wiki-entity-summarization-toolkit

A user-friendly toolkit for the Wiki-Entity-Summarization (WikES) and ESBM datasets.

Parent project

This toolkit is part of the Wiki-Entity-Summarization project (WikES).

Installation

pip install wikes-toolkit

Usage

WikES usage (object based)

Using this toolkit with Object-Oriented Programming (OOP) is straightforward.

Here is an example of how to use it:

from wikes_toolkit import WikESToolkit, WikESVersions, WikESGraph

toolkit = WikESToolkit(save_path="./data")  # save_path is optional
G = toolkit.load_graph(
    WikESGraph,
    WikESVersions.V1.WikiCinema.SMALL,
    entity_formatter=lambda e: f"Entity({e.wikidata_label})",
    predicate_formatter=lambda p: f"Predicate({p.label})",
    triple_formatter=lambda
        t: f"({t.subject_entity.wikidata_label})-[{t.predicate.label}]-> ({t.object_entity.wikidata_label})"
)

root_nodes = G.root_entities()
nodes = G.entities()
nx_graph = G._G
edges = G.triples()
labels = G.predicates()
number_of_nodes = G.total_entities()
number_of_directed_edges = G.total_triples()
node = G.fetch_entity('Q303')
node_degree = G.degree('Q303')
neighbors = G.ground_truths(node)
ground_truth_summaries = G.ground_truths(root_nodes[0])
G.mark_triple_as_summary(edges[0].subject_entity, edges[0])

for root in G.root_entities():
    print(f"Neighbors of [{root}]:")
    for triple in G.neighbors(root):
        print(triple)

    for _ in range(5):
        print("*" * 40)

    print("Ground truth summaries:")
    for summary in G.ground_truths(root):
        print(summary)
    G.mark_triples_as_summaries(root, G.neighbors(root))
    break

f1 =  G.f1_score()
f1_5 =  G.f1_score(5)
f1_10 = G.f1_score(10)
map = G.map_score()
map_5 = G.map_score(5)
map_10 = G.map_score(10)
""" Output of the above code:
Neighbors of [Entity(Elvis Presley)]:
(Elvis Presley)-[military unit]-> (32nd Cavalry Regiment)
(Elvis Presley)-[genre]-> (blues)
...
(Jim Morrison)-[influenced by]-> (Elvis Presley)
(Elvis Country – I'm 10,000 Years Old)-[performer]-> (Elvis Presley)
(The King)-[main subject]-> (Elvis Presley)
****************************************
Ground truth summaries:
(Elvis Presley)-[genre]-> (rockabilly)
(Million Dollar Quartet)-[has part(s)]-> (Elvis Presley)
(Jailhouse Rock)-[cast member]-> (Elvis Presley)
...
(Viva Las Vegas)-[cast member]-> (Elvis Presley)
(Elvis Presley)-[genre]-> (rhythm and blues)
(Elvis Presley)-[record label]-> (Sun Records)
(Elvis Presley)-[genre]-> (pop music)
"""

To load all graphs, you can use the load_all_graphs method:

from wikes_toolkit import WikESToolkit, WikESVersions, WikESGraph

for dataset_name, G in WikESToolkit(save_path="./data").load_all_graphs(WikESGraph, WikESVersions.V1):
    print(f"Dataset [{dataset_name}:")
    print(G.total_entities())

