personal-knowledge-library 2.1.5

Description:

personalknowledgelibrary 2.1.5

Wacom Private Knowledge Library
The required tenant API key is only available for selected partner companies.
Please contact your Wacom representative for more information.
Introduction
In knowledge management there is a distinction between data, information and knowledge.
In the domain of digital ink this means:

Data - The equivalent would be the ink strokes
Information - After using handwriting-, shape-, math-, or other recognition processes ink strokes are converted into machine readable content, such as text, shapes, math representations, other other digital content
Knowledge / Semantics - Beyond recognition content needs to be semantically analysed to become semantically understood based on a shared common knowledge.

The following illustration shows the different layers of knowledge:

For handling semantics, Wacom introduced the Wacom Private Knowledge (WPK) cloud service to manage personal ontologies and its associated personal knowledge graph.
This library provide simplified access to Wacom's personal knowledge cloud service.
It contains:

Basic datastructures for Ontology object and entities from the knowledge graph
Clients for the REST APIs
Connector for Wikidata public knowledge graph

Ontology service:

List all Ontology structures
Modify Ontology structures
Delete Ontology structures

Entity service:

List all entities
Add entities to knowledge graph
Access object properties

Search service:

Search for entities for labels and descriptions with a given language
Search for literals (data properties)
Search for relations (object properties)

Group service:

List all groups
Add groups, modify groups, delete groups
Add users and entities to groups

Ontology service:

List all Ontology structures
Modify Ontology structures

Named Entity Linking service:

Linking words to knowledge entities from graph in a given text (Ontology-based Named Entity Linking)

Wikidata connector:

Import entities from Wikidata
Mapping Wikidata entities to WPK entities

Technology stack
Domain Knowledge
The tasks of the ontology within Wacom's private knowledge system is to formalised the domain the technology is used in, such as education-, smart home-, or creative domein.
The domain model will be the foundation for the entities collected within the knowledge graph, describing real world concepts in a formal language understood by artificial intelligence system:

Foundation for structured data, knowledge representation as concepts and relations among concepts
Being explicit definitions of shared vocabularies for interoperability
Being actionable fragments of explicit knowledge that engines can use for inferencing (Reasoning)
Can be used for problem solving

An ontology defines (specifies) the concepts, relationships, and other distinctions that are relevant for modelling a domain.
Knowledge Graph

Knowledge graph is generated from unstructured and structured knowledge sources
Contains all structured knowledge gathered from all sources
Foundation for all semantic algorithms

Semantic Technology

Extract knowledge from various sources (Connectors)
Linking words to knowledge entities from graph in a given text (Ontology-based Named Entity Linking)
Enables a smart search functionality which understands the context and finds related documents (Semantic Search)

Functionality
Import Format
For importing entities into the knowledge graph, the tools/import_entities.py script can be used.
The ThingObject support a NDJSON based import format, where the individual JSON files can contain the following structure.



Field name
Subfield name
Data Structure
Description




source_reference_id

str
A unique identifier for the entity used in the source system


source_system

str
The source system describes the original source of the entity, such as wikidata, youtube, ...


image

str
A string representing the URL of the entity's icon.


labels

array
An array of label objects, where each object has the following fields:



value
str
A string representing the label text in the specified locale.



locale
str
A string combining the ISO-3166 country code and the ISO-639 language code (e.g., "en-US").



isMain
bool
A boolean flag indicating if this label is the main label for the entity (true) or an alias (false).


descriptions

array
An array of description objects, where each object has the following fields:



description
str
A string representing the description text in the specified locale.



locale
str
A string combining the ISO-3166 country code and the ISO-639 language code (e.g., "en-US").


type

str
A string representing the IRI of the ontology class for this entity.


literals

array[map]
An array of data property objects, where each object has the following fields:



Access API
The personal knowledge graph backend is implement as a multi-tenancy system.
Thus, several tenants can be logically separated from each other and different organisations can build their one knowledge graph.

In general, a tenant with their users, groups, and entities are logically separated.
Physically the entities are store in the same instance of the Wacom Private Knowledge (WPK) backend database system.
The user management is rather limited, each organisation must provide their own authentication service and user management.
The backend only has a reference of the user (“shadow user”) by an external user id.
The management of tenants is limited to the system owner - Wacom -, as it requires a tenant management API key.
While users for each tenant can be created by the owner of the Tenant API Key.
You will receive this token from the system owner after the creation of the tenant.

:warning: Store the Tenant API Key in a secure key store, as attackers can use the key to harm your system.

The Tenant API Key should be only used by your authentication service to create shadow users and to login your user into the WPK backend.
After a successful user login, you will receive a token which can be used by the user to create, update, or delete entities and relations.
The following illustration summarizes the flows for creation of tenant and users:

The organisation itself needs to implement their own authentication service which:

handles the users and their passwords,
controls the personal data of the users,
connects the users with the WPK backend and share with them the user token.

The WPK backend only manages the access levels of the entities and the group management for users.
The illustration shows how the access token is received from the WPK endpoint:

Entity API
The entities used within the knowledge graph and the relationship among them is defined within an ontology that is manage with Wacom Ontology Management System (WOMS).
An entity within the personal knowledge graphs consist of these major parts:

Icon - a visual representation of the entity, for instance a portrait of a person.
URI - a unique resource identifier of an entity in the graph.
Type - the type links to the defined concept class in the ontology.
Labels - labels are the word(s) use in a language for the concept.
Description - a short abstract that describes the entity.
Literals - literals are properties of an entity, such as first name of a person. The ontology defines all literals of the concept class as well as its data type.
Relations - the relationship among different entities is described using relations.

The following illustration provides an example for an entity:

Entity content
Entities in general are language-independent as across nationalities or cultures we only use different scripts and words for a shared instance of a concept.
Let's take Leonardo da Vinci as an example.
The ontology defines the concept of a Person, a human being.
Now, in English its label would be Leonardo da Vinci, while in Japanese レオナルド・ダ・ヴィンチ.
Moreover, he is also known as Leonardo di ser Piero da Vinci or ダ・ビンチ.
Labels
Now, in the given example all words that a assigned to the concept are labels.
The label Leonardo da Vinci is stored in the backend with an additional language code, e.g. en.
There is always a main label, which refers to the most common or official name of entity.
Another example would be Wacom, where Wacom Co., Ltd. is the official name while Wacom is commonly used and be considered as an alias.

