def test1_thres1(self):
input_df = pd.read_csv(
"test/data/schema_matching_helper/common_prefixes.csv")
output_expected = ["p1", "p2", "p3", "pA", "pB"]
output = get_common_prefixes(input_df,
threshold=1,
column_name="label")
assert output == output_expected
def test4_thres100(self):
input_df = pd.read_csv(
"test/data/schema_matching_helper/common_prefixes.csv")
output_expected = []
output = get_common_prefixes(input_df,
threshold=100,
column_name="label")
assert output == output_expected
def label_schema_matching(df,
endpoint=DBpedia,
uri_data_model=False,
to_lowercase=True,
remove_prefixes=True,
remove_punctuation=True,
prefix_threshold=1,
progress=True,
caching=True):
"""A schema matching method by checking for attribute -- rdfs:label between
links.
Args:
df (pd.DataFrame): The dataframe where matching attributes are supposed
to be found.
endpoint (Endpoint, optional): SPARQL Endpoint to be queried. Defaults
to DBpedia.
uri_data_model (bool, optional): If enabled, the URI is directly
queried instead of a SPARQL endpoint. Defaults to False.
to_lowercase (bool, optional): Converts queried strings to lowercase.
Defaults to True.
remove_prefixes (bool, optional): Removes prefices of queried strings.
Defaults to True.
remove_punctuation (bool, optional): Removes punctuation from queried
strings. Defaults to True.
prefix_threshold (int, optional): The number of occurences after which
a prefix is considered "common". Defaults to 1.
progress (bool, optional): If True, progress bars will be shown to
inform the user about the progress made by the process (if
"uri_data_model" = True). Defaults to True.
caching (bool, optional): Turn result-caching for queries issued during
the execution on or off. Defaults to True.
Returns:
pd.DataFrame: Two columns with matching links and a third column with the overlapped label.
"""
matches = pd.DataFrame(columns=["uri_1", "uri_2", "same_label"])
# Get URIs from the column names
cat_cols = [col for col in df.columns if re.findall("https*:", col)]
cat_cols_stripped = [
re.sub(r"^.*http://", "http://", col) for col in cat_cols
]
# transform attributes to sparql values list form
values = "(<" + pd.Series(cat_cols_stripped).str.cat(sep=">) (<") + ">) "
if uri_data_model:
# Query these URIs for the label
query = "SELECT ?value ?o WHERE {VALUES (?value) {(<**URI**>)} ?value rdfs:label ?o. FILTER (lang(?o) = 'en') }"
labels = uri_querier(
pd.DataFrame(cat_cols_stripped),
0,
query,
progress=progress,
caching=caching).drop_duplicates().set_index("value")
else:
query = "SELECT ?value ?o WHERE {VALUES (?value) {" + values + \
"} ?value rdfs:label ?o. FILTER (lang(?o) = 'en') }"
# query the equivalent classes/properties
labels = endpoint_wrapper(query, endpoint,
caching=caching).reset_index(drop=True)
if labels.empty:
return matches
# Get common prefixes
common_prefixes = get_common_prefixes(labels, prefix_threshold)
# Clean the results (i.e. the labels)
labels["o"] = labels["o"].apply(lambda x: clean_string(
x, common_prefixes, to_lowercase, remove_prefixes, remove_punctuation))
# Create a dictionary
if labels.index.name == "value":
labels.reset_index(inplace=True)
labels_dict = labels.set_index("value").T.to_dict("list")
#check if there are no matches
tmp = set()
for v in labels_dict.values():
tmp.update(v)
if len(labels_dict) == len(tmp):
combinations = list(itertools.combinations(cat_cols_stripped, 2))
combinations_sorted = [sorted(x) for x in combinations]
matches = pd.DataFrame(combinations_sorted, columns=["uri_1", "uri_2"])
matches["same_label"] = 0
return matches
else:
# Combine the uris that have the same labels into a DataFrame
new_labels_dict = collections.defaultdict(list)
for key, values in labels_dict.items():
for i in values:
new_labels_dict[i].append(key)
df_labels = pd.DataFrame(list(new_labels_dict.values()),
columns=["uri_1", "uri_2"])
#df_labels["same_label"] = pd.DataFrame(list(new_labels_dict.keys()))
df_labels.dropna(inplace=True)
# restrict the order of uris in one row
for _, row in df_labels.iterrows():
new_match = {
"uri_1": min(row["uri_1"], row["uri_2"]),
"uri_2": max(row["uri_1"], row["uri_2"]),
"same_label": 1
}
matches = matches.append(new_match, ignore_index=True)
# Get back the uris that are not quired by rdfs:label and turn df into dict
no_label = pd.DataFrame({
