=Paper=
{{Paper
|id=Vol-1625/paper2
|storemode=property
|title=Framework for Conceptual Modeling on Natural Language Texts
|pdfUrl=https://ceur-ws.org/Vol-1625/paper2.pdf
|volume=Vol-1625
|authors=Mikhail Bogatyrev,Kirill Samodurov
|dblpUrl=https://dblp.org/rec/conf/cla/BogatyrevS16
}}
==Framework for Conceptual Modeling on Natural Language Texts==
https://ceur-ws.org/Vol-1625/paper2.pdf
Framework for Conceptual Modeling on Natural
Language Texts
Mikhail Bogatyrev, Kirill Samodurov
Tula State University, Tula, Russia
okkambo@mail.ru, zmeymc@gmail.com
Abstract. The paper presents the framework for conceptual modeling which
has been used in on-going project of developing fact extraction technology on
textual data. The modeling technique combines the usage of conceptual graphs
and Formal Concept Analysis. Conceptual graphs serve as semantic models of
text sentences and the data source for formal context of concept lattice. Several
ways of creating formal contexts on a set of conceptual graphs have been
investigated and resulting solution is proposed. It is based on the analysis of the
use cases of semantic roles applied in conceptual graphs and their structural
patterns. Concept lattice building on textual data is interpreted as storage of
facts which can be extracted by using navigation in the lattice and interpretation
its concepts and hierarchical links between them. Experimental investigation of
the modeling technique was performed on the annotated textual corpus
consisted of descriptions of biotopes of bacteria.
Keywords: conceptual modeling, conceptual graphs, concept lattice, biotopes
of bacteria.
1 Introduction
Conceptual modeling in the Natural Language Processing (NLP) is a way of modeling
semantics. Semantics of texts is transformed to semantics of conceptual models at a
high level of abstraction, in terms of concepts. Conceptual graphs (CGs) [22]
represent a well-known type of conceptual models and there are some applications of
them in Text Mining problems solutions [13, 15].
Another paradigm of conceptual modeling is Formal Concept Analysis [10]. It is a
mathematical theory of data analysis which studies how objects can be hierarchically
grouped together according to their common attributes. Strong mathematical
background of FCA (it is based on the lattice theory [2] and uses matrix model of so
named “formal context”) provides its implementations as rigorous instrument for
14 Mikhail Bogatyrev, Kirill Samodurov
Information Retrieval (IR). The number of FCA applications now is growing up
including applications in Text Mining and linguistics [6, 19]. It is also applied in more
general field of knowledge processing [17].
The idea of joining two paradigms of conceptual modeling - conceptual graphs and
concept lattices - seems very attractive but not elaborated in the FCA community.
There are several its realizations due the years, from early implementation in [23] up
to recent investigations in [9].
This idea may get a second breath when FCA is utilized on textual data and
conceptual graphs serve as conceptual model of text semantics. Acquiring conceptual
graphs from natural language texts is non-trivial problem but it is quite solvable [5,
14]. The concepts of conceptual graphs may be treated as objects and attributes for
formal context as far as the “attribute” conceptual relation really exists in conceptual
graphs acquired from natural language texts. Actually, as it is followed from our
investigations, the “attribute” relation is not always good and even enough for formal
context. Except the “attribute” conceptual relation some other relations must be
analyzed in conceptual graphs to find objects and attributes needed for formal context.
The main problem which arises in CGs – FCA applications is the problem of
building formal concepts on conceptual graphs. Solution of this problem and the
whole principle of applying FCA on textual data are closely depended on the real-life
problems have been solved with FCA on textual data [12, 16]. In the sense of
Information Retrieval these problems may be generalized to the fact extraction
problem. Using FCA in its solution is based on that concept lattice built on textual
data may be interpreted as storage of facts which can be extracted by using navigation
in the lattice and interpretation its concepts and hierarchical links between them.
One of the fields where Text Mining applications are growing rapidly is
Bioinformatics. New term of Biomedical Natural Language Processing (BioNLP) has
been appeared there [1]. This is stipulated by huge amount of scientific publications
in Bioinformatics and organizing them into corpora with access to the full texts of
articles. FCA has great potential to take up a challenge from such areas as BioNLP.
In this paper we present the framework for conceptual modeling which has been
used in on-going project of developing fact extraction technology on textual data.
