wiki-entity-summarization - 1.0.5

We have generated datasets using A Brief History of Human Time project. These datasets contain different sets of seed nodes, categorized by various human arts and professions.

| dataset (variant, size, None/train/val/test) | #roots | #smmaries | #nodes | #edges | #labels | roots category distribution | Running Time(sec) | |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|--------|--------|---------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------| | WikiLitArt-s
csv, graphml, croissant.json | 494 | 10416 | 85346 | 136950 | 547 | actor=150
composer=35
film=41
novelist=24
painter=59
poet=39
screenwriter=17
singer=72
writer=57 | 91.934 | | WikiLitArt-s-train
csv, graphml, croissant.json | 346 | 7234 | 61885 | 96497 | 508 | actor=105
composer=24
film=29
novelist=17
painter=42
poet=27
screenwriter=12
singer=50
writer=40 | 66.023 | | WikiLitArt-s-val
csv, graphml, croissant.json | 74 | 1572 | 14763 | 20795 | 340 | actor=23
composer=5
film=6
novelist=4
painter=9
poet=6
screenwriter=2
singer=11
writer=8 | 14.364 | | WikiLitArt-s-test
csv, graphml, croissant.json | 74 | 1626 | 15861 | 22029 | 350 | actor=22
composer=6
film=6
novelist=3
painter=8
poet=6
screenwriter=3
singer=11
writer=9 | 14.6 | | WikiLitArt-m
csv, graphml, croissant.json | 494 | 10416 | 128061 | 220263 | 604 | actor=150
composer=35
film=41
novelist=24
painter=59
poet=39
screenwriter=17
singer=72
writer=57 | 155.368 | | WikiLitArt-m-train
csv, graphml, croissant.json | 346 | 7234 | 93251 | 155667 | 566 | actor=105
composer=24
film=29
novelist=17
painter=42
poet=27
screenwriter=12
singer=50
writer=40 | 111.636 | | WikiLitArt-m-val
csv, graphml, croissant.json | 74 | 1572 | 22214 | 33547 | 375 | actor=23
composer=5
film=6
novelist=4
painter=9
poet=6
screenwriter=2
singer=11
writer=8 | 22.957 | | WikiLitArt-m-test
csv, graphml, croissant.json | 74 | 1626 | 24130 | 35980 | 394 | actor=22
composer=6
film=6
novelist=3
painter=8
poet=6
screenwriter=3
singer=11
writer=9 | 26.187 | | WikiLitArt-l
csv, graphml, croissant.json | 494 | 10416 | 239491 | 466905 | 703 | actor=150
composer=35
film=41
novelist=24
painter=59
poet=39
screenwriter=17
singer=72
writer=57 | 353.113 | | WikiLitArt-l-train
csv, graphml, croissant.json | 346 | 7234 | 176057 | 332279 | 661 | actor=105
composer=24
film=29
novelist=17
painter=42
poet=27
screenwriter=12
singer=50
writer=40 | 244.544 | | WikiLitArt-l-val
csv, graphml, croissant.json | 74 | 1572 | 42745 | 71734 | 446 | actor=23
composer=5
film=6
novelist=4
painter=9
poet=6
screenwriter=2
singer=11
writer=8 | 57.263 | | WikiLitArt-l-test
csv, graphml, croissant.json | 74 | 1626 | 46890 | 77931 | 493 | actor=22
composer=6
film=6
novelist=3
painter=8
poet=6
screenwriter=3
singer=11
writer=9 | 60.466 | | WikiCinema-s
csv, graphml, croissant.json | 493 | 11750 | 70753 | 126915 | 469 | actor=405
film=88 | 118.014 | | WikiCinema-s-train
csv, graphml, croissant.json | 345 | 8374 | 52712 | 89306 | 437 | actor=284
film=61 | 84.364 | | WikiCinema-s-val
csv, graphml, croissant.json | 73 | 1650 | 13362 | 19280 | 305 | actor=59
film=14 | 18.651 | | WikiCinema-s-test
csv, graphml, croissant.json | 75 | 1744 | 14777 | 21567 | 313 | actor=62
film=13 | 19.851 | | WikiCinema-m
csv, graphml, croissant.json | 493 | 11750 | 101529 | 196061 | 541 | actor=405
film=88 | 196.413 | | WikiCinema-m-train
csv, graphml, croissant.json | 345 | 8374 | 75900 | 138897 | 491 | actor=284
film=61 | 142.091 | | WikiCinema-m-val
csv, graphml, croissant.json | 73 | 1650 | 19674 | 30152 | 344 | actor=59
film=14 | 31.722 | | WikiCinema-m-test
csv, graphml, croissant.json | 75 | 1744 | 22102 | 34499 | 342 | actor=62
film=13 | 33.674 | | WikiCinema-l
csv, graphml, croissant.json | 493 | 11750 | 185098 | 397546 | 614 | actor=405
film=88 | 475.679 | | WikiCinema-l-train
csv, graphml, croissant.json | 345 | 8374 | 139598 | 284417 | 575 | actor=284
film=61 | 333.148 | | WikiCinema-l-val
csv, graphml, croissant.json | 73 | 1650 | 37352 | 63744 | 412 | actor=59
film=14 | 68.62 | | WikiCinema-l-test
csv, graphml, croissant.json | 75 | 1744 | 43238 | 74205 | 426 | actor=62
film=13 | 87.07 | | WikiPro-s
csv, graphml, croissant.json | 493 | 9853 | 79825 | 125912 | 616 | actor=58
football=156
journalist=14
lawyer=16
painter=23
player=25
politician=125
singer=27
sport=21
writer=28 | 126.119 | | WikiPro-s-train
csv, graphml, croissant.json | 345 | 6832 | 57529 | 87768 | 575 | actor=41
football=109
journalist=10
lawyer=11
painter=16
