Formal knowledge descriptions have been one of the main tools for enabling knowledge representation and reasoning in AI. The W3C Semantic Web initiative proposed a set of standards, including on a common data interchange format that enabled greater interoperability and integration between knowledge descriptions generated by di erent organizations in the academy and industry. Nonetheless, these standards have been unable to provide contextualized integration of knowledge descriptions e ectively, as they do not provide constructs to explicitly de ne contextualized n-dimensional (e.g. time and space, n-ary and hierarchical facts, etc.) relationships between concepts from di erent descriptions, leading to ine ciencies in the reasoning and query processing over decontextualized relationships, and inconsistencies in the modeling approaches utilized for dealing with this limitation. The main goal of this demo is to present how a hybrid knowledge representation model and description language, namely Hyperknowledge, allows contextualized integration of knowledge descriptions using constructs that enable the explicit de nition of rich contextual relationships. This demonstration will be performed using the Knowledge Explorer System (KES), that supports visualization and management of the e ective contextualization of Hyperknowledge expressivity w.r.t. n-dimensional relationships between concepts from multiple descriptions and between those concepts with their corresponding non-symbolic content.
As the AI industry and research community grow, the number and size of knowledge descriptions produced and shared using the W3C Semantic Web Standards 2 (WSWS) tend to grow at a similar pace.
Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). 2 https://www.w3.org/standards/semanticweb/
Meanwhile, it has become clear that knowledge descriptions are not always
absolute, but instead are assumed to hold under certain circumstances that de ne
a speci c context [
Existing alternatives based on the WSWS, such as RDF graphs/datasets 3, the OWL Time Ontology 4 or Ontology Patterns for N-ary relations 5 can be used to work around the expressivity limitations of the underlying conceptual model, but they fail to provide a general approach in the following aspects, respectively: Multiple valid interpretations depending on the assumptions made for graph naming and meaning; addresses only one speci c contextual dimension using the same constructs that are used to represent the knowledge descriptions associated with it; propose multiple ways of representing the same n-ary relations using binary relations, as the underlying conceptual model is unable to represent them as rst-class constructs.
Therefore, as the WSWS does not prescribe a standard approach to contextualize knowledge descriptions, the context of those descriptions is often expressed in their identi ers or using textual labels and annotations, which is both ine cient, w.r.t reasoning and query processing but also hard to standardize across multiple organizations.
Several theories of context have been investigated in seminal works of the
elds of AI and knowledge representation, for example, the formalization of
contexts as rst-class constructs [
The integration of knowledge and non-symbolic content has been deeply
inuenced by the semantic gap [
Previous solutions from the non-symbolic AI community, such as metadata standards and models, attempted to bridge this gap by allowing the speci cation of prede ned elds to describe low-level aspects of non-symbolic content (e.g. MPEG-7 6 and Dublin Core 7). Meanwhile, solutions from the symbolic AI 3 https://www.w3.org/TR/rdf11-datasets 4 https://www.w3.org/TR/owl-time/ 5 https://www.w3.org/2001/sw/BestPractices/OEP/n-aryRelations-20060323 6 https://mpeg.chiariglione.org/standards/mpeg-7 7 https://dublincore.org/speci cations/dublin-core/ community, such as the WSWS, focused on formally describing abstract concepts and semantic relationships between them (e.g., RDF 8, OWL 9).
Hyperknowledge lls the gap left by previous solutions by providing
rstclass constructs that can be used to explicitly describe relationships between
concepts and n-dimensional fragments of non-symbolic content [
The Hyperknowledge Conceptual Model
The Hyperknowledge conceptual model is composed of three main groups of
entities: terminal nodes, composite nodes, and links [
A terminal node is composed of a collection of information units. The exact notion of what constitutes an information unit is part of the node de nition and depends on its specialization. A context node is a composite node (or a set) that may contain links, terminal nodes, and composite nodes. Links de ne relationships among nodes' anchors. There are di erent types of links such as SPO (subject-predicate-object) links, causal links, and constraint links.
Every node, either composite or terminal, can be connected with other nodes through anchors, that represent portions or fragments of a node. Anchors can be used to represent n-dimensional fragments of a node (e.g time and space dimensions), enabling the creation of links between either concept or content nodes within those dimensions. Also, as every entity in the Hyperknowledge model can be contained in a composite node, it is possible to create links that represent the relationships between nodes in a given context.
Therefore, the n-dimensional anchors and the context nodes can be used to explicitly contextualize n-dimensional relationships between concepts from multiple knowledge descriptions and corresponding non-symbolic content, which are represented as content nodes. 3
Knowledge Description and Non-Symbolic Integration To enable the integration of knowledge descriptions and non-symbolic content using Hyperknowledge, we devised an architecture that enables both human users (e.g., knowledge engineers, domain experts, and developers) and other systems (e.g. AI assistants and tools) to interact with the same Hyperknowlege Base (HKBase).
An overview of this architecture is depicted in Figure 1 where we highlight
the process of ingestion of knowledge descriptions, expressed using WSWS (e.g.,
8 https://www.w3.org/RDF/
9 https://www.w3.org/OWL/
RDF and OWL), and non-symbolic content (e.g., ML models and images).
Human users can perform this process using KES while other systems can
communicate directly with the HKBase using a RESTful API [
For this demonstration, the main storage option used for the Hyperknowledge descriptions is a triple store as it enables e cient ingestion and querying of knowledge descriptions based on WSWS, which makes it easier to integrate applications that were originally implemented using WSWS. As illustrated by the dashed arrows and boxes in Figure 1 the Hyperknowledge descriptions could be stored and queried using a document or graph database as well.
For the ingestion of non-symbolic content, we currently make use of object storage but future implementations will make use of optimized storage options for each type of content.
Our main object storage option is IBM Cloud Object Storage 10 but our system can be used with any AWS S3 11 compatible object storage. In that sense, we currently have support to all Hyperknowledge features using either MongoDB 12 or JanusGraph 13, but the extended RDF compatibility and SPARQL support are optimized when using a triple store, which in the current implementation is Apache Jena 14.
Datasets
Knowledge Descriptions Non-Symbolic
Content
Knowledge Engineers,Domain
Experts & Developers Knowledge Explorer
System (KES)
AI Assistants &Tools Hyperknowledge Base
Hyperknowledge
Descriptions Non-Symbolic Content
Storage
Triple Store Document Database
Graph Database
Object Storage
10 https://www.ibm.com/cloud/object-storage
11 https://docs.aws.amazon.com/AmazonS3
12 https://www.mongodb.com/
13 https://janusgraph.org/
14 https://jena.apache.org/
The main goal of this demo is to show Hyperknowledge features that support the
contextualized integration of multiple knowledge descriptions and non-symbolic
content through KES. In this sense, we de ned a dataset containing tra c
simulation images extracted from a visual perception benchmark [