:pushpin: For the language code the ISO 639-1:2002, codes for the representation of names of languages—Part 1: Alpha-2 code. Read more, here

Samples
Entity handling
This samples shows how to work with graph service.
import argparse
from typing import Optional, Dict, List

from knowledge.base.entity import Description, Label
from knowledge.base.language import LocaleCode, EN_US, DE_DE
from knowledge.base.ontology import OntologyClassReference, OntologyPropertyReference, ThingObject, ObjectProperty
from knowledge.services.graph import WacomKnowledgeService

# ------------------------------- Knowledge entities -------------------------------------------------------------------
LEONARDO_DA_VINCI: str = 'Leonardo da Vinci'
SELF_PORTRAIT_STYLE: str = 'self-portrait'
ICON: str = "https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Mona_Lisa_%28copy%2C_Thalwil%2C_Switzerland%29."\
"JPG/1024px-Mona_Lisa_%28copy%2C_Thalwil%2C_Switzerland%29.JPG"
# ------------------------------- Ontology class names -----------------------------------------------------------------
THING_OBJECT: OntologyClassReference = OntologyClassReference('wacom', 'core', 'Thing')
"""
The Ontology will contain a Thing class where is the root class in the hierarchy.
"""
ARTWORK_CLASS: OntologyClassReference = OntologyClassReference('wacom', 'creative', 'VisualArtwork')
PERSON_CLASS: OntologyClassReference = OntologyClassReference('wacom', 'core', 'Person')
ART_STYLE_CLASS: OntologyClassReference = OntologyClassReference.parse('wacom:creative#ArtStyle')
IS_CREATOR: OntologyPropertyReference = OntologyPropertyReference('wacom', 'core', 'created')
HAS_TOPIC: OntologyPropertyReference = OntologyPropertyReference.parse('wacom:core#hasTopic')
CREATED: OntologyPropertyReference = OntologyPropertyReference.parse('wacom:core#created')
HAS_ART_STYLE: OntologyPropertyReference = OntologyPropertyReference.parse('wacom:creative#hasArtstyle')


def print_entity(display_entity: ThingObject, list_idx: int, client: WacomKnowledgeService,
short: bool = False):
"""
Printing entity details.

Parameters
----------
display_entity: ThingObject
Entity with properties
list_idx: int
Index with a list
client: WacomKnowledgeService
Knowledge graph client
short: bool
Short summary
"""
print(f'[{list_idx}] : {display_entity.uri} <{display_entity.concept_type.iri}>')
if len(display_entity.label) > 0:
print(' | [Labels]')
for la in display_entity.label:
print(f' | |- "{la.content}"@{la.language_code}')
print(' |')
if not short:
if len(display_entity.alias) > 0:
print(' | [Alias]')
for la in display_entity.alias:
print(f' | |- "{la.content}"@{la.language_code}')
print(' |')
if len(display_entity.data_properties) > 0:
print(' | [Attributes]')
for data_property, labels in display_entity.data_properties.items():
print(f' | |- {data_property.iri}:')
for li in labels:
print(f' | |-- "{li.value}"@{li.language_code}')
print(' |')

relations_obj: Dict[OntologyPropertyReference, ObjectProperty] = client.relations(uri=display_entity.uri)
if len(relations_obj) > 0:
print(' | [Relations]')
for r_idx, re in enumerate(relations_obj.values()):
last: bool = r_idx == len(relations_obj) - 1
print(f' |--- {re.relation.iri}: ')
print(f' {"|" if not last else " "} |- [Incoming]: {re.incoming_relations} ')
print(f' {"|" if not last else " "} |- [Outgoing]: {re.outgoing_relations}')
print()


if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--user", help="External Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-t", "--tenant", help="Tenant Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-i", "--instance", default='https://private-knowledge.wacom.com',
help="URL of instance")
args = parser.parse_args()
TENANT_KEY: str = args.tenant
EXTERNAL_USER_ID: str = args.user
# Wacom personal knowledge REST API Client
knowledge_client: WacomKnowledgeService = WacomKnowledgeService(application_name="Wacom Knowledge Listing",
service_url=args.instance)
knowledge_client.login(args.tenant, args.user)
page_id: Optional[str] = None
page_number: int = 1
entity_count: int = 0
print('-----------------------------------------------------------------------------------------------------------')
print(' First step: Find Leonardo da Vinci in the knowledge graph.')
print('-----------------------------------------------------------------------------------------------------------')
res_entities, next_search_page = knowledge_client.search_labels(search_term=LEONARDO_DA_VINCI,
language_code=LocaleCode('en_US'), limit=1000)
leo: Optional[ThingObject] = None
s_idx: int = 1
for res_entity in res_entities:
# Entity must be a person and the label match with full string
if res_entity.concept_type == PERSON_CLASS and LEONARDO_DA_VINCI in [la.content for la in res_entity.label]:
leo = res_entity
break

print('-----------------------------------------------------------------------------------------------------------')
print(' What artwork exists in the knowledge graph.')
print('-----------------------------------------------------------------------------------------------------------')
relations_dict: Dict[OntologyPropertyReference, ObjectProperty] = knowledge_client.relations(uri=leo.uri)
print(f' Artwork of {leo.label}')
print('-----------------------------------------------------------------------------------------------------------')
idx: int = 1
if CREATED in relations_dict:
for e in relations_dict[CREATED].outgoing_relations:
print(f' [{idx}] {e.uri}: {e.label}')
idx += 1
print('-----------------------------------------------------------------------------------------------------------')
print(' Let us create a new piece of artwork.')
print('-----------------------------------------------------------------------------------------------------------')