player=17
politician=87
singer=19
sport=15
writer=20 | 89.874 | | WikiPro-s-val
csv, graphml, croissant.json | 74 | 1548 | 15769 | 21351 | 405 | actor=9
football=23
journalist=2
lawyer=3
painter=3
player=4
politician=19
singer=4
sport=3
writer=4 | 21.021 | | WikiPro-s-test
csv, graphml, croissant.json | 74 | 1484 | 15657 | 21145 | 384 | actor=8
football=24
journalist=2
lawyer=2
painter=4
player=4
politician=19
singer=4
sport=3
writer=4 | 21.743 | | WikiPro-m
csv, graphml, croissant.json | 493 | 9853 | 119305 | 198663 | 670 | actor=58
football=156
journalist=14
lawyer=16
painter=23
player=25
politician=125
singer=27
sport=21
writer=28 | 208.157 | | WikiPro-m-train
csv, graphml, croissant.json | 345 | 6832 | 86434 | 138676 | 633 | actor=41
football=109
journalist=10
lawyer=11
painter=16
player=17
politician=87
singer=19
sport=15
writer=20 | 141.563 | | WikiPro-m-val
csv, graphml, croissant.json | 74 | 1548 | 24230 | 34636 | 463 | actor=9
football=23
journalist=2
lawyer=3
painter=3
player=4
politician=19
singer=4
sport=3
writer=4 | 36.045 | | WikiPro-m-test
csv, graphml, croissant.json | 74 | 1484 | 24117 | 34157 | 462 | actor=8
football=24
journalist=2
lawyer=2
painter=4
player=4
politician=19
singer=4
sport=3
writer=4 | 36.967 | | WikiPro-l
csv, graphml, croissant.json | 493 | 9853 | 230442 | 412766 | 769 | actor=58
football=156
journalist=14
lawyer=16
painter=23
player=25
politician=125
singer=27
sport=21
writer=28 | 489.409 | | WikiPro-l-train
csv, graphml, croissant.json | 345 | 6832 | 166685 | 290069 | 725 | actor=41
football=109
journalist=10
lawyer=11
painter=16
player=17
politician=87
singer=19
sport=15
writer=20 | 334.864 | | WikiPro-l-val
csv, graphml, croissant.json | 74 | 1548 | 48205 | 74387 | 549 | actor=9
football=23
journalist=2
lawyer=3
painter=3
player=4
politician=19
singer=4
sport=3
writer=4 | 84.089 | | WikiPro-l-test
csv, graphml, croissant.json | 74 | 1484 | 47981 | 72845 | 546 | actor=8
football=24
journalist=2
lawyer=2
painter=4
player=4
politician=19
singer=4
sport=3
writer=4 | 92.545 | | WikiProFem-s
csv, graphml, croissant.json | 468 | 8338 | 79926 | 123193 | 571 | actor=141
athletic=25
football=24
journalist=16
painter=16
player=32
politician=81
singer=69
sport=18
writer=46 | 177.63 | | WikiProFem-s-train
csv, graphml, croissant.json | 330 | 5587 | 58329 | 87492 | 521 | actor=98
athletic=18
football=17
journalist=9
painter=13
player=22
politician=57
singer=48
sport=14
writer=34 | 127.614 | | WikiProFem-s-val
csv, graphml, croissant.json | 68 | 1367 | 14148 | 19360 | 344 | actor=21
athletic=4
football=3
journalist=4
painter=1
player=5
politician=13
singer=11
sport=1
writer=5 | 29.081 | | WikiProFem-test
csv, graphml, croissant.json | 70 | 1387 | 13642 | 18567 | 360 | actor=22
athletic=3
football=4
journalist=3
painter=2
player=5
politician=11
singer=10
sport=3
writer=7 | 27.466 | | WikiProFem-m
csv, graphml, croissant.json | 468 | 8338 | 122728 | 196838 | 631 | actor=141
athletic=25
football=24
journalist=16
painter=16
player=32
politician=81
singer=69
sport=18
writer=46 | 301.718 | | WikiProFem-m-train
csv, graphml, croissant.json | 330 | 5587 | 89922 | 140505 | 600 | actor=98
athletic=18
football=17
journalist=9
painter=13
player=22
politician=57
singer=48
sport=14
writer=34 | 217.699 | | WikiProFem-m-val
csv, graphml, croissant.json | 68 | 1367 | 21978 | 31230 | 409 | actor=21
athletic=4
football=3
journalist=4
painter=1
player=5
politician=13
singer=11
sport=1
writer=5 | 46.793 | | WikiProFem-m-test
csv, graphml, croissant.json | 70 | 1387 | 21305 | 29919 | 394 | actor=22
athletic=3
football=4
journalist=3
painter=2
player=5
politician=11
singer=10
sport=3
writer=7 | 46.317 | | WikiProFem-l
csv, graphml, croissant.json | 468 | 8338 | 248012 | 413895 | 722 | actor=141
athletic=25
football=24
journalist=16
painter=16
player=32
politician=81
singer=69
sport=18
writer=46 | 768.99 | | WikiProFem-l-train
csv, graphml, croissant.json | 330 | 5587 | 183710 | 297686 | 676 | actor=98
athletic=18
football=17
journalist=9
painter=13
player=22
politician=57
singer=48
sport=14
writer=34 | 544.893 | | WikiProFem-l-val
csv, graphml, croissant.json | 68 | 1367 | 46018 | 67193 | 492 | actor=21
athletic=4
football=3
journalist=4
painter=1
player=5
politician=13
singer=11
sport=1
writer=5 | 116.758 | | WikiProFem-l-test
csv, graphml, croissant.json | 70 | 1387 | 44193 | 63563 | 472 | actor=22
athletic=3
football=4
journalist=3
painter=2
player=5
politician=11
singer=10
sport=3
writer=7 | 118.524 |