Pandas usage

There is another version of this toolkit that uses Pandas DataFrame to store the graph data. To use this version, you can change the first parameter of the load_graph method to PandasWikESGraph:

from wikes_toolkit import WikESToolkit, PandasWikESGraph, WikESVersions

toolkit = WikESToolkit()
G = toolkit.load_graph(PandasWikESGraph, WikESVersions.V1.WikiLitArt.SMALL, entity_formatter=lambda e: e.identifier)

root_nodes = G.root_entities()
first_root_node = G.root_entity_ids()[0]
nodes = G.entities()
edges = G.triples()
first_edge = G.fetch_triple(edges.iloc[0])
labels = G.predicates()
number_of_nodes = G.total_entities()
number_of_directed_edges = G.total_triples()
node = G.fetch_entity('Q303')
node_degree = G.degree('Q303')
ground_truths = G.ground_truths(node)
neighbors = G.neighbors(node)
ground_truth_summaries = G.ground_truths(first_root_node)
G.mark_triple_as_summary(ground_truths.iloc[0].name,
                         (ground_truths.iloc[0]['subject'], ground_truths.iloc[0]['predicate'], ground_truths.iloc[0]['object']))
G.mark_triples_as_summaries(node, neighbors.iloc[0:2])
f1 =  G.f1_score()
f1_5 =  G.f1_score(5)
f1_10 = G.f1_score(10)
map = G.map_score()
map = G.map_score(no_rel=True)
map_5 = G.map_score(5)
map_10 = G.map_score(10)
G.clear_summaries()

Export WikESGraphs as CSV files

You can export the graphs as CSV files using the export_as_csv method. This method exports the graph as three CSV files:

from wikes_toolkit import WikESToolkit, WikESVersions, PandasWikESGraph

toolkit = WikESToolkit()

for dataset, G in toolkit.load_all_graphs(PandasWikESGraph, WikESVersions.V1):
    G.export_as_csv("./data")

Note: This method has been implemented for WikESPandasGraph, others yet to be implemented.

ESBM

We also cover ESBM datasets with almost the same functionalities and syntax as before. If you want to check how we have converted ESBM datasets to NetworkX format, or if you want to use GraphML formats of this dataset, check ESBM-to-nx-format repository.

Here is an example of loading ESBM and ESM_Plus datasets with this toolkit:

from wikes_toolkit import WikESToolkit, ESBMGraph, ESBMVersions

toolkit = WikESToolkit()
G = toolkit.load_graph(
    ESBMGraph,
    ESBMVersions.Plus.DBPEDIA_FULL,  # ESBMVersions.Plus.DBPEDIA_FULL or ESBMVersions.V1Dot2.DBPEDIA_TEST_0 or ESBMVersions.V1Dot2.LMDB_TRAIN_1
    entity_formatter=lambda e: e.identifier
)

root_nodes = G.root_entities()
first_root_node = G.root_entity_ids()[0]
nodes = G.entities()
edges = G.triples()
labels = G.predicates()
number_of_nodes = G.total_entities()
number_of_directed_edges = G.total_triples()
node = G.fetch_entity('http://dbpedia.org/resource/Adrian_Griffin')
node_degree = G.degree('http://dbpedia.org/resource/Adrian_Griffin')
gold_top5_0 = G.gold_top_5(node, 0)
gold_top10_0 = G.gold_top_10(node, 0)
neighbors = G.neighbors(node)
# or  G.neighbors('http://dbpedia.org/resource/Adrian_Griffin')


G.mark_triples_as_summaries(
    "http://dbpedia.org/resource/3WAY_FM",
    [
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://purl.org/dc/terms/subject',
            'http://dbpedia.org/resource/Category:Radio_stations_in_Victoria'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://xmlns.com/foaf/0.1/homepage',
            'http://3wayfm.org.au'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://dbpedia.org/ontology/broadcastArea',
            'http://dbpedia.org/resource/Victoria_(Australia)'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://dbpedia.org/ontology/callsignMeaning',
            '3 - Victoria'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://xmlns.com/foaf/0.1/name',
            '3WAY FM@en'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://www.w3.org/2000/01/rdf-schema#label',
            '3WAY FM@en'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://dbpedia.org/ontology/callsignMeaning',
            'Warrnambool And You'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://dbpedia.org/ontology/broadcastArea',
            'http://dbpedia.org/resource/Warrnambool'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://www.w3.org/1999/02/22-rdf-syntax-ns#type',
            'http://schema.org/RadioStation'
        ),
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://purl.org/dc/terms/subject',
            'http://dbpedia.org/resource/Category:Radio_stations_established_in_1990'
        )]
)