# Main labels for entity
artwork_labels: List[Label] = [
Label('Ginevra Gherardini', EN_US),
Label('Ginevra Gherardini', DE_DE)
]
# Alias labels for entity
artwork_alias: List[Label] = [
Label("Ginevra", EN_US),
Label("Ginevra", DE_DE)
]
# Topic description
artwork_description: List[Description] = [
Description('Oil painting of Mona Lisa\' sister', EN_US),
Description('Ölgemälde von Mona Lisa\' Schwester', DE_DE)
]
# Topic
artwork_object: ThingObject = ThingObject(label=artwork_labels, concept_type=ARTWORK_CLASS,
description=artwork_description,
icon=ICON)
artwork_object.alias = artwork_alias
print(f' Create: {artwork_object}')
# Create artwork
artwork_entity_uri: str = knowledge_client.create_entity(artwork_object)
print(f' Entity URI: {artwork_entity_uri}')
# Create relation between Leonardo da Vinci and artwork
knowledge_client.create_relation(source=leo.uri, relation=IS_CREATOR, target=artwork_entity_uri)

relations_dict = knowledge_client.relations(uri=artwork_entity_uri)
for ontology_property, object_property in relations_dict.items():
print(f' {object_property}')
# You will see that wacom:core#isCreatedBy is automatically inferred as relation as it is the inverse property of
# wacom:core#created.

# Now, more search options
res_entities, next_search_page = knowledge_client.search_description('Michelangelo\'s Sistine Chapel',
EN_US, limit=1000)
print('-----------------------------------------------------------------------------------------------------------')
print(' Search results. Description: "Michelangelo\'s Sistine Chapel"')
print('-----------------------------------------------------------------------------------------------------------')
s_idx: int = 1
for e in res_entities:
print_entity(e, s_idx, knowledge_client)

# Now, let's search all artwork that has the art style self-portrait
res_entities, next_search_page = knowledge_client.search_labels(search_term=SELF_PORTRAIT_STYLE,
language_code=EN_US, limit=1000)
art_style: Optional[ThingObject] = None
s_idx: int = 1
for entity in res_entities:
# Entity must be a person and the label match with full string
if entity.concept_type == ART_STYLE_CLASS and SELF_PORTRAIT_STYLE in [la.content for la in entity.label]:
art_style = entity
break
res_entities, next_search_page = knowledge_client.search_relation(subject_uri=None,
relation=HAS_ART_STYLE,
object_uri=art_style.uri,
language_code=EN_US)
print('-----------------------------------------------------------------------------------------------------------')
print(' Search results. Relation: relation:=has_topic object_uri:= unknown')
print('-----------------------------------------------------------------------------------------------------------')
s_idx: int = 1
for e in res_entities:
print_entity(e, s_idx, knowledge_client, short=True)
s_idx += 1

# Finally, the activation function retrieving the related identities to a pre-defined depth.
entities, relations = knowledge_client.activations(uris=[leo.uri], depth=1)
print('-----------------------------------------------------------------------------------------------------------')
print(f'Activation. URI: {leo.uri}')
print('-----------------------------------------------------------------------------------------------------------')
s_idx: int = 1
for e in res_entities:
print_entity(e, s_idx, knowledge_client)
s_idx += 1
# All relations
print('-----------------------------------------------------------------------------------------------------------')
for r in relations:
print(f'Subject: {r[0]} Predicate: {r[1]} Object: {r[2]}')
print('-----------------------------------------------------------------------------------------------------------')
page_id = None

# Listing all entities which have the type
idx: int = 1
while True:
# pull
entities, total_number, next_page_id = knowledge_client.listing(ART_STYLE_CLASS, page_id=page_id, limit=100)
pulled_entities: int = len(entities)
entity_count += pulled_entities
print('-------------------------------------------------------------------------------------------------------')
print(f' Page: {page_number} Number of entities: {len(entities)} ({entity_count}/{total_number}) '
f'Next page id: {next_page_id}')
print('-------------------------------------------------------------------------------------------------------')
for e in entities:
print_entity(e, idx, knowledge_client)
idx += 1
if pulled_entities == 0:
break
page_number += 1
page_id = next_page_id
print()
# Delete all personal entities for this user
while True:
# pull
entities, total_number, next_page_id = knowledge_client.listing(THING_OBJECT, page_id=page_id,
limit=100)
pulled_entities: int = len(entities)
if pulled_entities == 0:
break
delete_uris: List[str] = [e.uri for e in entities]
print(f'Cleanup. Delete entities: {delete_uris}')
knowledge_client.delete_entities(uris=delete_uris, force=True)
page_number += 1
page_id = next_page_id
print('-----------------------------------------------------------------------------------------------------------')

Named Entity Linking
Performing Named Entity Linking (NEL) on text and Universal Ink Model.
import argparse
from typing import List, Dict

import urllib3

from knowledge.base.language import EN_US
from knowledge.base.ontology import OntologyPropertyReference, ThingObject, ObjectProperty
from knowledge.nel.base import KnowledgeGraphEntity
from knowledge.nel.engine import WacomEntityLinkingEngine
from knowledge.services.graph import WacomKnowledgeService

urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)


TEXT: str = "Leonardo da Vinci painted the Mona Lisa."


def print_entity(entity: KnowledgeGraphEntity, list_idx: int, auth_key: str, client: WacomKnowledgeService):
"""
Printing entity details.