Dataset Parameters

| Parameter | Value | |-------------------------------|-------| | Min valid summary edges | 5 | | Random walk depth length | 3 | | Min random walk number-small | 100 | | Min random walk number-medium | 150 | | Min random walk number-large | 300 | | Max random walk number-small | 300 | | Max random walk number-medium | 600 | | Max random walk number-large | 1800 | | Bridges number | 5 |

Graph Structure

In the following you can see a sample of the graph format (we highly recommend using our toolkit to load the datasets):

CSV Format

After unzipping {variant}-{size}-{dataset_type}.zip file, you will find the following CSV files:

{variant}-{size}-{dataset_type}-entities.csv contains entities. An entity is a Wikidata item (node) in our dataset.

| Field | Description | datatype | |-----------------|--------------------------------------|----------| | id | incremental integer starting by zero | int | | entity | Wikidata qid, e.g. Q76 | string | | wikidatalabel | Wikidata label (nullable) | string | | wikidatadesc | Wikidata description (nullable) | string | | wikipediatitle | Wikipedia title (nullable) | string | | wikipediaid | Wikipedia page id (nullable) | long |

{variant}-{size}-{dataset_type}-root-entities.csv contains root entities. A root entity is a seed node described previously.

| Field | Description | datatype | |----------|----------------------------------------------------------|----------| | entity | id key in {variant}-{size}-{dataset_type}-entities.csv | int | | category | category | string |

{variant}-{size}-{dataset_type}-predicates.csv contains predicates. A predicate is a Wikidata property or a describing a connection.

| Field | Description | datatype | |-----------------|------------------------------------------|----------| | id | incremental integer starting by zero | int | | predicate | Wikidata Property id, e.g. P121 | string | | predicatelabel | Wikidata Property label (nullable) | string | | predicatedesc | Wikidata Property description (nullable) | string |

{variant}-{size}-{dataset_type}-triples.csv contains triples. A triple is an edge between two entities with a predicate.

| Field | Description | datatype | |-----------|------------------------------------------------------------|----------| | subject | id key in {variant}-{size}-{dataset_type}-entities.csv | int | | predicate | id key in {variant}-{size}-{dataset_type}-predicates.csv | int | | object | id key in {variant}-{size}-{dataset_type}-entities.csv | int |