G.clear_summaries()

G.mark_triples_as_summaries(
    "http://dbpedia.org/resource/3WAY_FM",
    [
        (
            'http://dbpedia.org/resource/3WAY_FM',
            'http://purl.org/dc/terms/subject',
            'http://dbpedia.org/resource/Category:Radio_stations_in_Victoria'
        )]
)

G.mark_triples_as_summaries(
    "http://dbpedia.org/resource/Adrian_Griffin",
    [
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://dbpedia.org/ontology/activeYearsEndYear',
            '2008'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://purl.org/dc/terms/subject',
            'http://dbpedia.org/resource/Category:Orlando_Magic_assistant_coaches'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://dbpedia.org/ontology/birthDate',
            '1974-07-04'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://dbpedia.org/ontology/height',
            '1.9558'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://dbpedia.org/ontology/weight',
            '98431.2'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://dbpedia.org/ontology/team',
            'http://dbpedia.org/resource/Orlando_Magic'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://purl.org/dc/terms/subject',
            'http://dbpedia.org/resource/Category:Basketball_players_from_Kansas'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://www.w3.org/1999/02/22-rdf-syntax-ns#type',
            'http://dbpedia.org/class/yago/BasketballPlayersFromKansas'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://www.w3.org/1999/02/22-rdf-syntax-ns#type',
            'http://dbpedia.org/class/yago/BasketballCoach109841955'
        ),
        (
            'http://dbpedia.org/resource/Adrian_Griffin',
            'http://www.w3.org/1999/02/22-rdf-syntax-ns#type',
            'http://dbpedia.org/class/yago/BasketballPlayer109842047'
        ),
    ]
)

f1_5 =  G.f1_score(5)
f1_10 = G.f1_score(10)
map_5 = G.map_score(5)
map_10 = G.map_score(10)

ESBM Pandas usage

For Pandas version of ESBM datasets, you can use PandasESBMGraph instead of ESBMGraph:

from wikes_toolkit import WikESToolkit, PandasESBMGraph, ESBMVersions

toolkit = WikESToolkit()
G = toolkit.load_graph(PandasESBMGraph, ESBMVersions.V1Dot2.DBPEDIA_FULL, entity_formatter=lambda e: e.identifier)

first_root_node_id = G.root_entity_ids()[0]
root_nodes = G.root_entities()
first_root_node = G.root_entities().iloc[0]
nodes = G.entities()
edges = G.triples()
labels = G.predicates()
number_of_nodes = G.total_entities()
number_of_directed_edges = G.total_triples()
node = G.fetch_entity('http://dbpedia.org/resource/Adrian_Griffin')
node_degree = G.degree('http://dbpedia.org/resource/Adrian_Griffin')
gold_top5_0 = G.gold_top_5(node, 0)
gold_top10_0 = G.gold_top_10(node, 0)
neighbors = G.neighbors(node)
all_golds = G.all_gold_top_k(5)
golds = G.gold_top_5(G.root_entities().iloc[0], 4)

Citation

If you use this project in your research, please cite the following paper:

@misc{javadi2024wiki,
    title = {Wiki Entity Summarization Benchmark},
    author = {Saeedeh Javadi and Atefeh Moradan and Mohammad Sorkhpar and Klim Zaporojets and Davide Mottin and Ira Assent},
    year = {2024},
    eprint = {2406.08435},
    archivePrefix = {arXiv},
    primaryClass = {cs.IR}
}

In case you use ESBM datasets, please cite their paper as well ours:

@inproceedings{esbm,
  author    = {Qingxia Liu and
                Gong Cheng and
                Kalpa Gunaratna and
                Yuzhong Qu},
  title     = {ESBM: An Entity Summarization Benchmark},
  booktitle = {ESWC},
  year      = {2020}
}