Parameters
----------
entity: KnowledgeGraphEntity
Named entity
list_idx: int
Index with a list
auth_key: str
Authorization key
client: WacomKnowledgeService
Knowledge graph client
"""
thing: ThingObject = knowledge_client.entity(auth_key=user_token, uri=entity.entity_source.uri)
print(f'[{list_idx}] - {entity.ref_text} [{entity.start_idx}-{entity.end_idx}] : {thing.uri}'
f' <{thing.concept_type.iri}>')
if len(thing.label) > 0:
print(' | [Labels]')
for la in thing.label:
print(f' | |- "{la.content}"@{la.language_code}')
print(' |')
if len(thing.label) > 0:
print(' | [Alias]')
for la in thing.alias:
print(f' | |- "{la.content}"@{la.language_code}')
print(' |')
relations: Dict[OntologyPropertyReference, ObjectProperty] = client.relations(auth_key=auth_key, uri=thing.uri)
if len(thing.data_properties) > 0:
print(' | [Attributes]')
for data_property, labels in thing.data_properties.items():
print(f' | |- {data_property.iri}:')
for li in labels:
print(f' | |-- "{li.value}"@{li.language_code}')
print(' |')
if len(relations) > 0:
print(' | [Relations]')
for re in relations.values():
print(f' |--- {re.relation.iri}: ')
print(f' |- [Incoming]: {re.incoming_relations} ')
print(f' |- [Outgoing]: {re.outgoing_relations}')
print()


if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--user", help="External Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-t", "--tenant", help="Tenant Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-i", "--instance", default="https://private-knowledge.wacom.com", help="URL of instance")
args = parser.parse_args()
TENANT_KEY: str = args.tenant
EXTERNAL_USER_ID: str = args.user
# Wacom personal knowledge REST API Client
knowledge_client: WacomKnowledgeService = WacomKnowledgeService(
application_name="Named Entity Linking Knowledge access",
service_url=args.instance)
# Wacom Named Entity Linking
nel_client: WacomEntityLinkingEngine = WacomEntityLinkingEngine(
service_url=args.instance,
service_endpoint=WacomEntityLinkingEngine.SERVICE_ENDPOINT
)
# Use special tenant for testing: Unit-test tenant
user_token, refresh_token, expiration_time = nel_client.request_user_token(TENANT_KEY, EXTERNAL_USER_ID)
entities: List[KnowledgeGraphEntity] = nel_client.\
link_personal_entities(text=TEXT, language_code=EN_US, auth_key=user_token)
idx: int = 1
print('-----------------------------------------------------------------------------------------------------------')
print(f'Text: "{TEXT}"@{EN_US}')
print('-----------------------------------------------------------------------------------------------------------')
for e in entities:
print_entity(e, idx, user_token, knowledge_client)
idx += 1

Access Management
The sample shows, how access to entities can be shared with a group of users or the tenant.
import argparse
from typing import List

from knowledge.base.entity import Label, Description
from knowledge.base.language import EN_US, DE_DE, JA_JP
from knowledge.base.ontology import OntologyClassReference, ThingObject
from knowledge.services.base import WacomServiceException
from knowledge.services.graph import WacomKnowledgeService
from knowledge.services.group import GroupManagementService, Group
from knowledge.services.users import UserManagementServiceAPI

# ------------------------------- User credential ----------------------------------------------------------------------
TOPIC_CLASS: OntologyClassReference = OntologyClassReference('wacom', 'core', 'Topic')


def create_entity() -> ThingObject:
"""Create a new entity.

Returns
-------
entity: ThingObject
Entity object
"""
# Main labels for entity
topic_labels: List[Label] = [
Label('Hidden', EN_US),
Label('Versteckt', DE_DE),
Label('隠れた', JA_JP),
]

# Topic description
topic_description: List[Description] = [
Description('Hidden entity to explain access management.', EN_US),
Description('Verstecke Entität, um die Zugriffsteuerung zu erlären.', DE_DE)
]
# Topic
topic_object: ThingObject = ThingObject(label=topic_labels, concept_type=TOPIC_CLASS, description=topic_description)
return topic_object


if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--user", help="External Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-t", "--tenant", help="Tenant Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-i", "--instance", default='https://private-knowledge.wacom.com',
help="URL of instance")
args = parser.parse_args()
TENANT_KEY: str = args.tenant
EXTERNAL_USER_ID: str = args.user
# Wacom personal knowledge REST API Client
knowledge_client: WacomKnowledgeService = WacomKnowledgeService(application_name="Wacom Knowledge Listing",
service_url=args.instance)
# User Management
user_management: UserManagementServiceAPI = UserManagementServiceAPI(service_url=args.instance)
# Group Management
group_management: GroupManagementService = GroupManagementService(service_url=args.instance)
admin_token, refresh_token, expiration_time = user_management.request_user_token(TENANT_KEY, EXTERNAL_USER_ID)
# Now, we create a users
u1, u1_token, _, _ = user_management.create_user(TENANT_KEY, "u1")
u2, u2_token, _, _ = user_management.create_user(TENANT_KEY, "u2")
u3, u3_token, _, _ = user_management.create_user(TENANT_KEY, "u3")