{viariant}_{size}_{dataset_type}-ground-truths.csv contains ground truth triples. A ground truth triple is an edge that is marked as a summary for a root entity.

| Field | Description | datatype | |-------------|---------------------------------------------------------------|----------| | rootentity | entity in `{variant}-{size}-{datasettype}-root-entities.csv| int | | subject | id key in{variant}-{size}-{datasettype}-entities.csv| int | | predicate | id key in{variant}-{size}-{datasettype}-predicates.csv| int | | object | id key in{variant}-{size}-{dataset_type}-entities.csv` | int |

Note: for this file one of the columns subject or object is equal to the root_entity.

Example of CSV Files

```csv

entities.csv

id,entity,wikidatalabel,wikidatadesc,wikipediatitle,wikipediaid 0,Q43416,Keanu Reeves,Canadian actor (born 1964),KeanuReeves,16603 1,Q3820,Beirut,capital and largest city of Lebanon,Beirut,37428 2,Q639669,musician,person who composes, conducts or performs music,Musician,38284 3,Q219150,Constantine,2005 film directed by Francis Lawrence,Constantine(film),1210303 ```

```csv

root-entities.csv

entity,category 0,Q43416,actor ```

```csv

predicates.csv

id,predicate,predicatelabel,predicatedesc 0,P19,place of birth,location where the subject was born 1,P106,occupation,occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39) 2,P161,cast member,actor in the subject production [use "character role" (P453) and/or "name of the character role" (P4633) as qualifiers] [use "voice actor" (P725) for voice-only role] ```