The next section of the paper contains brief description of FCA basics and
conceptual modeling technique which is used in the framework.
Section 3 is devoted to the framework; its structure and functionality are described
there.
In the section 4 current experimental results of using framework on bacteria
biotope textual corpus are presented and section 5 contains conclusion and planning
future works.
2 CGs – FCA modeling on natural language texts
We are developing conceptual modeling technique which combines the usage of
conceptual graphs and conceptual lattices from Formal Concept Analysis. Consider
some FCA basics needed for understanding the modeling technique.
Framework for Conceptual Modeling on Natural Language Texts 15
2.1 Formal Concept Analysis basics
There are two basic notions FCA deals with: formal context and concept lattice.
Formal context is a triple K = (G, M , I ) , where G is a set of objects, M – set of their
attributes, I ⊆ G × M – binary relation which represents facts of belonging attributes
to objects. The sets G and M are partially ordered by relations ф and – ,
correspondingly: G = (G, ф ) , M = ( M , – ) . Formal context may be represented by
[0, 1] - matrix K = {ki , j } in which units mark correspondence between objects
gi ∈ G and attributes m j ∈ M . The concepts in the formal context have been
determined by the following way. If for subsets of objects A ⊆ G and attributes
B ⊆ M there are exist mappings (which may be functions also) Aʹ : A → B and
Bʹ : B → A 1 with properties of Aʹ : = {∃m ∈ M | < g , m >∈ I ∀ g ∈ A} and
Bʹ : = {∃g ∈ G | < g , m >∈ I ∀ m ∈ B} then the pair (A, B) that Aʹ = B, Bʹ = A is
named as formal concept. The sets A and B are closed by composition of mappings:
A '' = A, B '' = B ; A and B is called the extent and the intent of a formal context
K = (G, M , I ) respectively.
A formal concept is a pair (A, B) of subsets of objects and attributes which are
connected so that every object in A has every attribute in B, for every object in G that
is not in A, there is an attribute in B that the object does not have and for every
attribute in M that is not in B, there is an object in A that does not have that attribute.
The partial orders established by relations ф and – on the set G and M induce a
partial order ≤ on the set of formal concepts. If for formal concepts (A1, B1) and (A2,
B2), A1 ф A 2 and B2 – B1 then (A1, B1) ≤ (A2, B2) and formal concept (A1, B1) is
less general than (A2, B2). This order is represented by concept lattice. A lattice
consists of a partially ordered set in which every two elements have a unique
supremum (also called a least upper bound or join) and a unique infimum (also called
a greatest lower bound or meet).
According to the central theorem of FCA [10], a collection of all formal concepts in
the context K = (G, M , I ) with subconcept-superconcept ordering ≤ constitutes the
concept lattice of K . Its concepts are subsets of objects and attributes connected each
other by mappings Aʹ , Bʹ and ordered by a subconcept-superconcept relation.
Although that level of abstraction makes FCA suitable for use with data of any nature,
its application to specific data often requires special investigation. It is fully relevant
for using FCA with textual data.
1)
More rigorous definition assumes that these mappings are different: ϕ : A → B ,ψ : B → A
but it is not a matter of principle here.
16 Mikhail Bogatyrev, Kirill Samodurov
2.2 FCA on textual data
The main problem in applying FCA on textual data is the problem of building formal
context. If textual data is represented as natural language texts then this problem
becomes especially important.
There are several approaches to the construction of formal contexts on the textual
data, presented as separate documents, as data corpora. One, mostly applied variant
of context is that its objects are text documents and the attributes are the terms in
these documents [6, 7]. The main problem which can be solved with that formal con-
text and concept lattice is the problem of retrieving textual documents.
Another variant of formal context is building directly on the texts. In the general
case, various word combinations constitute its concepts and the number of such con-
cepts may be very large. An advantage of such variant is that this context contains
potentially more information about texts than previous one and more general prob-
lems such as fact extraction problem can be solved on that formal context. The disad-
vantage of it is its great dimension and possible many pointless concepts.
Restricting the dimension of formal context and giving it more semantics is doing
by representing in it the various features of its source texts: semantic relations (syn-
onymy, hyponymy, hypernymy) in a set of words for semantic matching [12], verb-
object dependencies from texts [7], words and their lexico-syntactic contexts [16].