# Now, let's create an entity
thing: ThingObject = create_entity()
entity_uri: str = knowledge_client.create_entity(thing, auth_key=u1_token)
# Only user 1 can access the entity from cloud storage
my_thing: ThingObject = knowledge_client.entity(entity_uri, auth_key=u1_token)
print(f'User is the owner of {my_thing.owner}')
# Now only user 1 has access to the personal entity
knowledge_client.entity(entity_uri, auth_key=u1_token)
# Try to access the entity
try:
knowledge_client.entity(entity_uri, auth_key=u2_token)
except WacomServiceException as we:
print(f"Expected exception as user 2 has no access to the personal entity of user 1. Exception: {we}")
print(f"Status code: {we.status_code}")
print(f"Response text: {we.service_response}")
# Try to access the entity
try:
knowledge_client.entity(entity_uri, auth_key=u3_token)
except WacomServiceException as we:
print(f"Expected exception as user 3 has no access to the personal entity of user 1. Exception: {we}")
# Now, user 1 creates a group
g: Group = group_management.create_group("test-group", auth_key=u1_token)
# Shares the join key with user 2 and user 2 joins
group_management.join_group(g.id, g.join_key, auth_key=u2_token)
# Share entity with group
group_management.add_entity_to_group(g.id, entity_uri, auth_key=u1_token)
# Now, user 2 should have access
other_thing: ThingObject = knowledge_client.entity(entity_uri, auth_key=u2_token)
print(f'User 2 is the owner of the thing: {other_thing.owner}')
# Try to access the entity
try:
knowledge_client.entity(entity_uri, auth_key=u3_token)
except WacomServiceException as we:
print(f"Expected exception as user 3 still has no access to the personal entity of user 1. Exception: {we}")
print(f"URL: {we.url}, method: {we.method}")
print(f"Status code: {we.status_code}")
print(f"Response text: {we.service_response}")
print(f"Message: {we.message}")
# Un-share the entity
group_management.remove_entity_to_group(g.id, entity_uri, auth_key=u1_token)
# Now, again no access
try:
knowledge_client.entity(entity_uri, auth_key=u2_token)
except WacomServiceException as we:
print(f"Expected exception as user 2 has no access to the personal entity of user 1. Exception: {we}")
print(f"URL: {we.url}, method: {we.method}")
print(f"Status code: {we.status_code}")
print(f"Response text: {we.service_response}")
print(f"Message: {we.message}")
group_management.leave_group(group_id=g.id, auth_key=u2_token)
# Now, share the entity with the whole tenant
my_thing.tenant_access_right.read = True
knowledge_client.update_entity(my_thing, auth_key=u1_token)
# Now, all users can access the entity
knowledge_client.entity(entity_uri, auth_key=u2_token)
knowledge_client.entity(entity_uri, auth_key=u3_token)
# Finally, clean up
knowledge_client.delete_entity(entity_uri, force=True, auth_key=u1_token)
# Remove users
user_management.delete_user(TENANT_KEY, u1.external_user_id, u1.id, force=True)
user_management.delete_user(TENANT_KEY, u2.external_user_id, u2.id, force=True)
user_management.delete_user(TENANT_KEY, u3.external_user_id, u3.id, force=True)

Ontology Creation
The samples show how the ontology can be extended and new entities can be added using the added classes.
import argparse
import sys
from typing import Optional, List

from knowledge.base.entity import Label, Description
from knowledge.base.language import EN_US, DE_DE
from knowledge.base.ontology import DataPropertyType, OntologyClassReference, OntologyPropertyReference, ThingObject, \
DataProperty, OntologyContext
from knowledge.services.graph import WacomKnowledgeService
from knowledge.services.ontology import OntologyService
from knowledge.services.session import PermanentSession

# ------------------------------- Constants ----------------------------------------------------------------------------
LEONARDO_DA_VINCI: str = 'Leonardo da Vinci'
CONTEXT_NAME: str = 'core'
# Wacom Base Ontology Types
PERSON_TYPE: OntologyClassReference = OntologyClassReference.parse("wacom:core#Person")
# Demo Class
ARTIST_TYPE: OntologyClassReference = OntologyClassReference.parse("demo:creative#Artist")
# Demo Object property
IS_INSPIRED_BY: OntologyPropertyReference = OntologyPropertyReference.parse("demo:creative#isInspiredBy")
# Demo Data property
STAGE_NAME: OntologyPropertyReference = OntologyPropertyReference.parse("demo:creative#stageName")


def create_artist() -> ThingObject:
"""
Create a new artist entity.
Returns
-------
instance: ThingObject
Artist entity
"""
# Main labels for entity
topic_labels: List[Label] = [
Label('Gian Giacomo Caprotti', EN_US),
]

# Topic description
topic_description: List[Description] = [
Description('Hidden entity to explain access management.', EN_US),
Description('Verstecke Entität, um die Zugriffsteuerung zu erlären.', DE_DE)
]

data_property: DataProperty = DataProperty(content='Salaj',
property_ref=STAGE_NAME,
language_code=EN_US)
# Topic
artist: ThingObject = ThingObject(label=topic_labels, concept_type=ARTIST_TYPE, description=topic_description)
artist.add_data_property(data_property)
return artist


if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--user", help="External Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-t", "--tenant", help="Tenant Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-i", "--instance", default="https://private-knowledge.wacom.com", help="URL of instance")
args = parser.parse_args()
TENANT_KEY: str = args.tenant
EXTERNAL_USER_ID: str = args.user
# Wacom Ontology REST API Client
ontology_client: OntologyService = OntologyService(service_url=args.instance)
knowledge_client: WacomKnowledgeService = WacomKnowledgeService(
application_name="Ontology Creation Demo",
service_url=args.instance)
# Login as admin user
session: PermanentSession = ontology_client.login(TENANT_KEY, EXTERNAL_USER_ID)
if session.roles != "TenantAdmin":
print(f'User {EXTERNAL_USER_ID} is not an admin user.')
sys.exit(1)
knowledge_client.use_session(session.id)
knowledge_client.ontology_update()
context: Optional[OntologyContext] = ontology_client.context()
if context is None:
# First, create a context for the ontology
ontology_client.create_context(name=CONTEXT_NAME, base_uri=f'demo:{CONTEXT_NAME}')
context_name: str = CONTEXT_NAME
else:
context_name: str = context.context
# Creating a class which is a subclass of a person
ontology_client.create_concept(context_name, reference=ARTIST_TYPE, subclass_of=PERSON_TYPE)

# Object properties
ontology_client.create_object_property(context=context_name, reference=IS_INSPIRED_BY, domains_cls=[ARTIST_TYPE],
ranges_cls=[PERSON_TYPE], inverse_of=None, subproperty_of=None)
# Data properties
ontology_client.create_data_property(context=context_name, reference=STAGE_NAME,
domains_cls=[ARTIST_TYPE],
ranges_cls=[DataPropertyType.STRING],
subproperty_of=None)
# Commit the changes of the ontology. This is very important to confirm changes.
ontology_client.commit(context=context_name)
# Trigger graph service. After the update the ontology is available and the new entities can be created
knowledge_client.ontology_update()

res_entities, next_search_page = knowledge_client.search_labels(search_term=LEONARDO_DA_VINCI,
language_code=EN_US, limit=1000)
leo: Optional[ThingObject] = None
for entity in res_entities:
# Entity must be a person and the label match with full string
if entity.concept_type == PERSON_TYPE and LEONARDO_DA_VINCI in [la.content for la in entity.label]:
leo = entity
break

artist_student: ThingObject = create_artist()
artist_student_uri: str = knowledge_client.create_entity(artist_student)
knowledge_client.create_relation(artist_student_uri, IS_INSPIRED_BY, leo.uri)

