Extended Knowledge Graphs: A Conceptual Study

∗

Weronika T. Adrian

, Marek Adrian

, Krzysztof Kluza

Bernadetta Stachura-Terlecka

and Antoni Lig˛eza

AGH University of Science and Technology, al. A.Mickiewicza 30, 30-059 Krakow, Poland

Keywords:

Knowledge Graphs, Semantic Networks, Knowledge Representation, KRR, Rules, Ontologies, Reasoning,

Logic Programming, Answer Set Programming, Prolog.

Abstract:

The amount and variety of data that we produce every day pose a constant challenge for meaningful informa-

tion processing. While knowledge graphs have gained a considerable attention in the recent years, due to their

ﬂexible and universal knowledge representation, they lack the mechanisms facilitating knowledge processing.

In this paper, we propose an information system called extended knowledge graphs (EKG) that augments the

concept of knowledge graphs with procedural attachments. We put forward the requirements and assumption

of the EKG structure and present a categorisation of supported reasoning tasks.

1 INTRODUCTION

The amount of data produced and stored every day

may be daunting for who wants to process it efﬁ-

ciently. The variety of data adds an additional difﬁ-

culty layer for a meaningful analysis of the informa-

tion encoded within. In fact, it seems that knowledge

representation methods do not keep up with the ever-

growing mass of data to process, leaving us with sub-

optimal solutions, either in terms of possible tasks to

perform or the amount of data that can be processed.

In the recent years, a considerable attention has

been gained by so-called knowledge graphs (Hogan

et al., 2020; Fensel et al., 2020; Ji et al., 2020) –

graph-based knowledge representation systems that

can organize information in a ﬂexible and intuitive

way. Although they are used in both academic (Paul-

heim, 2017) and business (Noy et al., 2019) commu-

nications, there is no commonly agreed upon deﬁ-

nition of them (Ehrlinger and Wöß, 2016). In fact,

some parties see them as an reincarnation of on-

tologies (Uschold et al., 1996; Staab and Studer,

2010; Ławrynowicz, 2017), other deﬁne them as

knowledge-based systems (KBS) (Akerkar and Sajja,

2009; Ahmed et al., 2019) that consist of a knowledge

https://orcid.org/0000-0002-1860-6989

https://orcid.org/0000-0002-0435-0994

https://orcid.org/0000-0003-1876-9603

https://orcid.org/0000-0003-2887-5936

https://orcid.org/0000-0002-6573-4246

∗

This Paper Is Supported by AGH UST Grant.

base and a reasoner, or ﬁnally — a KBS with a knowl-

edge integration component. In this last deﬁnition, a

knowledge graph should integrate information from

different sources into an ontology and provide means

to reason over the integrated knowledge.

Regardless of the adopted deﬁnition, knowledge

graphs still deal with only static knowledge represen-

tation, i.e., describing the properties of objects and

classes (sets of objects) and relations between them.

In practical use cases, one often is interested in associ-

ating also some dynamic procedures to static objects

or classes. For instance, an editor in a journal would

like to “process” a submitted paper which requiress,

i.a., ﬁnding a suitable person to review the article. To

this end, a candidate reviewer should be familiar with

the topics considered in the paper, he or she should

not have a conﬂict of interests with the paper’s author

(that e.g. may mean they should not be co-authors or

work in the same department) etc. While theoretically

we already have access to all the needed information

e.g., in the ORCID and DBLP databases or Microsoft

Academic Graph that describe the scientists and their

work in a graph-like structure, it is not easy to solve

such a problem automatically. To address such prob-

lems, we propose to extend the knowledge graph rep-

resentation with procedural attachments, such that a

procedure (understood as a process that contains some

decisions) can be associated with a speciﬁc type of

objects deﬁned in the graph.

In the following sections, we present extended

knowledge graphs (EKG) to encode both structural

Adrian, W., Adrian, M., Kluza, K., Stachura-Terlecka, B. and Lig˛eza, A.

Extended Knowledge Graphs: A Conceptual Study.

