International Journal of Intelligence Science, 2011, 1, 35-45
doi:10.4236/ijis.2011.12005 Published Online October 2011 (
Copyright © 2011 SciRes. IJIS
A New Algorithm for the Acquisition of Knowledge from
Scientific Literature in Specific Fields Based on Natural
Language Comprehension
Hui Wei, Zhilong Dai
Department of Computer Science and Engineering, Fudan University, Shanghai, Chin a
Received September 11, 2011; revised October 1, 2011; accepted October 8, 2011
The acquisition of knowledge and the representation of that acquisition have always been viewed as the bot-
tleneck in the construction of knowledge-based systems. The traditional methods of acquiring knowledge are
based on knowledge engineering and communication with field experts. However, these methods cannot
produce systematic knowledge effectively, automatically construct knowledge-based systems, or benefit
knowledge reasoning. It has been noted that, in specific professional fields, experts often use fixed patterns
to describe their expertise in the scientific articles that they publish. Abstracts and conclusions, for example,
are key components of the scientific article, containing abundant field knowledge. This paper suggests a
method of acquiring production rules from the abstracts and conclusions of scientific articles in specific
fields based on natural language comprehension. First, the causal statements in article abstracts and conclu-
sions are extracted using existing techniques, such as text mining. Next, antecedence and consequence frag-
ments are extracted using causal template matching algorithms. As the final step, part-of-speech-tagging
production rules are automatically generated according to a syntax parsing tree from the speech pair se-
quence. Experiments show that this system not only improves the efficiency of knowledge acquisition but
also simultaneously generates systematic knowledge and guarantees the accuracy of acquired knowledge.
Keywords: Knowledge Acquisition, Knowledge Representation, Text Mining, Production Rules
1. Introduction
Knowledge acquisition (KA) has long been perceived as
the most difficult bottleneck in the construction of know-
ledge-based systems (KBS). Over the past decade, know-
ledge engineers have argued over the best means of con-
structing an effective and reliable KBS. Many research-
ers view knowledge acquisition as critical [1-5]. For
example, Edward Albert Feigenbaum once said, “There
are many important problems to be solved in the use,
representation, and acquisition of the knowledge. Of
them all, the knowledge acquisition is the most important
and critical bottleneck” [6]. It is imperative to build
automated knowledge acquisition systems.
In specific professional fields, conclusive knowledge
and experience are customarily represented concisely in
normalized scientific language when stored in text form.
Consequently, massive amounts of specialized field
knowledge can be obtained from scientific articles. This
can then be used to build knowledge-based systems.
From the perspective of knowledge engineering, state-
ments in scientific articles are usually verified, explicit,
and expressed in a conclusive and conceptive style. All
of these traits closely resemble the requirements of KBS.
Additionally, in scientific articles in specific professional
fields, these statements are often written in a relatively
fixed style that makes them easier to extract.
The abstract and conclusion sections of most scientific
articles contain useful field knowledge. Labeling the
summary statements in abstracts and conclusions, then,
may greatly improve the effectiveness and accuracy of
knowledge acquisition. These statements tend to have
significant causal relationships, which makes them easier
to represent using automatic production rules and easier
to translate into field knowledge that can be used by
KBS. The implementation of this idea is based on the
following proven scientific practices:
1) There already exist many NLU and text mining
techniques that can be used to extract knowledge en
masse from scientific articles. The issue for knowledge
engineers is how to formalize the explicit knowledge in
these articles and the difficulty is how to achieve auto-
matic conversion. Existing data mining and analysis
techniques, such as text retrieval, text association analy-
sis, and so on, provide effective algorithms to implement
the transformation [7].
2) The material processed in this paper focuses on a
specific scientific field. In addition, the statements in
abstracts and conclusions are often short, conclusive, and
designed to focus on a single topic. Therefore, priori
knowledge can be used as a guide during the text mining
process to improve effectiveness of KA. In the experi-
ments described in this paper, the range of both field
knowledge and topic has been limited in order to im-
prove accuracy.
