=Paper=
{{Paper
|id=Vol-2212/paper44
|storemode=property
|title=Development of a knowledge base based on context analysis of external information resources
|pdfUrl=https://ceur-ws.org/Vol-2212/paper44.pdf
|volume=Vol-2212
|authors=Nadezhda Yarushkina,Aleksey Filippov,Vadim Moshkin
}}
==Development of a knowledge base based on context analysis of external information resources ==
https://ceur-ws.org/Vol-2212/paper44.pdf
Development of a knowledge base based on context analysis
of external information resources
N Yarushkina1, A Filippov1 and V Moshkin1
1
Ulyanovsk State Technical University, Severny Venetz str. 32, Ulyanovsk, Russia, 432027
Abstract. The article describes the process of developing a knowledge base (KB). The
content of KB is formed as a result of the analysis of the contexts of external information
resources. In this case, the context is a certain ”point of view” on the problem area (PrA) and
its features. A graph database (DB) Neo4j is used as the basis for storing the contents of the KB
in the form of an ontology. An attempt is made to implement the mechanism of inference by
the contents of a graph database. The mechanism is used to dynamically generate the screen
forms of the user interface to simplify the work with the KB. This article also describes the
method of extension of KB based on the content of the wiki-resources and relational
databases.
1. Introduction
Post-industrial society operates with huge volumes of information both in everyday and
professional activities. A large amount of information causes difficulties in making decisions
within the framework of rigid time constraints.
A variety of software automation of human activities are used to solve this problem. However,
it is necessary to adapt them to the specifics of a particular problem area (PrA) and its contexts
for the effective operation of these tools [1, 2, 7, 10, 18, 19, 20].
Thus, ”trained” automation software solves the tasks more efficiently, but they require
considerable resources (human and temporary) for training.
In this paper, an attempt is made to construct a KB. The content of the KB is an applied
ontology. The basic requirements for KB are [26]:
• adaptation to the specifics of PrA based on contexts;
• reliability and speed of ontology storage;
• the presence of a mechanism of logical inference;
• availability of tools to simplify work with the KB for unprepared users;
• availability of mechanisms for importing data from external information resources.
As you can see from Figure 1, the KB consists of the following subsystems:
(i) Ontology store:
• Neo4j [12];
• content management module;
• ontology import/export module.
(ii) Inference subsystem:
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N Yarushkina, A Filippov and V Moshkin
• inference module.
(iii) A subsystem for interaction with users:
• screen forms generation module.
(iv) A subsystem for importing data from wiki-resources:
• a module for importing data from wiki-resources.
(v) A subsystem for importing data from relational databases:
• a module for importing data from relational databases.
Figure 1. Knowledge base architecture.
2. The organization of the ontology store of KB
Ontology is a model of the representation of the PrA in the form of a semantic graph [9].
Graph-oriented database management system (Graph DBMS) Neo4j is the basis of the
ontology store for KB. Neo4j is currently one of the most popular graph databases and has
the following advantages:
(i) Having a free community version.
(ii) Native format for data storage.
(iii) One copy of Neo4j can work with graphs containing billions of nodes and relationships.
(iv) The presence of a graph-oriented query language Cypher.
(v) Availability of transaction support.
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Neo4j was chosen to store the description of the PrA in the form of an applied ontology, since
the ontology is actually a graph. In this case, it is only necessary to limit the set of nodes and
graph relations into which ontologies on RDF and OWL will be translated.
The context of an KB is some state of content of KB, obtained during versioning or building
a content of KB using different ”points of view” [6, 8].
Figure 2 shows an example of the translation of the owl representation of ontology of family
relations into the entities of the KB.
Figure 2. Example of the translation of the owl representation of ontology of family relations
into the content of the KB.
Formally, the content of the KB can be represented by the following equation:
O = hT, C Ti , I Ti , P Ti , S Ti , F Ti , RTi i, i = 1, t, (1)
where t is a number of the KB contexts,
T = {T1 , T2 , . . . , Tt } is a set of KB contexts,
C Ti = {C1Ti , C2Ti , . . . , CnTi } is a set of KB classes within the i-th context,
I Ti = {I1Ti , I2Ti , . . . , InTi } is a set of KB objects within the i-th context,
P Ti = {P1Ti , P2Ti , . . . , PnTi } is a set of KB classes properties within the i-th context,
S Ti = {S1Ti , S2Ti , . . . , SnTi } is a set of KB objects states within the i-th context,
F Ti = {F1Ti , F2Ti , . . . , FnTi } is a set of the logical rules fixed in the KB within the i-th context,
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RTi is a set of KB relations within the i-th context defined as:
Ti
RTi = {RC , RITi , RPTi , RSTi , RFTi },
Ti
where RC is a set of relations defining hierarchy of KB classes within the i-th context,
Ti
RI is a set of relations defining the ”class-object” KB tie within the i-th context,
RPTi is a set of relations defining the ”class-class property” KB tie within the i-th context,
RSTi is a set of relations defining the ”object-object state” KB tie within the i-th context,
RFTi is a set of relations generated on the basis of logical KB rules in the context of i-th context.