DOI: 10.5220/0010111601730180

In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD, pages 173-180

ISBN: 978-989-758-474-9

173

(static) and procedural (dynamic) knowledge. We

outline its rationale and main objectives, discuss main

features and components and illustrate its use with ex-

emplary use cases. We argue that a procedural exten-

sion to KG representation can be both beneﬁcial for

practical use cases and plausible to implement using

existing logical reasoners.

The contributions of the paper are threefold:

• Knowledge Representation: we discuss a new lan-

guage for representing both static graph-based

knowledge and dynamic procedures;

• Typology of Operations: we consider differ-

ent types of problems that extended knowledge

graphs should address and organize them into

groups

• Applications: we illustrate the use of our sys-

tem on practical examples that describe academic

community in terms of publications and collabo-

ration.

2 BACKGROUND AND

MOTIVATION

Knowledge Representation (KR) methods can be di-

vided into descriptive (or declarative) and procedu-

ral (or imperative) ones (Torsun, 1995). The descrip-

tive methods include tables, formulas or graphs and

they are characterised by storing information explic-

itly. On the contrary, knowledge expressed in proce-

dural manner is manifested in the actual execution of

the program and is therefore stored implicitly. The

declarative methods are easier to expand and modify.

However, in various application, intelligent systems

may need both kinds of representation.

“A semantic network or net is a graphic nota-

tion for representing knowledge in patterns of inter-

connected nodes and arcs.” (Sowa, 1987). Semantic

nets have long been used in philosophy, psychology

and linguistic before the computer implementation of

them were developed. They have represented knowl-

edge or supported automated reasoning. A declar-

ative graphic representation characterizes semantic

networks. There exist various sorts of semantic net-

works, designed for different purposes and of differ-

ent levels of formalisation. In (Sowa, 1987), the fol-

lowing classiﬁcation of semantic nets was introduced:

1. Deﬁnitional Networks. In these networks the sub-

type or is-a relation between a concept type and a

newly deﬁned subtype is emphasised. Their basic

mechanisms are ”as old as Aristotle”, who worked

on a classiﬁcation of nature. Deﬁnitorial networks

support the rule of inheritance and, usually, mono-

tonic reasoning, in which inferred knowledge is

added, but what is once known does not change.

2. Assertional Networks are designed to assert

propositions. Some of the assertional (proposi-

tional) networks have been used to graphically

represent structures underlying natural language

semantics. C. S. Peirce introduced relational

graphs and existential graphs, by means of which

he intended to show ”the atoms and molecules of

logic”. Propositional networks include also Tes-

nière’s dependency graphs used to represent nat-

ural language sentences. A variety of semantic

propositional networks called Conceptual Graphs

have been developed by J. F. Sowa. Core con-

ceptual graphs are successors of Peirce’s exis-

tential graphs while extended and research con-

ceptual graphs explored novel techniques for rea-

soning, knowledge representation and natural lan-

guage semantics. For core and extended CG the

semantics is deﬁned by a mapping to ISO Com-

mon Logic standard using subset of First Order

Logic.

3. Implicational Networks. These structures use im-

plication as the primary relation for connecting

nodes. They may be used to represent patterns

of beliefs (belief networks, Bayesian networks),

causality (causal networks), or inferences (truth-

maintenance systems). To one graph different in-

terpretations and reasoning may apply. Both log-

ical, forward- and backward-chaining, as well as

probabilistic reasoning may be used.

4. Executable Networks. These networks contain

mechanisms which can perform inferences, pass

messages, or search for patterns and associations

and cause some changes to the network itself.

These mechanisms, which distinguish the net-

works from other, static ones include marker pass-

ing, attached procedures and graph transforma-

tions. Dataﬂow graphs and Petri Nets, although

rarely classiﬁed as semantic networks, use similar

techniques as procedural semantic networks.

5. Learning networks build or extend their repre-

sentations by acquiring knowledge from exam-

ples. The new knowledge may change the old

network by adding and deleting nodes and arcs

(rote memory) or by modifying numerical values,

called weights, associated with the nodes and arcs.

Rote memory techniques ﬁnd an application in

case-base reasoning systems, whereas changing

weights is often used in artiﬁcial neural nets.

6. Hybrid networks combine two or more of the pre-

vious techniques, either in a single network or in

KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development

174

separate, but closely interacting networks.