3) The production rule is a mature method of know-
ledge representation with strong expression ability. It is
easily combined with the Drools engine mechanism that
is used as inference device in the KBS proposed in this
The method described in this paper takes texts that
contain abundant field knowledge, such as professional
papers, as input. After labeling, casual statements are
then transformed into production rules that can be exe-
cuted by machine. Transformation between text know-
ledge and production rules can be achieved automatically
through necessary and effective manual interventions,
which improve the degree of typical KA automation and
realize computer-aided KA.
2. Related Work
As the data show, knowledge acquisition techniques are
optimized toward three main goals:
1) To improve the efficiency of knowledge acquisition,
which means acquiring knowledge from field experts
effectively or adopting semi-automatic knowledge ac-
quisition methods.
2) To extend the scope of knowledge that can be ac-
cessed and improve the automation of knowledge acqui-
3) To simplify the process of conversion from ac-
quired knowledge to production rules that can be exe-
cuted by machines.
As far as improving KA efficiency is concerned, re-
cent studies have mainly focused on two issues. First,
there has been the development of methods and assistant
tools that shorten the communication cycle with field
experts and guarantee the accuracy of acquired know-
ledge. These include the famous Repertory Grid method
of delimiting and identifying field objects, the integrated
model of KA, the MRM method, and the KADs model-
based method, a comprehensive methodology for KA
from multiple knowledge sources [8-12]. Second, there
has been the development of techniques that improve KA
automation and shorten the acquisition cycle [13-16].
These include pattern recognition, machine learning, and
text mining techniques, such as the automatic KA me-
thod based on inductive learning, the incremental ap-
proach to discovering knowledge from text, and know-
ledge discovery [17-19]. All of these methods have their
own inevitable disadvantages. KA methods that use pat-
tern reorganization or machine learning focus primarily
on implicit rules contained in mass data and are suitable
only for processing data text [20]. The rules obtained are
still untested and have to be verified by field experts. KA
methods based on text mining are often designed to
process large amounts of text and the knowledge ob-
tained is generally inadequate. Additionally, they are
always designed to investigate objects and object hierar-
chies [21,22]. There is great gap between the knowledge
rules and production rules used in KBS.
Compared to traditional KA methods, the method
suggested in this paper processes causal statements fo-
cused on one specific field. The algorithm proposed in
this paper, natural language comprehension for rule ex-
traction (NLCRE), is designed to obtain IF-THEN rules
from scientific articles by labeling the causal statements
in those, extracting antecedence and consequence using
causal templates, and generating rules automatically us-
ing a syntax parsing tree.
3. Architecture of Knowledge Based System
Based on NLCRE
Using the NLCRE algorithm, we developed a new know-
ledge-based system with a knowledge base that can ex-
pand incrementally. The overall architecture of the KBS
is shown in Figure 1. The KBS architecture is comprised
of four modules:
1) Causal-statement-finding module (white)
The function of the causal-statement-finding module is
to extract causal statements from the abstracts and con-
clusions of scientific articles.
2) Production-rule-generation module (gray)
This module is responsible for generating rules from
causal statements. Pre-definition templates are con-
structed based on the characters of causal statements.
The antecedence and consequence portions of production
rules can be obtained by using templates matching algo-
rithms. Then production rules are then generated by the
part-of-speech-tagging and syntax-parsing tree based on
natural language comprehension. After being filtered and
refined manually, these production rules are added to the
Copyright © 2011 SciRes. IJIS
Copyright © 2011 SciRes. IJIS
Text analysis
Fill algorithm
rule type
Knowledge module,
production rule system
Text input device Atom operation
(predication, function
Specialized format
language table
Inference device
Pseudo production rule
Manual filter
and refine
Sub-field set KBS
Production syntax rules
Figure 1. Archit ecture of K nowledge-Based Sy stem Based on NL CRE.
4. NLCRE Production Rule Generation
3) Field knowledge management module (purple)
Production rules generated from the scientific articles
or acquired from field experts are stored and managed in
the field KBS. Knowledge management includes know-
ledge update, knowledge addition, knowledge deletion,
and other processes. In order to eliminate redundancy
and conflict, these operations must be verified before
they are performed.
The presentation of field knowledge contained in the
natural language of scientific papers is quite different
from the requirements of knowledge representation in
KBS. Translating natural language to formal language
requires an essential change translates labeled text into
production rules. This change benefits the process of
forming clear knowledge hierarchies and structures in
knowledge systems. Furthermore, it improves the exten-
sibility of knowledge and provides an effective means of
increasing the efficiency of reasoning.