Principles similar to the paradigm of object-oriented programming are at the basis of the
content of the KB:
• KB classes are concepts of the PrA;
• classes can have properties, the child-class inherits properties of the parent class;
• objects of KB describe instances of the concepts of the PrO;
• specific values for the properties of objects inherited from the parent class are determined
by the states;
• logical rules are used to implement the functions of inference by the content of KB.
3. The inference on the contents of KB
The inference is the process of reasoning from the premises to the conclusion. Reasoners are
used to implement the function of inference. Reasoners form logical consequences on the basis
of many statements, facts and axioms. The most popular at the moment reasoners are [5, 17]:
• Pellet;
• FaCT++;
• Hermit;
• Racer, etc.
These reasoners are actively used in the development of intelligent software. However, Neo4j
does not assume the possibility of using similar default reasoners. Thus, there is a need to
develop a mechanism for inference based on the content of a KB [3, 4].
Currently the Semantic Web Rule Language (SWRL) is used to record logical rules [24].
These SWRL rules describe the conditions under which object a has ”nephew-uncle” relation
with object c. Formally the logical rule of the KB is:
F Ti = hAT ree , ASW RL , ACypher i,
where Ti is the i-th context of the the KB, AT ree is the tree-like representation of a logical rule
F Ti , ASW RL is the SWRL representation of the logical rule F Ti , ACypher is the Cypher
representation of the logical rule F Ti .
The tree-view AT ree of a logical rule F Ti is:
AT ree = hAnt, Consi,
where Ant = Ant1 ΘAnt2 Θ . . . ΘAntn is the antecedent (condition) of the logical rule F Ti ;
Θ ∈ {AN D, OR} is a set of permissible logical operations between antecedent atoms;
Cons is the consequent (consequence) of a logical rule F Ti .
Figure 3 shows an example of a tree-like representation of two logical rules for the ontology
of family relations. That rules describes the father-child relationships.
The tree-like logical rule is translated into the following SWRL:
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hasFather(?a,?b) => hasChild(?b,?a)
hasSister(?c,?a) & hasFather(?c,?b) => hasChild(?b,?a)
and the following Cypher view:
MATCH (s1:Statement{name: "hasChild", lr: true})
MATCH (r1a)<-[:Domain]-(:Statement{name:"hasFather"})-[:Range]->(r1b)
MERGE (r1b)-[:Domain]->(s1)
MERGE (r1a)-[:Range]->(s1)
MATCH (s1:Statement{name: "hasChild", lr: true})
MATCH (r2c)<-[:Domain]-(:Statement{name:"hasSister"})-[:Range]->(r2a)
MATCH (r2c)<-[:Domain]-(:Statement{name:"hasFather"})-[:Range]->(r2b)
MERGE (r2b)-[:Domain]->(s1)
MERGE (r2a)-[:Range]->(s1)
Figure 3. Example of a tree-like representation of a logical rule.
Thus, the rules are translated into their tree-view when imported into the KB of logical rules
in the SWRL language.
The presence of a tree-like representation of a logical rule allows to form both a SWRL-
representation of a logical rule and a Cypher-representation based on it.
Relations of a special type are formed by using Cypher to represent the logical rule between
entities of the KB. Figure 4 shows the content of KB after executing the Cypher queries that
were built for the logical rule shown in Figure 3. These relations correspond to the antecedent
atoms of the logical rule. Formed relationships provide the inference from the contents of the
KB.
4. Building a Graphical User Interface based on the contents of a KB
The dynamic graphical user interface (GUI) mechanism is used to simplify the work with KB
of untrained users and control of user input [11, 13, 21].
You need to map the KB entities to the GUI elements to build a GUI based on the contents
of the KB. Formally, the GUI model can be represented as follows:
U I = hL, C, I, P, Si, (2)
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where L = {L1, L2, . . . , Ln} is a set of graphical GUI components (for example, ListBox,
TextBox, ComboBox, etc.), C = {C1, C2, . . . , Cn} is a set of KB classes, I = {I1, I2, . . . , In} is a
set of KB objects, P = {P1, P2, . . . , Pn} is a set of properties of KB classes, S = {S1, S2, . . . , Sn}
is a set of states of KB objects.
Figure 4. The result of executing Cypher queries for logical rule.
The following function is used to build a GUI based on content of KB:
φ (O) : {C O , I O , P O , S O , F O , RO }Ti → {LU I , C U I , I U I , P U I , S U I },
where {C O , I O , P O , S O , F O , RO }Ti is a set of entities of KB represented by expression 1 within
the i-th context;
{LU I , C U I , I U I , P U I , S U I } is a set of GUI entities of KB represented by the expression 2.
Thus, the contents of the KB are mapped to set of GUI components. This makes it easier
to work with KB for a user who does not have skills in ontological analysis and knowledge
engineering. It also allows you to monitor the logical integrity of the user input, which leads to
a reduction in the number of potential input errors.