```csv

triples.csv

subject,predicate,object 0,0,1 0,1,2 3,2,0 ```

```csv

ground-truth.csv

root_entity,subject,predicate,object 0,0,0,1 3,3,2,0 ```

GraphML Example

The same graph can be represented in GraphML format.

xml <?xml version="1.0" encoding="UTF-8"?> <graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd"> <key id="d9" for="edge" attr.name="summary_for" attr.type="string"/> <key id="d8" for="edge" attr.name="predicate_desc" attr.type="string"/> <key id="d7" for="edge" attr.name="predicate_label" attr.type="string"/> <key id="d6" for="edge" attr.name="predicate" attr.type="string"/> <key id="d5" for="node" attr.name="category" attr.type="string"/> <key id="d4" for="node" attr.name="is_root" attr.type="boolean"/> <key id="d3" for="node" attr.name="wikidata_desc" attr.type="string"/> <key id="d2" for="node" attr.name="wikipedia_title" attr.type="string"/> <key id="d1" for="node" attr.name="wikipedia_id" attr.type="long"/> <key id="d0" for="node" attr.name="wikidata_label" attr.type="string"/> <graph edgedefault="directed"> <node id="Q43416"> <data key="d0">Keanu Reeves</data> <data key="d1">16603</data> <data key="d2">Keanu_Reeves</data> <data key="d3">Canadian actor (born 1964)</data> <data key="d4">True</data> <data key="d5">actor</data> </node> <node id="Q3820"> <data key="d0">Beirut</data> <data key="d1">37428</data> <data key="d2">Beirut</data> <data key="d3">capital and largest city of Lebanon</data> </node> <node id="Q639669"> <data key="d0">musician</data> <data key="d1">38284</data> <data key="d2">Musician</data> <data key="d3">person who composes, conducts or performs music</data> </node> <node id="Q219150"> <data key="d0">Constantine</data> <data key="d1">1210303</data> <data key="d2">Constantine_(film)</data> <data key="d3">2005 film directed by Francis Lawrence</data> </node> <edge source="Q43416" target="Q3820" id="P19"> <data key="d6">P19</data> <data key="d7">place of birth</data> <data key="d8">location where the subject was born</data> <data key="d9">Q43416</data> </edge> <edge source="Q43416" target="Q639669" id="P106"> <data key="d6">P106</data> <data key="d7">occupation</data> <data key="d8">occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39) </data> </edge> <edge source="Q219150" target="Q43416" id="P106"> <data key="d6">P161</data> <data key="d7">cast member</data> <data key="d8">actor in the subject production [use "character role" (P453) and/or "name of the character role" (P4633) as qualifiers] [use "voice actor" (P725) for voice-only role] </data> <data key="d9">Q43416</data> </edge> </graph> </graphml>

- Python
Published by msorkhpar over 1 year ago

wiki-entity-summarization - 1.0.4

We have generated datasets using A Brief History of Human Time project. These datasets contain different sets of seed nodes, categorized by various human arts and professions.

| dataset (variant, size, None/train/val/test) | #roots | #smmaries | #nodes | #edges | #labels | roots category distribution | Running Time(sec) | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------|--------|--------|---------|--------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------| | WikiLitArt-s-train csv, graphml, croissant.json | 346 | 7234 | 61885 | 96497 | 508 | actor=105
composer=24
film=29
novelist=17
painter=42
poet=27
screenwriter=12
singer=50
writer=40 | 66.023 | | WikiLitArt-s-val csv, graphml, croissant.json | 74 | 1572 | 14763 | 20795 | 340 | actor=23
composer=5
film=6
novelist=4
painter=9
poet=6
screenwriter=2
singer=11
writer=8 | 14.364 | | WikiLitArt-s-test csv, graphml, croissant.json | 74 | 1626 | 15861 | 22029 | 350 | actor=22
composer=6
film=6
novelist=3
painter=8
poet=6
screenwriter=3
singer=11
writer=9 | 14.6 | | WikiLitArt-m csv, graphml, croissant.json | 494 | 10416 | 128061 | 220263 | 604 | actor=150
composer=35
film=41
novelist=24
painter=59
poet=39
screenwriter=17
singer=72
writer=57 | 155.368 | | WikiLitArt-m-train csv, graphml, croissant.json | 346 | 7234 | 93251 | 155667 | 566 | actor=105
composer=24
film=29
novelist=17
painter=42
poet=27
screenwriter=12
singer=50
writer=40 | 111.636 | | WikiLitArt-m-val csv, graphml, croissant.json | 74 | 1572 | 22214 | 33547 | 375 | actor=23
composer=5
film=6
novelist=4
painter=9
poet=6
screenwriter=2
singer=11
writer=8 | 22.957 | | WikiLitArt-m-test csv, graphml, croissant.json | 74 | 1626 | 24130 | 35980 | 394 | actor=22
composer=6
film=6
novelist=3
painter=8
poet=6
screenwriter=3
singer=11
writer=9 | 26.187 | | WikiLitArt-l csv, graphml, croissant.json | 494 | 10416 | 239491 | 466905 | 703 | actor=150
composer=35
film=41
novelist=24
painter=59
poet=39
screenwriter=17
singer=72
writer=57 | 353.113 | | WikiLitArt-l-train csv, graphml, croissant.json | 346 | 7234 | 176057 | 332279 | 661 | actor=105
composer=24
film=29
novelist=17
painter=42
poet=27
screenwriter=12
singer=50
writer=40 | 244.544 | | WikiLitArt-l-val csv, graphml, croissant.json | 74 | 1572 | 42745 | 71734 | 446 | actor=23