4) Inference device (green)
Knowledge reasoning must be considered when de-
signing a KBS. In this paper, the Drools java rules en-
gine is the main tool employed toward knowledge rea-
soning. Drools is an open-source business rule engine
and an enhanced java language implementation system
based on the RETE algorithm [23].
The NLCRE algorithm can be divided into four stages,
as shown in Figure 2. These stages will now be des-
cribed in detail.
In addition to traditional functions such as knowledge
storage, management, and reasoning, the KBS presented
in this paper can also make use of production rules di-
rectly extracted from great numbers of scientific papers
and alter reasoning results accordingly. Because the
production rules can be obtained automatically, the de-
gree of KBS automation is improved and knowledge
acquisition time is reduced relative to traditional methods
of knowledge acquisition. In this way, computer-assisted
knowledge acquisition processes can be realized.
4.1. Stage 1: Labeling of Causal Statements
The aim of this stage is to mine casual statements from
great numbers of field-specific articles. This paper
mainly processes the abstracts and conclusions of scien-
tific papers because they are contain the most useful field
Text extraction of casual statements takes advantage
of NLU and data mining technologies and uses existing
Copyright © 2011 SciRes. IJIS
Figure 2. Production rule conversion.
field knowledge for guidance. This stage comprises the
following steps:
1) Extract abstracts and conclusions from the scientific
articles in question, then label and extract their conclu-
sive sentences.
2) Label field glossaries such as “NEPE propellant,”
“metal powder,” “oxidants,” “energy,” and so on. Cur-
rent effective labeling techniques can be adopted in this
step. These include Tregex, which is used to search and
operate tree data structure.
3) Label field operation terms, such as “increase,” “de-
crease,” and “add.” These operation terms will take the
glossaries defined in the previous step as operation ob-
4) Identify causal words and use predefined causal
templates with the terms and glossaries to extract causal
statements from the conclusive sentences.
Causal statements can then be extracted from scientific
articles and these sentences can be translated into pro-
duction rules.
4.2. Stage 2: Elimination of Noise
After stage 1, some noisy words that do not contain
knowledge will inevitably be included in the causal
statements. These noisy words affect the accuracy of the
production rules and should be eliminated before con-
verting the field causal statements to production rules. To
this end, noise elimination templates must be constructed
according to field and language characters. Because the
labeled contents are often conclusive sentences that al-
ways have fixed patterns and locations, it is practical to
locate most of the similar noisy words with high occur-
rences and separate them from the labeled causal state-
ments using fuzzy matching algorithms. Table 1 shows
key words often labeled as noisy content.
4.3. Stage 3: Extraction of Antecedence and
Consequen c e Phras e s
Once the noisy words have been eliminated, it is crucial
to convert the remaining labeled content into rough pro-
duction rules describable as “IF (antecedence) THEN
(consequence)” according to causal templates. This en-
sures the accuracy of the whole conversion, so it is im-
portant to construct effective causal templates with high
accuracy. In field-specific scientific papers, causal rela-
tionships are usually presented in fixed patterns. Conver-
sion templates meant to translate causal statements to
production rules can be constructed by summarizing
common presentation patterns. Detailed information is
provided in Appendix.
In the Chinese language, some words are used to indi-
cate casual relations explicitly, such as “for,” “because,”
“so,” and so on. For sentences that have obvious casual
relationships, it is quite easy to identify antecedents and
consequents fill in “IF (antecedent) THEN (consequent)”
rules. For example, the causal statement “B is the result
of A” can be converted to “IF (A) THEN (B).” However,
sometimes causal statements are more complicated. In-
tricate “and” and “or” relationships may exist among
many conditions and results. Conditions and results may
even intersect. Conversion templates must be designed to
deal with this complicated reality. They must also be
Table 1. Key noise content words.
preliminary view, studies suggest that, studies show
that, experimental results show that, results show that,
studies discover that, preliminary discover, through
this experiment, through above analysis that we can
see , … shows, discover … by computing, discover …
by experiments, discover … by studies, computing
results show, experiments show, discover … by ana-
lyzing …, as experimental results indicate, in sum-
mary, etc.
tested and refined according to experiment results. This
improves the effectiveness of the conversion from causal
statements to production rules.