"value":
[x for x in cat_cols_stripped if x not in list(labels["value"])],
"o":
np.nan
})
labels = labels.append(no_label, ignore_index=True)
full_labels_dict = labels.set_index("value").T.to_dict("list")
# Create all unique combinations from the URIs, order them alphabetically and turn them into a DataFrame
combinations = list(itertools.combinations(full_labels_dict.keys(), 2))
combinations_sorted = [sorted(x) for x in combinations]
result = pd.DataFrame(combinations_sorted, columns=["uri_1", "uri_2"])
# merged with the non_matched combinations and drop duplicates
for _, row in result.iterrows():
new_match = {
"uri_1": min(row["uri_1"], row["uri_2"]),
"uri_2": max(row["uri_1"], row["uri_2"]),
"same_label": 0
}
matches = matches.append(new_match, ignore_index=True)
matches.drop_duplicates(subset=["uri_1", "uri_2"],
inplace=True,
ignore_index=True)
return matches
def string_similarity_matching(df,
predicate="rdfs:label",
to_lowercase=True,
remove_prefixes=True,
remove_punctuation=True,
similarity_metric="norm_levenshtein",
prefix_threshold=1,
n=2,
progress=True,
caching=True):
"""Calculates the string similarity from the text field obtained by
querying the attributes for the predicate, by default rdfs:label.
Args:
df (pd.DataFrame): Dataframe where matching attributes are supposed to
be found
predicate (str, optional): Defaults to "rdfs:label".
to_lowercase (bool, optional): converts queried strings to lowercase.
Defaults to True.
remove_prefixes (bool, optional): removes prefices of queried strings.
Defaults to True.
remove_punctuation (bool, optional): removes punctuation from queried
strings. Defaults to True.
similarity_metric (str, optional): norm by which strings are compared.
Defaults to "norm_levenshtein".
prefix_threshold (int, optional): The number of occurences after which
a prefix is considered "common". defaults to 1. n (int, optional):
parameter for n-gram and Jaccard similarities. Defaults to 2.
progress (bool, optional): If True, progress bars will be shown to
inform the user about the progress made by the process. Defaults to
True.
caching (bool, optional): Turn result-caching for queries issued during
the execution on or off. Defaults to True.
Returns:
pd.DataFrame: Two columns with matching links and a third column with
the string similarity score.
"""
# Get URIs from the column names
cat_cols = [col for col in df.columns if re.findall("https*:", col)]
cat_cols_stripped = [
re.sub(r"^.*http://", "http://", col) for col in cat_cols
]
# Query these URIs for the predicate (usually the label)
query = "SELECT ?value ?o WHERE {VALUES (?value) {(<**URI**>)} ?value "
query += predicate + " ?o. FILTER (lang(?o) = 'en') }"
labels = uri_querier(pd.DataFrame(cat_cols_stripped),
0,
query,
progress=progress,
caching=caching).set_index("value")
# Get common prefixes
common_prefixes = get_common_prefixes(labels, prefix_threshold)
# Clean the results (i.e. the labels)
labels["o"] = labels["o"].apply(lambda x: clean_string(
x, common_prefixes, to_lowercase, remove_prefixes, remove_punctuation))
# Create a dictionary that maps the URIs to their result (i.e. label)
labels.reset_index(inplace=True)
no_label = pd.DataFrame({
"value":
[x for x in cat_cols_stripped if x not in list(labels["value"])],
"o":
np.nan
})
labels = labels.append(no_label, ignore_index=True)
labels_dict = labels.set_index("value").T.to_dict("list")
#labels_dict = labels.to_dict(orient="index")
# Create all unique combinations from the URIs, order them alphabetically
# and turn them into a DataFrame
combinations = list(itertools.combinations(labels_dict.keys(), 2))
combinations_sorted = [sorted(x) for x in combinations]
result = pd.DataFrame(combinations_sorted, columns=["uri_1", "uri_2"])
# For each combination in this DataFrame, calculate the string similarity
# of their results (i.e. labels)
if progress:
tqdm.pandas(
desc="String Similarity Matching: Calculate String Similarities")
result["value_string"] = result.progress_apply(
lambda x: calc_string_similarity(x["uri_1"],
x["uri_2"],
labels_dict,
metric=similarity_metric,
n=n),
axis=1)
else:
result["value_string"] = result.apply(lambda x: calc_string_similarity(
x["uri_1"], x["uri_2"
], labels_dict, metric=similarity_metric, n=n),
axis=1)
return result