composer=5
film=6
novelist=4
painter=9
poet=6
screenwriter=2
singer=11
writer=8 | 57.263 | | WikiLitArt-l-test csv, graphml, croissant.json | 74 | 1626 | 46890 | 77931 | 493 | actor=22
composer=6
film=6
novelist=3
painter=8
poet=6
screenwriter=3
singer=11
writer=9 | 60.466 | | WikiCinema-s csv, graphml, croissant.json | 493 | 11750 | 70753 | 126915 | 469 | actor=405
film=88 | 118.014 | | WikiCinema-s-train csv, graphml, croissant.json | 345 | 8374 | 52712 | 89306 | 437 | actor=284
film=61 | 84.364 | | WikiCinema-s-val csv, graphml, croissant.json | 73 | 1650 | 13362 | 19280 | 305 | actor=59
film=14 | 18.651 | | WikiCinema-s-test csv, graphml, croissant.json | 75 | 1744 | 14777 | 21567 | 313 | actor=62
film=13 | 19.851 | | WikiCinema-m csv, graphml, croissant.json | 493 | 11750 | 101529 | 196061 | 541 | actor=405
film=88 | 196.413 | | WikiCinema-me-train csv, graphml, croissant.json | 345 | 8374 | 75900 | 138897 | 491 | actor=284
film=61 | 142.091 | | WikiCinema-m-val csv, graphml, croissant.json | 73 | 1650 | 19674 | 30152 | 344 | actor=59
film=14 | 31.722 | | WikiCinema-m-test csv, graphml, croissant.json | 75 | 1744 | 22102 | 34499 | 342 | actor=62
film=13 | 33.674 | | WikiCinema-l csv, graphml, croissant.json | 493 | 11750 | 185098 | 397546 | 614 | actor=405
film=88 | 475.679 | | WikiCinema-l-train csv, graphml, croissant.json | 345 | 8374 | 139598 | 284417 | 575 | actor=284
film=61 | 333.148 | | WikiCinema-l-val csv, graphml, croissant.json | 73 | 1650 | 37352 | 63744 | 412 | actor=59
film=14 | 68.62 | | WikiCinema-l-test csv, graphml, croissant.json | 75 | 1744 | 43238 | 74205 | 426 | actor=62
film=13 | 87.07 | | WikiPro-s csv, graphml, croissant.json | 493 | 9853 | 79825 | 125912 | 616 | actor=58
football=156
journalist=14
lawyer=16
painter=23
player=25
politician=125
singer=27
sport=21
writer=28 | 126.119 | | WikiPro-s-train csv, graphml, croissant.json | 345 | 6832 | 57529 | 87768 | 575 | actor=41
football=109
journalist=10
lawyer=11
painter=16
player=17
politician=87
singer=19
sport=15
writer=20 | 89.874 | | WikiPro-s-val csv, graphml, croissant.json | 74 | 1548 | 15769 | 21351 | 405 | actor=9
football=23
journalist=2
lawyer=3
painter=3
player=4
politician=19
singer=4
sport=3
writer=4 | 21.021 | | WikiPro-s-test csv, graphml, croissant.json | 74 | 1484 | 15657 | 21145 | 384 | actor=8
football=24
journalist=2
lawyer=2
painter=4
player=4
politician=19
singer=4
sport=3
writer=4 | 21.743 | | WikiPro-m csv, graphml, croissant.json | 493 | 9853 | 119305 | 198663 | 670 | actor=58
football=156
journalist=14
lawyer=16
painter=23
player=25
politician=125
singer=27
sport=21
writer=28 | 208.157 | | WikiPro-m-train csv, graphml, croissant.json | 345 | 6832 | 86434 | 138676 | 633 | actor=41
football=109
journalist=10
lawyer=11
painter=16
player=17
politician=87
singer=19
sport=15
writer=20 | 141.563 | | WikiPro-m-val csv, graphml, croissant.json | 74 | 1548 | 24230 | 34636 | 463 | actor=9
football=23
journalist=2
lawyer=3
painter=3
player=4
politician=19
singer=4
sport=3
writer=4 | 36.045 | | WikiPro-m-test csv, graphml, croissant.json | 74 | 1484 | 24117 | 34157 | 462 | actor=8
football=24
journalist=2
lawyer=2
painter=4
player=4
politician=19
singer=4
sport=3
writer=4 | 36.967 | | WikiPro-l csv, graphml, croissant.json | 493 | 9853 | 230442 | 412766 | 769 | actor=58
football=156
journalist=14
lawyer=16
painter=23
player=25
politician=125
singer=27
sport=21
writer=28 | 489.409 | | WikiPro-l-train csv, graphml, croissant.json | 345 | 6832 | 166685 | 290069 | 725 | actor=41
football=109
journalist=10
lawyer=11
painter=16
player=17
politician=87
singer=19
sport=15
writer=20 | 334.864 | | WikiPro-l-val csv, graphml, croissant.json | 74 | 1548 | 48205 | 74387 | 549 | actor=9
football=23
journalist=2
lawyer=3
painter=3
player=4
politician=19
singer=4
sport=3
writer=4 | 84.089 | | WikiPro-l-test csv, graphml, croissant.json | 74 | 1484 | 47981 | 72845 | 546 | actor=8
football=24
journalist=2
lawyer=2
painter=4
player=4
politician=19
singer=4
sport=3
writer=4 | 92.545 | | WikiProFem-s csv, graphml, croissant.json | 468 | 8338 | 79926 | 123193 | 571 | actor=141
athletic=25
football=24
journalist=16
painter=16
player=32
politician=81
singer=69
sport=18
writer=46 | 177.63 | | WikiProFem-s-train csv, graphml, croissant.json | 330 | 5587 | 58329 | 87492 | 521 | actor=98
athletic=18
football=17
journalist=9
painter=13
player=22
politician=57
singer=48
sport=14
writer=34 | 127.614 | | WikiProFem-s-val csv, graphml, croissant.json | 68 | 1367 | 14148 | 19360 | 344 | actor=21
athletic=4
football=3
journalist=4
painter=1
player=5
politician=13
singer=11
sport=1
writer=5 | 29.081 | | WikiProFem-test csv, graphml, croissant.json | 70 | 1387 | 13642 | 18567 | 360 | actor=22
athletic=3
football=4
journalist=3
painter=2
player=5
politician=11
singer=10
sport=3