After this stage, causal statements described in natural
language are formatted into “IF (antecedent) THEN
(consequent)” structures. The antecedents and conse-
quents are refined and formatted to predicate logic to
generate production rules correctly. The semantics of the
production rules must be consistent with the causal
statements. Integrity must be preserved during conver-
4.4. Stage 4: Generation of Production Rules
The antecedent and consequent of each production rule
must be separated from the causal statement. However,
they are still represented in natural language and cannot
be used to form production rules. This kind of know-
ledge, then, must be converted into first-order predicate
expressions that have clear semantics and are suitable for
use in production rules. In this stage the parts of speech
(POS) of the antecedent and consequent are tagged. Next,
POS tagging results are analyzed according to a parsing
tree (Figure 3) and a bottom-up merge is carried out. At
the same time, the order of the words and phrases in each
antecedent and consequent are changed to eventually
form predicate verbs with structures such as “predicate
(object, value).”
The POS sequences of the antecedents and cones-
quents are produced through this POS-tagging. Then
verb-object structures contained in the antecedents and
consequents can be found out. Finally, production rules
are generated by translating the verb-object structures
into first-order predicate expressions. Generally, predi-
cate (object, value) structure is the basic element of pro-
duction rules. The process of transforming antecedents
and consequents into production rules can be focused on
generating predicate (object, value) structures.
In this paper, a parsing tree, which is summarized
from scientific articles from one specific field, is used as
guide to analyze POS sequences and transforming them
into predicate structure elements. The conversion process
is made up of the following steps:
1) Mark the POSes of the antecedents and consequents
and represent these in speech pairs (word, speech tag).
For example, an antecedent that literally says “increase
content of metal fuel AL” and may be represented with a
speech pair list including (increase, V), (metal fuel, DV),
(AL, DC), (of, “of”), (content, DV). There are eight POS
types in this system, as shown in Table 2 .
2) Merge the appropriate speech pairs into noun ph-
rases (NPs) according to the parsing tree (Figure 3) and
mark the speech tag as NP. For example, according to
Copyright © 2011 SciRes. IJIS
Copyright © 2011 SciRes. IJIS
Verb Structure AnalysisFixed Item Analysis
Noun Structure AnalysisSummarize
Adj Structure Analysis
Num Unit
Figure 3. Speech pair list parsin g tree.
4.5. Implementation of Production Rule
Generation via Algorithm
the “DC.DV->NP” branch of the parsing tree, “metal
fuel” and “AL” should be combined and tagged “NP” to
form a new speech pair: “(metal fuel, NP).”
3) Combine verbs with NPs and other words according
to the parsing tree to form predicate elements with
“predicate (object, value)” structure. For example, the
speech pairs (increase, V), (metal fuel AL, NP), and
(content, V) must be merged together to form a predicate
element such as increase (AL, content). In this step,
some fixed phrases must also be converted into formal
formats according to the corresponding part of the pars-
ing tree (Figure 3).
In the process of obtaining rules from scientific literature,
the first three steps can be implemented by using existing
mature template matching algorithms. At the end of the
third stage, the antecedence and consequence of each
rule has been extracted from the causal statements and
structured as “IF (antecedence) THEN (consequence).”
However, the antecedence and consequence are still rep-
resented in the form of natural language. This section
will focus on describing the algorithm used to convert
them into predicate expressions according to the parsing
tree shown in Figure 3.
4) Merge the predicate elements bottom-up according
to the parsing tree and change the relative positions of
the predicate elements. When the merging process comes
to the highest level, rules will be generated.
1) This step can be implemented by querying field
variable table, field constant able, etc. (Table 2). After t
Table 2. POS in the KBS.
POS Type Corresponding Causal Statement Element
Field Variable: DV Variables, such as “metal power,” etc.
Field Constant: DC Constant, such as AL, etc.
Field Verb: V Verb, such as “increase,” “improve,” “raise,” etc.
Value: Value Constant, such as “temperature is 20°C,” etc.
Degree Word: Adj (adjective) Degree words, such as “mass of,” etc.
Field Adverb: Ad (adverb) Properties, such as “significantly,” “effectively,” etc.