The approach to knowledge representation based

on describing the world of interest in terms of ob-

jects enables to introduce hierarchy and structure into

a knowledge base. Sentences or rules can be grouped

by the concepts (objects) they refer to. One of the pro-

cedural knowledge representation formalism which is

object-oriented is the one of frames, ﬁrst proposed by

Marvin Minsky in 1975. In a basic version of the

formalism, there are two types of frames: individ-

ual frames or instance-frames for representing single

objects and general frames or class-frames for repre-

senting classes or categories of objects. Frames have

attributes or slots, which are ﬁlled by ﬁllers. Two

special distinguished slots were deﬁned, INSTANCE-

OF for individual frames and IS-A for general frames.

By means of them one could specify that given ob-

ject is an instance of a class or introduce hierarchy

between two generic classes. The relations these

slots specify are called generalisation. Other relations

between frames include aggregation, in which sev-

eral frames representing components are associated

with another one, which represents a whole and as-

sociation, which denotes other semantic relationship.

Frame slots could also have attached procedures.

Two basic sorts of such procedures are IF-ADDED

and IF-NEEDED (Brachman et al., 2004) (or accord-

ing to (Negnevitsky, 2005): WHEN-CHANGED and

WHEN-NEEDED). The procedures are executed if a

frame is added or a new value is placed in a slot or

if some computation is needed to determine a slot

value. In (Negnevitsky, 2005), a distinction is drawn

between methods and demons which were both types

of attached procedures. Demons usually are in a form

of an IF-THEN rule and when the attribute speciﬁed

in IF statement changes its value, then the consequent

of the rule is ﬁred. The term method is sometimes

used as a synonym, but it usually denotes more com-

plex procedure consisting of several actions.

Reasoning with frames mainly consists in discov-

ering the relations between frames and creating indi-

vidual instances of generic frames. It involves ﬁll-

ing some slots with values and inferring other val-

ues. Of a particular signiﬁcance are INSTANCE-OF

and IS-A slots. They enable for instantiating indi-

viduals and triggering procedures, which were not

stated explicitly in an individual frame. The pro-

cess of ”passing information from generic frames

down through their specializations and eventually to

their instances” (Brachman et al., 2004) is called in-

heritance and it is present both in frame formalism

and in object-oriented programming. In fact, inheri-

tance is an essential feature of frame-based systems.

Frames were developed concurrently with object-

oriented programming languages and share some of

their intuition. According to (Negnevitsky, 2005),

”frames are an application of object-oriented pro-

gramming for expert systems”. Although frames were

applied in several ﬁelds (Brachman et al., 2004), they

lack the reasoning support and are too inexpressive

for many problems.

More advanced formalisms based on an idea to

represent knowledge in terms of concepts and object

are Description Logics (DL) (Baader et al., 2003).

They enable for constructing complex descriptions

which denote the classes of individuals. As opposed

to the frame languages, Description Logics have for-

mal, First Order Logic semantics. Therefore, they

extend frames’ capabilities and provide intrinsic rea-

soning support. Description Logics became the log-

ical foundations of the Semantic Web ontology lan-

guages, and respective OWL subsets have their log-

ical counterparts in DL languages. It is noteworthy

that the catalogue of reasoning tasks in Description

Logics is limited to several problems, such as classiﬁ-

cation, consistency checking, realization or retrieval.

3 RELATED WORK

Graph-based knowledge representation has been suc-

cessfully used in almost every branch of Artiﬁcial In-

telligence (Sowa, 1992). The newest incarnation of it

are knowledge graphs. As a term, Knowledge Graph

was “re-discovered” and popularized by Google in

their communication about search based on objects,

not words (Singhal, 2012). Since then, the term has

been widely adopted in the Semantic Web community

and beyond.

Basic notions of the knowledge graphs have been

further extended in several directions. Yahya et

al. considers extended knowledge graphs as graphs

that combine relational facts with textual web con-

tents (Yahya et al., 2016a; Yahya et al., 2016b).

Zhang et al. (Zhang et al., 2020) extend knowledge

graphs in order to integrate heterogeneous data re-

sources and enhance knowledge-based services for

health care systems. Thus, their Health Knowledge

Graph Builder may be used to construct disease-

speciﬁc and extensible health knowledge graphs from

multiple sources. Another interpretation of extending

knowledge graphs has been put forward in (Yoo and

Jeong, 2020) in which the authors proposed a method

for auto-extending knowledge graphs.

