------|------|-----------:|---------:|------:|------------:|------------:|
| Wine     | small  | 7   | 4   | 1 | 5  | 5  |
| AWO      | small  | 14  | 7   | 0 | 7  | 7  |
| ODRL     | medium | 35  | 13  | 6 | 19 | 19 |
| Water    | medium | 43  | 21  | 0 | 21 | 20 |
| VGO      | large  | 68  | 30  | 1 | 31 | 22 |
| SWO      | large  | 88  | 35  | 0 | 35 | 26 |

All sources of the selected ontologies:

- **wine**: [Wine Ontology](https://github.com/UCDavisLibrary/wine-ontology)
- **awo**: [African Wildlife Ontology](https://people.cs.uct.ac.za/~mkeet/OEbook/ontologies/AfricanWildlifeOntology1.owl)
- **odrl**: [ODRL Vocabulary Ontology](https://www.w3.org/ns/odrl/2/)
- **water**: [SAREF4WATR Ontology](https://saref.etsi.org/saref4watr/v1.1.1/)
- **vgo**: [Video Game Ontology](https://vocab.linkeddata.es/vgo/)
- **swo**: [Software Ontology](https://obofoundry.org/ontology/swo.html)


## File formats

### Annotation Records:

**`<Domain>_CQs_Annotations.xlsx`**: annotation process with per-CQ class and property splits, plus axioms.

### CQ2Onto Task:

**`cq_to_onto_<domain>.json`** (CQ2Onto Input): list of CQs. Gold standard is the ontology, corresponding to `.owl` file.

```json
[
  {"id": "CQ1", "value": "Which wine characteristics should I consider when choosing a wine?"}
]
```

**`sub_<domain>.owl`** (CQ2Onto Gold Standard): OWL source code in RDF/XML. CQ-driven restriction of the source ontology, retaining only what's required to satisfy the CQs.

### CQ2Term Task:

**`cq_to_terms_<domain>.json`** (CQ2Term Input & Gold Standard): list of CQs, with the gold standard class and property labels.

```json
[
  {
    "id": "CQ1",
    "question": "Which wine characteristics should I consider when choosing a wine?", # Input Competency Question
    "classes": ["Wine", "WineDescriptor"], # Gold Standard Classes
    "properties": ["hasWineDescriptor"] # Gold Standard Properties
  }
]
```


## Loading

```python
from huggingface_hub import hf_hub_download
import json, rdflib

# CQ2Term: Load Dataset
path = hf_hub_download("oeg/CQ2Onto", "wine/cq_to_terms_wine.json", repo_type="dataset")
cqs = json.load(open(path))

# CQ2Onto
path = hf_hub_download("oeg/CQ2Onto", "wine/sub_wine.owl", repo_type="dataset")
g = rdflib.Graph().parse(path)
```
## License

Apache 2.0.