For building formal context, one needs to distinguish some of these lexical
elements in texts as objects and attributes. There are following approaches to solve
this problem:
• adding special descriptions to texts which mark objects and attributes and partial
order – this is usually done manually;
• using semantic models of texts and corpus tagging [7].
We apply the second approach and use conceptual graphs for representing
semantics of individual sentences of a text.
2.3 CGs – FCA modeling process
The whole process of CGs – FCA modeling has the following steps.
1. Acquiring a set of conceptual graphs from input texts. Conceptual graph [22] is
bipartite directed graph having two types of vertices: concepts and conceptual
relations. These vertices are connected by arrows representing binary relations.
Conceptual graphs can be created by our tool CGs Maker 2. Some details about it can
be found in [13, 14].
2. Aggregating the set of conceptual graphs. Aggregation is needed to exclude
excessive dimension of conceptual models, not related to useful information. We have
tested two ways of conceptual graphs aggregation: conceptual graphs clustering and
restricting the number of conceptual graphs by identifying and excluding sentences
which are not corresponded to the problem solving with the current technique.
2
The lightweight online version of CGs Maker for simple English and Russian texts can be
found at http://85.142.138.156:8888 .
Framework for Conceptual Modeling on Natural Language Texts 17
3. Creating formal contexts. One or several formal contexts are built on the
aggregated conceptual graphs. The number of formal concepts and the method of
building them have been determined in the solving problem.
4. Building concept lattice. Having a concept lattice, it is possible to identify
connections between the concepts according to the principle of "common –
particular". Each concept, the node in the lattice is interpreted as the set of potential
facts of certain level, which is associated with other facts.
5. Fact extraction from concept lattice. Concept lattice is the data storage for fact
extraction system. This system has domain oriented user interface for query
processing and generating output.
This paper reflects results of investigations corresponded to steps 1-3 of the
process. On the step 4 we used standard open source tool for building and visualizing
concept lattices [8] which we integrated into the whole modeling system. Creating the
fact extraction system (step 5) is separate problem currently being under
development.
2.4 Usage of conceptual graphs
The crucial step in the described process of CGs – FCA modeling is creating
formal contexts on the set of conceptual graphs. At first glance, this problem has
simple solution: those concepts which are connected by "attribute" relation have been
put into formal context as its objects and attributes. Actually the solution is much
more complex. To illustrate it consider conceptual graph for the sentence “Xylella
fastidiosa is a gram-negative fastidious, xylem-limited bacterium” shown on Fig. 1.
This sentence is from bacteria biotopes textual corpus [4] which we use for our
method evaluation.
Fig. 1. Conceptual graph for the sentence “Xylella fastidiosa is a gram-negative fastidious,
xylem-limited bacterium.”
18 Mikhail Bogatyrev, Kirill Samodurov
Conceptual graph on the Fig.1 has four conceptual relations “attribute” but only
three of them indicate real objects and attributes for formal context. Using
“fastidiosa” as object and “Xylella” as attribute in the formal context is wrong way
because “Xylella fastidiosa” is known full name of this bacterium. Full names of
bacteria have to be objects in the formal context devoted to bacteria. Word
combinations denoting the names of bacteria must be recognized before conceptual
graphs building. There is no other way of doing this than to use an external source of
information, for example, the corpus tagging.
We also realize the following rules for creating formal contexts on conceptual
graphs.
1. Not only individual concepts and relations, but also patterns of connections
between concepts in conceptual graphs represented as subgraphs have been
analyzed and processed. The pattern “agent - patient” is mostly frequent in biotope
texts.
2. The hierarchy of conceptual relations in conceptual graphs is fixed and taken into
account when creating formal context. Such hierarchy exists on the Fig.1: relations
“agent” and “patient” are on the top level and relations "attribute" belong to
underlying level. Using this hierarchy of conceptual relations we can select for
formal contexts more or less details from conceptual graphs. This makes
conceptual graphs more power and flexible semantic model for FCA than n-grams
or collocations.
3. FCA – model for fact extraction is domain specific. Domain information is also
taken into account in conceptual graphs building. This information is from external
resources – thesauruses or tagging of textual corpuses.
Concrete implementations of these rules are in the section 4.