And/or Relationship Words: And/or Logical words, such as “at the same time,” “and,” etc.
Negative Words: Non Negative words, such as “not,” “cannot,” etc.
Preposition: Prep Generally this is the Chinese word “liao,” following a verb.
Comparing Words: Com Such as “more than,” “equal to,” “lower than,” “reach,” etc.
Suffix: Pos Generally this is the Chinese word “de,” following a DC or DV.
Number: Num Number, such as 18, 32, etc.
Unit Noun: Unit Unit nouns, such as mm.s-1, %, etc.
this step, speechPairs structured as (word, speech tag) are
created. The speechPairs of each antecedent and conse-
quent are stored in the speechPairList.
2) Extract predicate expressions by merging structured
speechPairs according to the parsing tree: The merging
process must take place in a bottom-up and circular style
following the instructions in Stage 4. When deriving NP
and predicate elements, conflict resolution may be nec-
essary if multiple routes exist. The merging algorithm is
described as follows:
//Set merging completion mark:
end = false;
//Keep looping until POS merging is completed and
production rule is generated
//”.” indicates adjoining words
//Combine Noun structure circularly
word,”DC”>)||< word A,”DC”>.”de”Or< word
A,”DV”>.”de”||< word A,”DC”>.< word B,”adj”>||<
word A,”NP”>AndOr< word B,”NP”>){
Routes route = searchAllMatch-
route = resolveConfliction(route);
//Summarize current word according to final
route path
word C = compile(words sequence, route);
define(word C, “NP”);
//Change single speech pair with structure < word,”DC”>
or < word,”DV”> into < word,”NP”>;
if(single(< word A,”DC”>)|| single(< word
define(word A, “NP”);
//Merge Verb structure words into predicate element cir-
while(<word A,”adv”>.< word B,”V'>||< word
A,”NP”>.< word B,” non”>.< word C,” V”>||< word
A,”NP”>.< word B,” de”>.< word C,” V”>){
Routes route = searchAllMatch-
route = resolveConfliction(route);
// Summarize current word according to final
route path
word C = combine(words sequence, route);
define(word C, “VP”);
//Convert fixed phrases into formal formats cir-
Copyright © 2011 SciRes. IJIS
while(<word A,”Num”>.< word B,”Unit”>){
word C = combine(word A, word B);
define(word C, “Val”);
if(antecedence or consequence has been converted to
predicate elements or formal fixed phrases){
PredicateExpression pe = compose(speechPair,
end = true;
//Generate production rule.
convert(speechPairList, pe);
Production rules obtained through the abovementioned
process can be used in knowledge reasoning immediately
after conflicts have been eliminated. It can then be stored
in corresponding knowledge managing modules.
5. Experiments
This section describes two instances of the algorithm
used for deriving rules from scientific articles under
simulated use.
5.1. Example 1
Stage 1: A causal statement is extracted from the paper
“Effect of RDX Particle Size on Properties of CMDB
Propellant” [24]. “As experiments have proved, adding
moderate amounts of AP and decreasing the granularity
and granularity gradation of high-energy composite pro-
pellants can improve flammability.”
Stage 2: After eliminating the noise in the statement,
the result turns to be “through adding moderate AP, de-
creasing its granularity and granularity gradation in
high-energy composite propellant, the flammability will
be improved.”
Stage 3: The antecedent and consequent are derived by
matching casual relation templates (Through A, B: IF (A)
THEN (B)) and represented as IF (adding moderate AP,
decreasing its granularity and granularity gradation in
high-energy composite propellant) THEN (the flamma-
bility will be improved).
Stage 4: In this stage, the predicate expressions are de-
rived from the antecedent and consequent according to
the parsing tree. This can be implemented following the
instructions in Section 4.4.
1) By querying the field variable table (e.g., high-en-
ergy composite propellant), field constant table (e.g., AP),
etc. (Table 2), the structured speechPairs of the antece-
dent and consequent are produced as follows: IF({in
[.prep]}{ high-energy composite propellant [.DV]} {add
[.V]} {moderate[.adj]} {AP[.DC]} {,[.And]} {decrease
[.V]} its{granularity[.DV]} {and[.And]} {make[.V]}
{granularity[.DV]} {gradation[.DV]}) THEN ({improve
[.V]} {flammability[.DV]}).