5. Extracting knowledge from wiki-resources
At present, wiki-technologies are used to organize corporate KB. It is necessary to solve the task
of knowledge extracting from wiki-resources [14, 15, 16, 23, 27]. Table 1 contains the result of
mapping the KB entities to the wiki-resource entities [22]. Thus, it becomes possible to import the
structure of external wiki resources for initial filling of the KB contents.
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Table 1. The correspondence between the wiki-resource entities and the entities of KB.
The entities of knowledge base The entities of wiki-resource
Class Category
Subclass Subcategory
Object Page
Class properties The infobox elements (properties)
Object states The infobox elements (values)
Relations Hyperlinks
Also, a content of KB can be built on the basis of an analysis of the content of wiki-resources
pages. In this work the Syntaxnet [22] framework to construct a syntactic tree Synt of content
of wiki-resources pages is used. Further, using a set of rules RuleSynt , a syntax tree Synt is
translated into entities of KB.
Formally the functions of translating a syntactic tree into entities of KB:
φStruct (Synt) : {NSynt , RuleStruct O O O Ti
Synt } → {C , P , RP } ,
φContent (Synt) : {NSynt , RuleContent
Synt } → {I O , S O , RIO , RSO }Ti ,
where NSynt is a set of nodes of the syntactic tree Synt,
RuleStruct
Synt is a set of rules to translating nodes of syntactic into structure entities of the KB,
RuleContent
Synt is a set of rules to translating nodes of syntactic into content entities of the KB,
{C , P , RP }Ti is a set of structure entities of the KB within the context Ti (eq. 1),
O O O
{I O , S O , RIO , RSO }Ti is a set of content entities of the KB within the context Ti (eq. 1).
Formally the rules to translating nodes of syntactic into entities of the KB:
Synt
RuleStruct
Synt = N1 , N2Synt , . . . , NiSynt , . . . , NnSynt → {C O , P O , RPO },
RuleContent
Synt = N1Synt , N2Synt , . . . , NiSynt , . . . , Nm
Synt
→ {I O , S O , RIO , RSO },
where NiSynt is the i-th node of syntactic tree.
Thus, it becomes possible to extract knowledge from the structure of wiki-resource and
contents of wiki-resource pages and present the extracted knowledge as a content of KB.
6. Extracting knowledge from relational databases
Relational databases are widely used for data storing and contains subject area description in
the form of interconnected tables. Nowadays, researchers of various scientific groups are involved
in solving the problem of extracting knowledge from relational databases.
The relational data model can be represented as the following expression:
RDM = (E, R) ,
where E = {E1 , E2 , . . . , En is a set of database tables (entities),
R = {R1 , R2 , . . . , Ri , . . . Rn is a set of relationships between database tables:
F (x)
Ri = Ej Ek ,
G (x)
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where Ej , Ek are database entities;
F (x) is the relationship between entity Ej and entity Ek ,
G (x) is the relationship between entity Ek and entity Ej .
Scope of functions F (x) and G (x) are U – single relationship and N – multiply relationship.
For mapping of relational database structure with KB structure special functions are used:
φStruct (RDM ) : {E RDM , RRDM } → {C O , P O , RPO }Ti ,
φContent (RDM ) : {E RDM , RRDM } → {I O , S O , RIO , RSO }Ti ,
where {E RDM , RRDM } is a set of entities of relational database and relationships between them,
{C O , P O , RPO }Ti is a set of structure entities of the KB within the context Ti (eq. 1),
{I O , S O , RIO , RSO }Ti is a set of content entities of the KB within the context Ti (eq. 1).
Importing data from a relational database to the KB were finish after mapping the structure
of the relational database to the set of structure entities {C O , P O , RPO }Ti of the KB ends. Set of
content entities of KB {I O , S O , RIO , RSO }Ti are created during the import of data basis from the
relational database (row set) to the Ti context. Table 2 contains a comparison of KB entities
with relational database entities.
Table 2. The correspondence between the relational database entities and the entities of KB
The entity of knowledge base The entity of relational database
Class Table
Object Table row
Class properties and Relations Relations between tables, table columns
Object states Content of cells
Thus, it becomes possible to extract knowledge from the contents of relational databases and
present the extracted knowledge as a content of KB.
7. Conclusion
Thus, the use of KB stored in the Graph DBMS in the decision support process presupposes
the existence of a certain set of mechanisms:
• organization of inference on the content of KB by translating SWRL-rules into Cypher-
structures;
• building a graphical user interface based on the contents of KB;
• automated import of knowledge from structure and content of wiki-resources;
• automated import of knowledge from relational databases.
These mechanisms allow to automate the learning process of KB and simplify the work of
specialists with KB. The application of a contextual approach to the storage of knowledge raises
the effectiveness of the use of subject ontologies, allowing to adapt the KB to the characteristics
of the PrA and to the requirements of specialists. This approach provides them with a tool that
is convenient in a software dynamically changeable depending on the contents of the KB.
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Acknowledgments
This work was financially supported by the Russian Foundation for Basic Research (Grant No.
16-47-732054).
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