Combining static graph-based knowledge with a

description of dynamics of a system is a recurring

challenge. Depending on the domain and application,

it appeared thorough years in a form of simple as-

Extended Knowledge Graphs: A Conceptual Study

175

sert/retract operations on conceptual graphs (Mineau,

1998), a range of proposals for integrating rules

and ontologies (Eiter et al., 2008), associative graph

data structures (Horzyk, 2013), and recently in com-

plex processes integrated with “full-ﬂedged data mod-

els” (van der Aalst, 2019). Speciﬁc ways to model the

dynamics are processes and rules.

Business Process Model and Notation

(BPMN) (OMG, 2011) is a notation created by

OMG (Object Management Group) to graphically

represent business processes using diagrams. It

is now a well established standard not only for

modeling, but also for managing process knowledge

of organizations. Recently, the Decision Model

and Notation (DMN) standard (OMG, 2014), also

created by OMG, has been proposed. It works

as a complementary notation for describing rules

and decisions in organizations. DMN is especially

useful for modeling decision logic for processes. As

both processes and decisions constitute executable

speciﬁcations (Janssens et al., 2016), it is possible to

use process and decision engine for deployment and

enactment of such models.

Integrating logic based on rules with static se-

mantic representation of objects is another way to in-

tegrate declarative and procedural knowledge, both

on the theoretical (Eiter et al., 2008; Nalepa and

Furma

nska, 2010a) and practical (Nalepa and Fur-

nska, 2010b) level. Embedding a rule engine into a

semantic system (Adrian et al., 2011) allows to prac-

tically model and test such an integration.

This paper is a communication of a work-in-

progress research on deﬁning yet another knowledge

representation system that would meet the challenges

of processing large graph-based datasets with differ-

ent types of data transformations, outlined and cate-

gorized in the following sections.

4 EXTENDED KNOWLEDGE

GRAPHS

In this section, we put forward our rationale and main

assumptions of extended knowledge graphs (EKG).

We introduce the language syntax and semantics and

outline the architecture of the systems adopting the

language.

4.1 Main Assumptions

In the era of interconnected information systems, we

recognize the value of graph-based knowledge rep-

resentation and its visualization (Dudycz, 2017). It

is both ﬂexible and universal and so we assume that

the data model rely on graphs. Moreover, we assume

that it is convenient to distinguish different levels of

abstraction in a knowledge based system. Thus, we

assume that the following layers should be supported:

1. graph-based knowledge representation,

2. logic layer that allows for different kinds of data

transformations,

3. visualization layer for clear knowledge summary.

While the layered architecture can be already ob-

served in the Semantic Web in the form of the famous

semantic stack, the intention of extended knowledge

graphs is to encode also dynamic knowledge in the

“logic layer” that could be related to Semantic Web’s

ontologies and rules. With use of rules and proce-

dures, we would like to be able to design and imple-

ment a dynamic knowledge based system, that should

be able to modify the knowledge if needed, rather than

only query the static part of it.

Another important assumption is that we would

like to be able to deﬁne different kinds of objects in

EKG. The objects’ classes (or categories) would de-

termine what is possible to do upon the considered

object.

Finally, we intend to express a range of tasks and

problems to solve with our system. In particular, we

would like to allow for tasks beyond simple query-

ing (SELECT-like operations), such as information

retrieval, path-ﬁnding, forward-chain and backward-

chain reasoning, hard and weak constraints (optimiza-

tion). To this end, we consider appropriate operators

and envision the transformations they would induce.

4.2 Knowledge Representation in EKG

In principle, we propose to identify three main types

of objects:

1. Agents: “active objects” that represent persons in

the system that can initiate some actions or oper-

ations over the static objects, and who can have

particular permissions over certain classes of ob-

jects and roles in the system, deﬁned by these per-

missions;

2. Objects: “passive objects” that can be acted upon

– they have methods, as in object-oriented pro-

gramming, that deﬁne the operations in which

they can participate

3. Transformations: “functional objects” that de-

ﬁne the operations to be performed with active

and passive objects; they are deﬁned by the re-

quired input and expected output, a trigger that

initiates them and the task deﬁnition that can be

expressed in multiple forms such as a process, a

set of rules and constraints or a process.

KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development

176

The transformations can be triggered by events. It is

possible to distinguish different types and subtypes of

them (the following speciﬁcation of events is inspired

by BPMN event types (OMG, 2011)):

• a message event indicates that a message is re-

ceived,

• a timer event (Rademakers and van Liempd, 2012)

usually provides some time speciﬁcation which

may denote:

– a speciﬁc time and date, e.g in the format ISO

8601, when to trigger the event,

– a time duration which sets some period of time

that must elapse before the event is triggered,

e.g. ”for 3 days”,

– a time cycle which sets time intervals to trigger

an event periodically, e.g. 5 repetitions, each

for an hour,

• a conditional event which checks if a certain con-

dition is true,

• a signal event which catches a global signal deliv-

ered to the process,

• an exception event, which in some cases may be

realized by the abovementioned events, but usu-

ally is implemented as:

– an error event in the case of occurring of a po-

tential error,

– an escalation event to escalate an exception

from the sub-processes to the parent process,

– a cancel event, used in the context of the trans-

actions and invokes compensation events to

compensate the speciﬁc tasks.

Agents, Objects and Transformations can be con-

nected with each other with links that represent rela-

tions between them.

To put it into a slightly more formal perspective,

we deﬁne an agent as:

A = hid, AT T, ROL, PRCi

where:

• id is a string identiﬁer

• AT T is a set of attributes such as for example a

name, a surname, age, nationality etc.

• ROL is a set of permissions over the Objects

• PRC is a set of procedures the agent can initiate

We deﬁne a (passive) object as:

O = hid, ATT, PRCi

where:

• id is a string identiﬁer

• AT T is a set of attributes

• PRC is a set of procedures that can be performed

over (using) the object

and a Transformation objects as:

T = hid, tr, ts, OBJi

where:

• id is a string identiﬁer

• trg is a type of trigger that initiate the transforma-

tion

• tsk is a deﬁnition of the “task” to be executed

• OBJ is a set of objects that are transformed

The EKG structure is partially based on the entity

network introduced in (Adrian and Manna, 2018) and

is a tuple

Y = hC, I, P, R, rel, proci

where:

• C is a set of concept names, denoting both agents’

and objects’ classes

• I is a set of object instances

• P is a set of procedures (transformations)

• R is a set of relation names

• rel : (I ∪C ∪ P) × (I ∪C) → 2

is a function that

associated the relation names to pairs of entities in

the network

• proc : C → 2

is a function that associate proce-

dures with classes of objects

5 REASONING WITH EKG

The language of the logic layer should cover differ-

ent forms of rules, processes as well as hard and weak

constraints. In fact, we envision that several differ-

ent “levels” of tasks could be considered, from simple

database-like operations, through more sophisticated

logical reasoning up to full-ﬂedged data analysis. To

systematically analyze and deﬁne these operations in

EKG, we propose the following categories of prob-

lems:

Level 0: CRUD (Create / Read / Update / Delete) op-

erations – these are basic transformations of the

underlying graph database

Level 1: SELECT operations, aggregation, creating

views – these are the transformations available in

classical relational databases. They “return” por-

tions of data present in the knowledge graph or the

results of simple operations such as sum or aver-

age of data.

Extended Knowledge Graphs: A Conceptual Study

177

Level 2: Deduction – these are operations that return

data inferred from those present in the knowledge

base via logical rules, possibly with use of recur-

sion, chaining, transitive closure etc.

Level 3: Finding similar objects to the ones given –

these are even higher level tasks that include an

analysis of the given examples (with use of se-

lected metrics of similarity), followed by search

mechanisms to discover relevant objects in the

knowledge base

Level 4: Induction – these are the operations that

perform generalizations from given examples to

more general assumptions about classes of ob-

jects; this level encompasses optimization prob-

lems based on data analysis, clustering and com-

parison.