2) The process of merging speechPairs based on the
parsing tree is shown in Figure 4:
Highenergy composite propellant && addAPmoderate&&decrease
granularity&&makegranularity gradation
POS replacement
NP&&VNP&&VNP&&VNP&&VNPPOS replacement
antecedence analysisconsequence analysis
Prep DV
adj DV and DV and DV DVand DV DV
Figure 4. Merging process of example 1.
Copyright © 2011 SciRes. IJIS
Copyright © 2011 SciRes. IJIS
3) The production rule is finally derived from the
causal statement as follows: IF (high-energy composite
propellant &&add (AP, moderate) && decrease (granu-
larity) &&make (granularity gradation)) THEN (improve
5.2. Example 2
Stage 1: Another example of a statement with a causal
relationship was extracted from the paper “Influence of
Ammonium Perchlorate and Aluminum Powder on the
Combustion Characteristics of AP-CMDB Propellant”
[25]. “If average size of RDX decreases from 92.02 μm
to 17.35 μm, pressure index will increase 7.3%.”
Stage 2: The above statement does not contain any
noise, so it is not changed during this stage.
Stage 3: By matching casual relation template (If A, B:
IF (A) THEN (B)), the antecedent and consequent can be
extracted from the statement and represented as follows:
IF (average size of RDX decreases from 92.02 μm to
17.35 μm) THEN (pressure index will increase 7.3%).
Stage 4: Based on the above result, the production rule
can be derived as follows:
1) After querying field variable table (e.g., average
size), field constant table (e.g., RDX), field verb table
(e.g., decrease, increase) etc. (shown in Table 2), the
structured speechPairs will be produced. The result is:
IF({RDX[DC]} {average size[DV]} {92.02[Num]} {μm
[Unit]} {decrease[V]} {17. 35 [Nu m]} {μm[Unit]}) THEN
({pressure index[DV]} {increase[V]} {7.3[Num]} {%
2) According to the parsing tree, the merging process
of the speechPairs takes place as shown in Figure 5.
3) Upon the completion of these processes, the pro-
duction rule becomes “IF (decrease (RDX average size,
92.02 μm, 17.35 μm)) THEN (increase (pressure index,
6. Conclusions and Possibilities for Future
This paper is based on a well-noted fact: in specific pro-
fessional fields, experts often use relatively fixed pat-
terns to describe their findings when they publish scien-
tific articles. Moreover, the abstracts and conclusions,
the essence of these articles, contain abundant field
knowledge. Labeling the summarizing those statements
in abstracts and conclusions that have apparent causal
relationships, transforming these relationships into for-
mats that can be used in KBSes, and then using an auto-
matic process to represent this knowledge as production
rules may greatly improve the effectiveness and accuracy
Figure 5. Merging process of example 2.
Copyright © 2011 SciRes. IJIS
of knowledge acquisition.
In this paper a pattern-text-mining-based method of
acquiring production rules from field-specific papers is
proposed. This paper takes texts that contain field know-
ledge as input. First, the summarizing statements in the
abstracts and conclusions of the articles are processed
using mature technologies, such as text mining, and
causal statements are extracted from them. Then produc-
tion rules are derived by POS analysis and parsing tree
Two experiments showed that the proposed method
can be used effectively to obtain production rules from
summarizing statements. In future studies, algorithms
used for POS analysis can be investigated to enhance the
algorithm’s self-learning ability. At the same time, the
process of converting causal statements to production
rules can be refined to further improve the efficiency of
field knowledge acquisition.
7. Acknowledgements
This work was supported by the 973 Program (Project
No. 2010CB327900).
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Appendix: Causal Relationships in Scientific Articles
Original sentence Production rule
1 If A, B IF (A) THEN (B)
2 After A, B or B, after A IF (A) THEN (B)
3 With A, B or B, with A IF (A) THEN (B)
4 When A, B or B, when A IF (A) THEN (B)
5 As A, B or B, as A IF (A) THEN (B)
6 Because A, B or B, because A IF (A) THEN (B)
7 A is the reason for B IF (A) THEN (B)
8 When A, after B, C IF (A AND B) THEN (C)
9 Through A, B IF (A) THEN (B)
10 B is the result of A IF (A) THEN (B)
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