Asynchronous Client
The sample shows how to use the asynchronous client.
Most of the methods are available in the asynchronous client(s).
Only for the ontology management the asynchronous client is not available.
import argparse
import asyncio
import uuid
from pathlib import Path
from typing import Tuple, List, Dict, Any, Optional

from knowledge.base.entity import Label
from knowledge.base.language import LanguageCode, EN, SUPPORTED_LOCALES, EN_US
from knowledge.base.ontology import ThingObject
from knowledge.ontomapping import load_configuration
from knowledge.ontomapping.manager import wikidata_to_thing
from knowledge.public.relations import wikidata_relations_extractor
from knowledge.public.wikidata import WikidataSearchResult, WikiDataAPIClient, WikidataThing
from knowledge.services.asyncio.graph import AsyncWacomKnowledgeService
from knowledge.services.asyncio.group import AsyncGroupManagementService
from knowledge.services.asyncio.users import AsyncUserManagementService
from knowledge.services.base import WacomServiceException, format_exception
from knowledge.services.group import Group
from knowledge.services.session import PermanentSession, RefreshableSession
from knowledge.services.users import UserRole, User


def import_entity_from_wikidata(search_term: str, locale: LanguageCode) -> Dict[str, ThingObject]:
"""
Import entity from Wikidata.
Parameters
----------
search_term: str
Search term
locale: LanguageCode
Language code

Returns
-------
things: Dict[str, ThingObject]
Mapping qid to thing object
"""
search_results: List[WikidataSearchResult] = WikiDataAPIClient.search_term(search_term, locale)
# Load mapping configuration
load_configuration(Path(__file__).parent.parent / 'pkl-cache' / 'ontology_mapping.json')
# Search wikidata for entities
qid_entities: List[WikidataThing] = WikiDataAPIClient.retrieve_entities([sr.qid for sr in search_results])
qid_things: Dict[str, WikidataThing] = {qt.qid: qt for qt in qid_entities}
relations: Dict[str, List[Dict[str, Any]]] = wikidata_relations_extractor(qid_things)
# Now, let's create the things
things: Dict[str, ThingObject] = {}
for res in qid_entities:
wikidata_thing, import_warnings = wikidata_to_thing(res, all_relations=relations,
supported_locales=SUPPORTED_LOCALES,
pull_wikipedia=True,
all_wikidata_objects=qid_things)
things[res.qid] = wikidata_thing
return things


async def user_management_sample(tenant_api_key: str, instance: str) -> Tuple[User, str, str]:
"""
User management sample.
Parameters
----------
tenant_api_key: str
Session
instance: str
Instance URL

Returns
-------
user: User
User object
user_token: str
User token
refresh_token: str
Refresh token
"""
user_management: AsyncUserManagementService = AsyncUserManagementService(
application_name="Async user management sample",
service_url=instance)
meta_data: dict = {'user-type': 'demo'}
user, user_token, refresh_token, _ = await user_management.create_user(tenant_key=tenant_api_key,
external_id=uuid.uuid4().hex,
meta_data=meta_data,
roles=[UserRole.USER])
return user, user_token, refresh_token


async def clean_up(instance: str, tenant_api_key: str):
"""
Clean up sample.
Parameters
----------
instance: str
Instance URL
tenant_api_key: str
Tenant API key
"""
user_management: AsyncUserManagementService = AsyncUserManagementService(
application_name="Async user management sample",
service_url=instance)
users: List[User] = await user_management.listing_users(tenant_api_key)
for user in users:
if 'user-type' in user.meta_data and user.meta_data['user-type'] == 'demo':
await user_management.delete_user(tenant_key=tenant_api_key, external_id=user.external_user_id,
internal_id=user.id, force=True)


async def main(external_user_id: str, tenant_api_key: str, instance: str):
"""
Main function for the async sample.

Parameters
----------
external_user_id: str
External Id of the shadow user within the Wacom Personal Knowledge.
tenant_api_key: str
Tenant api key of the shadow user within the Wacom Personal Knowledge.
instance: str
URL of instance
"""
async_client: AsyncWacomKnowledgeService = AsyncWacomKnowledgeService(application_name="Async sample",
service_url=instance)
permanent_session: PermanentSession = await async_client.login(tenant_api_key=tenant_api_key,
external_user_id=external_user_id)
"""
The permanent session contains the external user id, the tenant id, thus it is capable to refresh the token and
re-login if needed. The functions check if the token is expired and refresh it if needed. Internally, the token
manager handles the session. There are three different session types:
- Permanent session: The session is refreshed automatically if needed.
- Refreshable session: The session is not refreshed automatically using the refresh token,
but if the session is not used for a day the refresh token is invalidated.
- Timed session: The session is only has the authentication token and no refresh token. Thus, it times out after
one hour.
"""
print(f'Service instance: {async_client.service_url}')
print('-' * 100)
print(f'Logged in as {permanent_session.external_user_id} (tenant id: {permanent_session.tenant_id}) ')
is_ten_admin: bool = permanent_session.roles == "TenantAdmin"
print(f'Is tenant admin: {is_ten_admin}')
print('-' * 100)
print(f'Token information')
print('-' * 100)
print(f'Refreshable: {permanent_session.refreshable}')
print(f'Token must be refreshed before: {permanent_session.expiration} UTC')
print(f'Token expires in {permanent_session.expires_in} seconds)')
print('-' * 100)
print(f'Creating two users')
print('-' * 100)
# User management sample
user_1, user_token_1, refresh_token_1 = await user_management_sample(tenant_api_key, instance)
print(f'User: {user_1}')
user_2, user_token_2, refresh_token_2 = await user_management_sample(tenant_api_key, instance)
print(f'User: {user_2}')
print('-' * 100)
async_client_user_1: AsyncWacomKnowledgeService = AsyncWacomKnowledgeService(application_name="Async user 1",
service_url=instance)
refresh_session_1: RefreshableSession = await async_client_user_1.register_token(auth_key=user_token_1,
refresh_token=refresh_token_1)
async_client_user_2: AsyncWacomKnowledgeService = AsyncWacomKnowledgeService(application_name="Async sample",
service_url=instance)
await async_client_user_2.register_token(auth_key=user_token_2, refresh_token=refresh_token_2)
"""
Now, let's create some entities.
"""
print('Creation of entities')
print('-' * 100)
things_objects: Dict[str, ThingObject] = import_entity_from_wikidata('Leonardo da Vinci', EN)
created: List[ThingObject] = await async_client_user_1.create_entity_bulk(list(things_objects.values()))
for thing in created:
try:
await async_client_user_2.entity(thing.uri)
except WacomServiceException as we:
print(f'User 2 cannot see entity {thing.uri}.\n{format_exception(we)}')