3 Architecture and Functionality of the Framework
Architecture of the CGs – FCA modeling framework is shown on the Fig. 2. Consider
its main elements.
Database. Database is very important part of the framework. We use relational
database on the SAP-Sybase platform. It was built with CASE technology
PowerDesigner™ [18] and may be scaled and expanded. Database stores texts,
conceptual graphs, formal contexts and concept lattices. Special indexing is applied to
textual data.
Conceptual graphs building module. This module and several other modules
constitute the NLP block of modules of the framework. They realize our algorithm of
acquiring conceptual graphs from texts, visualization of conceptual graphs and their
clusters, interaction with external resources including WordNet.
English and Russian languages have been supported in the framework. The
framework has internal dictionaries and may communicate with external ones.
Representing of modeling results. Modeling results have been presented as
visualization of conceptual graphs and concept lattices as in table and textual forms.
Storing all objects in database allows analyzing its data and computing conceptual
graphs and concept lattice characteristics.
Framework for Conceptual Modeling on Natural Language Texts 19
Programming environment. Java is the main programming platform which is used
in the framework. Some modules of NLP block have been written on PowerScript
language of SAP-Sybase platform.
Fig. 2. Architecture of the framework
4 Experiments and Results
Experimental evaluation of CGs – FCA modeling technique has been carried out
on the textual corpus of bacteria biotopes which is used in the innovation named as
BioNLP Shared Task [4]. This innovation includes three IR tasks: the Bacteria Gene
Renaming, the Bacteria Gene Interaction and the Bacteria Biotopes. The Bacteria
Biotope task is formulated as consisting of two standard Text Mining tasks of Named
Entity Recognition (NER) and Relations Extraction (RE) [20].
Biotope is an area of uniform environmental conditions providing a living place for
plants, animals or any living organism. According to [4] there are two types of entities
to be extracted: the names of bacteria and their locations. We added third entity of
pathogenicity of bacteria.
20 Mikhail Bogatyrev, Kirill Samodurov
It is preliminarily clear that the task of extracting the names of bacteria and the task
of extracting locations and pathogenicity have different complexities. For extracting
the names of bacteria some words or collocations (Xylella fastidiosa) have to be
analyzed in the text. Locations and pathogenicity may be represented by more
complex and long word combinations. As for bacterium Xylella fastidiosa on the Fig.
1, the example of its location is the following fragment from the text about it: “the
bacteria … receive a safe environment and metabolites from the insect”. To extract
“insect” as location of bacterium we need to analyze some relations between words in
the sentence. This is done also through the use of conceptual graphs.
Biotope texts tagging includes full names of bacteria, its abbreviated names and
unified key codes in the database. We add additional tags if special words (extreme,
obligately, etc.) recognized in the texts.
A BioNLP data is always domain-specific. All the texts in the corpus [4] are about
bacteria themselves, their areal and pathogenicity. Not every text contains these three
topics but if some of them are in the text then they are presented as separate text
fragments. This simplifies text processing. According to these three topics of interest
we construct three different formal contexts of “Entity”, “Areal” and “Pathogenicity”.
They engender three different concept lattices which are connected each other. To
join lattices we use facet technology [19].
Our solution of the task of Named Entity Recognition is supported by conceptual
graphs. As it is illustrated above (Fig. 1) conceptual graphs can represent names of
bacteria as named entities. Named Entity Recognition also includes anaphora
resolution.
4.1 Anaphora resolution and noise reduction
Anaphora resolution is the problem of resolving references to earlier or later items in
the text. These items are usually noun phrases representing objects called referents but
can also be verb phrases, whole sentences or paragraphs. Anaphora resolution is the
standard problem in NLP.
Biotope texts we work with contain several types of anaphora:
• hyperonym definite expressions (“bacterium” - “organism”, “cell” - “bacterium”),
• higher level taxa often preceded by a demonstrative determinant (“this bacteria”,
“this organism”),
• sortal anaphors (“genus”, “species”, “strain”).
For anaphora detection and resolution we use a pattern-based approach. It is based
on fixing anaphora items in texts and establishing relations between these items and
the objects in conceptual models we use. These objects are bacteria names for
“Entity” context, mentions of water, soil and other environment parameters for
“Areal” context and names and characteristics of diseases for “Pathogenicity” context.