Level 5: Merge – integration of knowledge from sev-

eral graphs, understood not as a simple sum, but

taking into consideration consistency analysis, in-

duction and concept alignment

When it comes to the implementation perspec-

tives, an EKG system is intended to work as a frame-

work, with conﬁgurable elements: reasoners, knowl-

edge graph databases and visualization component(s).

As for the database layer, we consider both RDF stan-

dard based on triple representation and URIs for data

identiﬁcation, and labeled property graph paradigm

in which both edges and vertices are represented with

key-value structures. While the former is the base of

Semantic Web standards and supported with differ-

ent triple stores, the latter has been adopted in widely

used graph databases such as Neo4j.

As far as reasoning layer is concerned, there are

many reasoners that could realize particular tasks out-

lined above, such as Prolog implementations for de-

duction, constraint satisfaction problems and goal-

driven inference, ASP engines (Adrian et al., 2018)

for various graph problems, data-driven reasoning as

well as constraint and optimization problems, ontol-

ogy reasoners, process mining tools etc.

6 APPLICATIONS

The extended knowledge graphs could be used in var-

ious scenarios that include processes operating over

a networked knowledge. In this section, we demon-

strate an exemplary application of EKG over knowl-

edge about researchers and their works. Let us con-

sider a part of an extended knowledge graph de-

signed for a simpliﬁed science database 1. We have

three researchers R1, R2, R3, ﬁve topics/keywords

T 1, T 2, T 3, T 4, T 5 and seven papers (P1 − P7). In

Figure 1: Exemplary extended knowledge graph about re-

searchers and their work.

Figure 2: Research interests of R3.

this knowledge graph, topics/keywords can only be

connected to papers and so are the researchers, but

researchers are also agents so they can initiate proce-

dures. In the following sections, we present possible

operations over this piece of knowledge.

6.1 Exemplary Use Cases

The most basic procedure would be recognizing in

what topic a given researcher has a strong interest. In

our case, we would deﬁne a strong interest as having

written at least 2 papers related to a topic/keyword.

So, R3 has a strong interest in T 2, T 4, T 5 (see Fig. 2),

R1 in T 4, but has also written papers on T 1, T 2 and

T 3 (see Fig. 3(a)) and R2 has a strong interest in T 2,

but has also written on T1 and T 3 (see Fig. 3(b)).

Another obvious procedure would be ﬁnding col-

laborators of a given researcher, namely researchers

that have at least one co-authored paper with a given

researcher. For instance, R1 is a collaborator of R3,

but R2 is not.

We can broaden this operation to an “Erdös

KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development

178

(a) Research interests of R1.

(b) Research interests of R2.

Figure 3: Research interests recognition.

number”-style concept for any researcher. To recall,

we deﬁne Erdös number recursively: if a researcher

has a paper with Erdös, their Erdös number is 1; if

the researcher has a paper with someone whose Erdös

number is 1, but no paper with Erdös, their Erdös

number is 2, and so on. In our case, the R3 number

for R1 is 1 and is 2 for R2.

Finally, we can put some more sophisticated de-

duction using this knowledge representation. Con-

sider a researcher who looks for people with simi-

lar research interests, but not too far away form him

with respect to collaboration. To this end, we would

look for all the researchers with at least one common

strong interest and the “researcher number” within a

given small boundary (for example 2). In our case,

the potential collaborators of R3 are both R1 and R2.

What is interesting is that while connection with R1

could be returned via simple SELECT operation, the

connection between R2 and R3 is not that obvious,

seeing that R1 and R2 have no common strong inter-

est. However, R2 has a common strong interest with

R3 and the R3-number of 2 as seen in Figure 4 which

renders them a valid candidate.

Figure 4: Looking for potential collaborators.

7 CONCLUSION

We have presented a concept of extending knowledge

graphs with procedural attachments under a com-

mon knowledge representation. We have introduced

extended knowledge graphs — a knowledge repre-

sentation and reasoning system that integrates static

knowledge from existing knowledge sources and can

perform logical reasoning in forward and backward

chaining mode.

For future work, we plan to fully implement the

idea, deﬁne new use cases and problems over existing

knowledge graphs and evaluate empirically the efﬁ-

ciency of the system. Moreover, we will continue to

add new tasks to be possible to deﬁne and execute

with the system.

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