writer=7 | 27.466 | | WikiProFem-m csv, graphml, croissant.json | 468 | 8338 | 122728 | 196838 | 631 | actor=141
athletic=25
football=24
journalist=16
painter=16
player=32
politician=81
singer=69
sport=18
writer=46 | 301.718 | | WikiProFem-m-train csv, graphml, croissant.json | 330 | 5587 | 89922 | 140505 | 600 | actor=98
athletic=18
football=17
journalist=9
painter=13
player=22
politician=57
singer=48
sport=14
writer=34 | 217.699 | | WikiProFem-m-val csv, graphml, croissant.json | 68 | 1367 | 21978 | 31230 | 409 | actor=21
athletic=4
football=3
journalist=4
painter=1
player=5
politician=13
singer=11
sport=1
writer=5 | 46.793 | | WikiProFem-m-test csv, graphml, croissant.json | 70 | 1387 | 21305 | 29919 | 394 | actor=22
athletic=3
football=4
journalist=3
painter=2
player=5
politician=11
singer=10
sport=3
writer=7 | 46.317 | | WikiProFem-l csv, graphml, croissant.json | 468 | 8338 | 248012 | 413895 | 722 | actor=141
athletic=25
football=24
journalist=16
painter=16
player=32
politician=81
singer=69
sport=18
writer=46 | 768.99 | | WikiProFem-l-train csv, graphml, croissant.json | 330 | 5587 | 183710 | 297686 | 676 | actor=98
athletic=18
football=17
journalist=9
painter=13
player=22
politician=57
singer=48
sport=14
writer=34 | 544.893 | | WikiProFem-l-val csv, graphml, croissant.json | 68 | 1367 | 46018 | 67193 | 492 | actor=21
athletic=4
football=3
journalist=4
painter=1
player=5
politician=13
singer=11
sport=1
writer=5 | 116.758 | | WikiProFem-l-test csv, graphml, croissant.json | 70 | 1387 | 44193 | 63563 | 472 | actor=22
athletic=3
football=4
journalist=3
painter=2
player=5
politician=11
singer=10
sport=3
writer=7 | 118.524 |

Dataset Parameters

| Parameter | Value | |-------------------------------|-------| | Min valid summary edges | 5 | | Random walk depth length | 3 | | Min random walk number-small | 100 | | Min random walk number-medium | 150 | | Min random walk number-large | 300 | | Max random walk number-small | 300 | | Max random walk number-medium | 600 | | Max random walk number-large | 1800 | | Bridges number | 5 |

Graph Structure

In the following, you can see a sample of the graph format (we highly recommend using our toolkit to load the datasets):

CSV Format

After unzipping {variant_index}_{size}_{dataset_type}.zip file, you will find the following CSV files:

{variant_index}_{size}_{dataset_type}__entities.csv contains entities. An entity is a Wikidata item (node) in our dataset.

| Field | Description | datatype | |-----------------|--------------------------------------|----------| | id | incremental integer starting by zero | int | | entity | Wikidata qid, e.g. Q76 | string | | wikidatalabel | Wikidata label (nullable) | string | | wikidatadesc | Wikidata description (nullable) | string | | wikipediatitle | Wikipedia title (nullable) | string | | wikipediaid | Wikipedia page id (nullable) | long |

{variant_index}_{size}_{dataset_type}__root_entities.csv contains root entities. A root entity is a seed node described previously.

| Field | Description | datatype | |----------|-----------------------------------------------------------------|----------| | entity | id key in {variant_index}_{size}_{dataset_type}__entities.csv | int | | category | category | string |

{variant_index}_{size}_{dataset_type}__predicates.csv contains predicates. A predicate is a Wikidata property or a describing a connection.

| Field | Description | datatype | |-----------------|------------------------------------------|----------| | id | incremental integer starting by zero | int | | predicate | Wikidata Property id, e.g. P121 | string | | predicatelabel | Wikidata Property label (nullable) | string | | predicatedesc | Wikidata Property description (nullable) | string |

{variant_index}_{size}_{dataset_type}__triples.csv contains triples. A triple is an edge between two entities with a predicate.

| Field | Description | datatype | |-----------|-------------------------------------------------------------------|----------| | subject | id key in {variant_index}_{size}_{dataset_type}__entities.csv | int | | predicate | id key in {variant_index}_{size}_{dataset_type}__predicates.csv | int | | object | id key in {variant_index}_{size}_{dataset_type}__entities.csv | int |

{variant_index}_{size}_{dataset_type}__ground_truths.csv contains ground truth triples. A ground truth triple is an edge that is marked as a summary for a root entity.

| Field | Description | datatype | |-------------|----------------------------------------------------------------------|----------| | rootentity | entity in `{variantindex}{size}{datasettype}rootentities.csv| int | | subject | id key in{variantindex}{size}{datasettype}entities.csv| int | | predicate | id key in{variantindex}{size}{datasettype}predicates.csv| int | | object | id key in{variantindex}{size}{datasettype}__entities.csv` | int |

Note: for this file one of the columns subject or object is equal to the root_entity.

Example of CSV Files