# Now using the group management service
group_management: AsyncGroupManagementService = AsyncGroupManagementService(application_name="Group management",
service_url=instance)
await group_management.use_session(refresh_session_1.id)
# User 1 creates a group
new_group: Group = await group_management.create_group("sample-group")
for thing in created:
try:
await group_management.add_entity_to_group(new_group.id, thing.uri)
except WacomServiceException as we:
print(f'User 1 cannot delete entity {thing.uri}.\n{format_exception(we)}')
await group_management.add_user_to_group(new_group.id, user_2.id)
print(f'User 2 can see the entities now. Let us check with async client 2. '
f'Id of the user: {async_client_user_2.current_session.external_user_id}')
for thing in created:
iter_thing: ThingObject = await async_client_user_2.entity(thing.uri)
label: Optional[Label] = iter_thing.label_lang(EN_US)
print(f'User 2 can see entity {label.content if label else "UNKNOWN"} {iter_thing.uri}.'
f'Ownership: owner flag:={iter_thing.owner}, owner is {iter_thing.owner_id}.')
print('-' * 100)
await clean_up(instance=instance, tenant_api_key=tenant_api_key)


if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--user", help="External Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-t", "--tenant", help="Tenant Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-i", "--instance", default='https://private-knowledge.wacom.com',
help="URL of instance")
args = parser.parse_args()
asyncio.run(main(args.user, args.tenant, args.instance))

Semantic Search
The sample shows how to use the semantic search.
There are two types of search:

Label search
Document search

The label search is used to find entities based on the label.
The document search is used to find documents based on the content.
import argparse
import re
import time
from typing import List, Dict, Any

from knowledge.base.language import EN_US
from knowledge.base.search import LabelMatchingResponse, DocumentSearchResponse, VectorDBDocument
from knowledge.services.search import SemanticSearchClient


def clean_text(text: str, max_length: int = -1) -> str:
"""
Clean text from new lines and multiple spaces.

Parameters
----------
text: str
Text to clean.
max_length: int [default=-1]
Maximum length of the cleaned text. If length is - 1 then the text is not truncated.

Returns
-------
str
Cleaned text.
"""
# First remove new lines
text = text.strip().replace('\n', ' ')
# Then remove multiple spaces
text = re.sub(r'\s+', ' ', text)
if 0 < max_length < len(text):
return text[:max_length] + '...'
return text


if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--user", help="External Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-t", "--tenant", help="Tenant Id of the shadow user within the Wacom Personal Knowledge.",
required=True)
parser.add_argument("-i", "--instance", default="https://private-knowledge.wacom.com", help="URL of instance")
args = parser.parse_args()
client: SemanticSearchClient = SemanticSearchClient(service_url=args.instance)
session = client.login(args.tenant, args.user)
max_results: int = 10
labels_count: int = client.count_documents(locale=EN_US)
print(f"Tenant ID: {client.current_session.tenant_id} | Labels count: {labels_count} for [locale:={EN_US}]")
t0: float = time.time()
results: LabelMatchingResponse = client.labels_search(query="Leonardo Da Vinci", locale=EN_US,
max_results=max_results)
t1: float = time.time()
if len(results.results) > 0:
print("=" * 120)
for idx, res in enumerate(results.results):
print(f"{idx + 1}. {res.label} | Relevance: ({res.score:.2f}) | URI: {res.entity_uri}")
all_labels: List[VectorDBDocument] = client.retrieve_labels(EN_US, results.results[0].entity_uri)
print("=" * 120)
print(f"Labels for best match: {results.results[0].entity_uri}")
for idx, label in enumerate(all_labels):
print(f"{idx + 1}. {label.content}")
print("=" * 120)
print(f"Time: {(t1 - t0) * 1000:.2f} ms")
print("=" * 120)
document_count: int = client.count_documents(locale=EN_US)
print(f"Document count: {document_count} for [locale:={EN_US}]")
t2: float = time.time()
document_results: DocumentSearchResponse = client.document_search(query="Leonardo Da Vinci artwork", locale=EN_US,
max_results=max_results)
t3: float = time.time()
print("=" * 120)
if len(document_results.results) > 0:

for idx, res in enumerate(document_results.results):
print(f"{idx + 1}. URI: {res.content_uri} | Relevance: {res.score:.2f} | Chunk:"
f"\n\t{clean_text(res.content_chunk, max_length=100)}")
print(f"\n All document chunks for best match: {document_results.results[0].content_uri}")
print("=" * 120)
# If you need all document chunks, you can retrieve them using the content_uri.
best_match_uri: str = document_results.results[0].content_uri
chunks: List[VectorDBDocument] = client.retrieve_documents_chunks(locale=EN_US, uri=best_match_uri)
metadata: Dict[str, Any] = document_results.results[0].metadata
for idx, chunk in enumerate(chunks):
print(f"{idx + 1}. {clean_text(chunk.content)}")
print("\n\tMetadata:\n\t---------")
for key, value in metadata.items():
print(f"\t- {key}: {clean_text(value, max_length=100) if isinstance(value, str) else value }")
print("=" * 120)
print(f"Time: {(t3 - t2) * 1000:.2f} ms")
print("=" * 120)