Corpus tagging is also used for anaphora detection. In particular encoding bacteria
(for instance bacterium Burkholderia phytofirmans is encoded as PsJN) is found from
tagging and further used as its name in text processing.
Noise is constituted by the text elements that contain no facts or cannot be
interpreted as facts. Also noise consider the data that are deliberately excluded from
Framework for Conceptual Modeling on Natural Language Texts 21
consideration, for example, information about when and by whom a bacterium was
first identified.
4.2 Data Processing
We have selected 130 mostly known bacteria and processed corresponding corpus
texts about them. Three formal contexts of “Entity”, “Areal” and “Pathogenicity” had
built on the texts. They have the names of bacteria as objects and corresponding
concepts from conceptual graphs as attributes.
Table 1 shows numerical characteristics of created contexts.
Context name Number of Number of Number of
objects attributes formal concepts
Entity 130 26 426
Areal 130 18 127
Pathogenicity 130 28 692
Table 1. Numerical characteristics of created contexts
As it is followed from the table there is relatively small number of formal concepts
in the contexts. This is due to the sparse form of all contexts generated by conceptual
graphs and noise reduction.
4.3 Fact extraction
Extracting facts from concept lattices is realized by forming special views
constructed on the lattice and corresponded to certain property (intent in the lattice) or
entity (extent in the lattice) on the set of bacteria. Every view is a sub lattice. It shows
the links between concrete bacterium and its properties.
An example of such view as the fragment of lattice is shown on Fig. 3. The lattice
on the Fig. 3 contains formal concepts related to the following bacteria: Borrelia
turicatae, Frankia, Legionella, Clamydophila, Thermoanaerobacter tengcongensis,
Xanthomonas oryzae. Highlighted view on the figure corresponds to gram-negative
property of bacteria. Such bacteria are resistant to conventional antibiotics.
Using this view, some facts about bacteria can be extracted:
• only three bacteria from the set, Thermoanaerobacter tengcongensis,
Clamydophila and Xanthomonas oryzae, are gram-negative;
• two gram-negative bacteria, Thermoanaerobacter tengcongensis and Xanthomonas
oryzae, have the shape as rod;
• one of gram-negative bacteria, Clamydophila, is obligately pathogenic.
22 Mikhail Bogatyrev, Kirill Samodurov
Note that attribute obligately pathogenic was formed directly from the same two
words in the text according to the rule of marking words denoting extreme situation.
Fig. 3. Example of view concerned gram-negative property of bacteria.
We can compare our results with the known ones, most completely presented in the
work [20]. Although we use the same corpus and some resembling methods (they use
pattern-based approach and domain lexical resources) our results are different in fact.
Our main result is not certain words extracted from texts as solution of NER and RE
tasks but the whole information resource of concept lattice which is similar to
ontology. So we resume that CGs – FCA modeling provides solving wider set of tasks
than Named Entity Recognition and Relations Extraction, the set which corresponds
to fact extraction problem.
5 Conclusion and Future Work
This paper describes the first but very important stage of creating environment for
performing experiments of CGs – FCA modeling in the project of creating fact
extraction technology on natural language texts. Some parts of this project are under
construction but current results demonstrate effectiveness of CGs – FCA modeling.
Conceptual graphs were recognized as valid low level conceptual model for
creating high level such model of concept lattice. Using conceptual graphs, it is
possible to control semantic depth of representing sentences in formal concepts by
selecting certain levels (sub graphs) of graph structure.
Among the topics of our future work there are the following.
Now the verb-centric approach which we use in acquiring conceptual graphs is not
fully applied for creating formal contexts. When conceptual graph has the pattern
- (agent) – – (patient) - the verb serves as condition
which links two concepts. In other patterns with other conceptual relations including
attribute verbs play the same role. This opens the need to construct tricontexts on
conceptual graphs. We plan to construct multidimensional data model on our database
under SAP PowerDesigner™ CASE technology and apply OLAP for modeling
tricontexts and triclusters.
Framework for Conceptual Modeling on Natural Language Texts 23
We also plan to use SAP HANA Environment [21] for work with big textual data.
Acknowledgments. The paper concerns the work which is partially supported by
Russian Foundation of Basic Research, grant № 15-07-05507.
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