```csv

entities.csv

id,entity,wikidatalabel,wikidatadesc,wikipediatitle,wikipediaid 0,Q43416,Keanu Reeves,Canadian actor (born 1964),KeanuReeves,16603 1,Q3820,Beirut,capital and largest city of Lebanon,Beirut,37428 2,Q639669,musician,person who composes, conducts or performs music,Musician,38284 3,Q219150,Constantine,2005 film directed by Francis Lawrence,Constantine(film),1210303 ```

```csv

root_entities.csv

entity,category 0,Q43416,actor ```

```csv

predicates.csv

id,predicate,predicatelabel,predicatedesc 0,P19,place of birth,location where the subject was born 1,P106,occupation,occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39) 2,P161,cast member,actor in the subject production [use "character role" (P453) and/or "name of the character role" (P4633) as qualifiers] [use "voice actor" (P725) for voice-only role] ```

```csv

triples.csv

subject,predicate,object 0,0,1 0,1,2 3,2,0 ```

```csv

ground_truth.csv

root_entity,subject,predicate,object 0,0,0,1 3,3,2,0 ```

GraphML Example

The same graph can be represented in GraphML format.

xml <?xml version="1.0" encoding="UTF-8"?> <graphml xmlns="http://graphml.graphdrawing.org/xmlns" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://graphml.graphdrawing.org/xmlns http://graphml.graphdrawing.org/xmlns/1.0/graphml.xsd"> <key id="d9" for="edge" attr.name="summary_for" attr.type="string"/> <key id="d8" for="edge" attr.name="predicate_desc" attr.type="string"/> <key id="d7" for="edge" attr.name="predicate_label" attr.type="string"/> <key id="d6" for="edge" attr.name="predicate" attr.type="string"/> <key id="d5" for="node" attr.name="category" attr.type="string"/> <key id="d4" for="node" attr.name="is_root" attr.type="boolean"/> <key id="d3" for="node" attr.name="wikidata_desc" attr.type="string"/> <key id="d2" for="node" attr.name="wikipedia_title" attr.type="string"/> <key id="d1" for="node" attr.name="wikipedia_id" attr.type="long"/> <key id="d0" for="node" attr.name="wikidata_label" attr.type="string"/> <graph edgedefault="directed"> <node id="Q43416"> <data key="d0">Keanu Reeves</data> <data key="d1">16603</data> <data key="d2">Keanu_Reeves</data> <data key="d3">Canadian actor (born 1964)</data> <data key="d4">True</data> <data key="d5">actor</data> </node> <node id="Q3820"> <data key="d0">Beirut</data> <data key="d1">37428</data> <data key="d2">Beirut</data> <data key="d3">capital and largest city of Lebanon</data> </node> <node id="Q639669"> <data key="d0">musician</data> <data key="d1">38284</data> <data key="d2">Musician</data> <data key="d3">person who composes, conducts or performs music</data> </node> <node id="Q219150"> <data key="d0">Constantine</data> <data key="d1">1210303</data> <data key="d2">Constantine_(film)</data> <data key="d3">2005 film directed by Francis Lawrence</data> </node> <edge source="Q43416" target="Q3820" id="P19"> <data key="d6">P19</data> <data key="d7">place of birth</data> <data key="d8">location where the subject was born</data> <data key="d9">Q43416</data> </edge> <edge source="Q43416" target="Q639669" id="P106"> <data key="d6">P106</data> <data key="d7">occupation</data> <data key="d8">occupation of a person; see also "field of work" (Property:P101), "position held" (Property:P39) </data> </edge> <edge source="Q219150" target="Q43416" id="P106"> <data key="d6">P161</data> <data key="d7">cast member</data> <data key="d8">actor in the subject production [use "character role" (P453) and/or "name of the character role" (P4633) as qualifiers] [use "voice actor" (P725) for voice-only role] </data> <data key="d9">Q43416</data> </edge> </graph> </graphml>

- Python
Published by msorkhpar over 1 year ago

wiki-entity-summarization - 1.0.3

We're excited to release the first version of our dataset, featuring:

Four Seed Node Sets: Each is available in small, medium, and large sizes and covers human arts, cinema, and famous influencers in human history. Structured Splits: Each set is divided into train, validation, and test graphs. 48 graphs in total

- Python
Published by msorkhpar over 1 year ago

wiki-entity-summarization - v0.1-alpha

First RC version

- Python
Published by msorkhpar almost 2 years ago