Tools
The following samples show how to utilize the library to work with Wacom's Personal Knowledge.
Tenant creation
The script setup_tenant.py creates a tenant with a user and an ontology.
It takes a JSON configuration file as input. Here's an example of a configuration file:
{
"service": {
"url": "<URL-OF-DEPLOYMENT>"
},
"tenant": {
"api_key": "<TENANT-API-KEY>"
},
"users": [
{
"external_id": "<ADMIN-EXTERNAL-ID>",
"role": "tenantadmin",
"meta_data": {
"user_type": "system"
}
},
{
"external_id": "<USER-EXTERNAL-ID>",
"role": "user",
"meta_data": {
"user_type": "system",
"description": "This is a user created by the system"
}
}
],
"ontology": {
"context": "<CORE-NAME>",
"schema": "<SCHEMA>"
}
}

This JSON configuration is designed to set up a tenant for a private knowledge system.
It specifies the service URL, tenant API key, users, and initial ontology for the knowledge graph.
Here's a breakdown of its structure:


service: This section contains information about the service deployment.

url: The URL of the deployment.



tenant: This section includes details specific to the tenant.

api_key: The API key for the tenant.



users: This array holds information about users who will have access to the tenant. Each user has the following fields:

external_id: A unique identifier for the user.
role: Defines the user's role within the tenant. Possible roles include "tenantadmin" for administrative access and "user" for general user access.
meta_data: Additional information about the user.

user_type: Specifies the type of user, e.g., "system".
description (optional): Provides a description of the user, particularly useful for system-created users.





ontology: This section defines the initial setup of the knowledge graph.

context: The context of the ontology, e.g., "core".
schema: Specifies the schema to be used, e.g., "company".



Listing script
Listing the entities for tenant.
>> python listing.py [-h] -u USER -t TENANT [-r] [-i INSTANCE]

Parameters:

-i INSTANCE, --instance INSTANCE - URL of instance
-u USER, --user USER - External ID to identify user of the Wacom Private Knowledge
-t TENANT, --tenant TENANT - Tenant key to identify tenant
-r, --relations (optional) - Requesting the relations for each entity

Export entities script
Dump all entities of a user to a ndjson file.
>> python export_entities.py [-h] -u USER -t TENANT [-r] [-a] [-p] [-d DUMP]
[-c CONCEPT_TYPE] [-i INSTANCE]

Parameters:

-i INSTANCE, --instance INSTANCE - URL of instance. (default:=https://private-
knowledge.wacom.com)
-u USER, --user USER - External ID to identify user of the Wacom Private Knowledge
-t TENANT, --tenant TENANT - Tenant key to identify tenant
-r, --relations (optional) - Requesting the relations for each entity
-a, --all (optional) - All entities the user as access to, otherwise only his own entities are dumped.
-p, --images (optional) - Include the images in the dump.
-d DUMP, --dump DUMP - Defines the location of the dump path.

Import entities script
Pushing entities to knowledge graph.
>> python import_entities.py [-h] [-u USER] [-t TENANT] [-g GROUP_NAME] [-r] [-i INSTANCE]

Parameters:

-i INSTANCE, --instance INSTANCE - URL of instance
-u USER, --user USER - External ID to identify user of the Wacom Private Knowledge
-t TENANT, --tenant TENANT - Tenant key to identify tenant
-i CACHE, --cache CACHE - Path to entities that must be imported.
-g GROUP_NAME, --group_id GROUP_NAME - Group name where the entities will be assigned to.
-p , --public - Group name where the entities will be assigned to.

Wikidata scrapper
usage: wikidata_scrapper.py [-h] -u USER -t TENANT [-i INSTANCE] [-c CACHE]
[-m MAPPING] [-d DEPTH]
[-l LANGUAGES [LANGUAGES ...]]

Parameters:

-i INSTANCE, --instance INSTANCE - URL of instance
-u USER, --user USER - External ID to identify user of the Wacom Private Knowledge
-t TENANT, --tenant TENANT - Tenant key to identify tenant
-c CACHE, --cache CACHE - Path to entities that must are exports in import format.
-m MAPPING, --mapping MAPPING - Mapping file to configure the wikidata mapping.
-d DEPTH, --depth DEPTH - Depth of the graph to be scrapped.
-l LANGUAGES [LANGUAGES ...], --languages LANGUAGES [LANGUAGES ...] - Languages to be scrapped.

Wikidata search
usage: wikidata_search.py [-h] [-t TERM] [-l LANGUAGE]

Parameters:

-t TERM, --term TERM - Search term.
-l LANGUAGE, --language LANGUAGE - Language code

Semantic Label Search
usage: semantic_search_labels.py [-h] -u USER -t TENANT [-i INSTANCE] -q QUERY
[-l LOCALE] -m MAX_RESULTS

Parameters:

-i INSTANCE, --instance INSTANCE - URL of instance
-u USER, --user USER - External ID to identify user of the Wacom Private Knowledge
-t TENANT, --tenant TENANT - Tenant key to identify tenant
-q QUERY, --query QUERY - Search term
-l LOCALE, --locale LOCALE - Language code
-m MAX_RESULTS, --max_results MAX_RESULTS - Maximum number of results

Semantic Document Search
usage: semantic_search_documents.py [-h] -u USER -t TENANT [-i INSTANCE] -q
QUERY [-l LOCALE] -m MAX_RESULTS

Parameters:

-i INSTANCE, --instance INSTANCE - URL of instance
-u USER, --user USER - External ID to identify user of the Wacom Private Knowledge
-t TENANT, --tenant TENANT - Tenant key to identify tenant
-q QUERY, --query QUERY - Search term
-l LOCALE, --locale LOCALE - Language code
-m MAX_RESULTS, --max_results MAX_RESULTS - Maximum number of results

Documentation
You can find more detailed technical documentation, here.
API documentation is available here.
Contributing
Contribution guidelines are still work in progress.
License
Apache License 2.0