=Paper=
{{Paper
|id=Vol-3745/paper7
|storemode=property
|title=Identifying Scientific Problems and Solutions: Semantic Network Analytics and Deep Learning
|pdfUrl=https://ceur-ws.org/Vol-3745/paper7.pdf
|volume=Vol-3745
|authors=Lu Huang,Xiaoli Cao,Hang Ren,Chunze Zhang,Zhenxin Wu
|dblpUrl=https://dblp.org/rec/conf/eeke/HuangCRZW24
}}
==Identifying Scientific Problems and Solutions: Semantic Network Analytics and Deep Learning==
https://ceur-ws.org/Vol-3745/paper7.pdf
Identifying scientific problems and solutions: Semantic network
analytics and deep learning
Lu Huang1,2, Xiaoli Cao3,4,οͺ, Hang Ren1, Chunze Zhang1,5 and Zhenxin Wu3,4
1
School of Economics, Beijing Institute of Technology, Beijing, China, 100081
2
Digital Economy and Policy Intelligentization Key Laboratory of Ministry of Industry and Information
Technology, Beijing Institute of Technology, Beijing, China, 100081
3
National Science Library, Chinese Academy of Sciences, Beijing, China, 100190
4
Department of Information Resources Management, School of Economics and Management, University
of Chinese Academy of Sciences, Beijing, China, 100190
5
Zhejiang Sineva Intelligent Technology Co., Ltd, Zhejiang, China, 314499
Abstract
As critical building blocks of scientific research, scientific problems and solutions are put
forward to reveal the existing issues and primary methods in scientific and technological
practice. In this paper, we proposed a novel method for identifying scientific problems and
solutions using semantic network analytics and deep learning. Firstly, the BERT-CRF model
constructed is combined with BIO tagging to identify four entity types: research object, problem,
solution, and fundamental principle. Then, the Levenshtein algorithm is applied to align entities,
and a knowledge network is constructed integrating semantic information and co-occurrence
associations, comprehensively and accurately depicting the relations between entities. Finally,
the correlations between the four entity types are thoroughly explored using semantic network
analytics and topological structure analytics. A case study on artificial intelligence domain
demonstrates the reliability of the proposed methodology, and the results provide intelligent
support for raising and solving scientific problems in the field.
Keywords 1
Scientific problems and solutions, Semantic network analytics, BERT-CRF, Knowledge
network, Entity identification
1. Introduction Identifying scientific problems and solutions can
help scholars map the scientific field, enhance the
speed of information retrieval and processing,
The rapid increase in scientific articles lays a
and offer reference solutions for real-world
strong foundation for identifying problems and
issues in industrial practices [2,3].
solutions in a field [1]. The intelligent mining of
Some scholars have mentioned that problems
scientific problems and solutions aims to identify
and corresponding solutions constitute the "key
the real-world issues existing in the scientific and
insights" within scientific articles [4]. Many
technological practices of a field, find
significant studies focus on extracting key
corresponding solutions, and explore the
viewpoints (e.g., research problems, and
underlying theoretical foundations. It facilitates a
solutions) from scientific papers using entity
deep exploration of the intrinsic logical
extraction techniques [5,6]. However, these
relationships among research objects, problems,
methods usually involve supervised learning on
solutions, and fundamental principles.
οͺ
Corresponding Author
Joint Workshop of the 5th Extraction and Evaluation of
Knowledge Entities from Scientific Documents and the 4th AI +
Informetrics (EEKE-AII2024), April 23~24, 2024, Changchun,
China and Online
EMAIL: huanglu628@163.com (Lu Huang);
cxl163990307@163.com (Xiaoli Cao); renhang0988@163.com
(Hang Ren); zhangchunze@sineva.com.cn (Chunze Zhang);
wuzx@mail.las.ac.cn (Zhenxin Wu)
Β©οΈ 2024 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
60
pre-annotated datasets, a process that requires To address these concerns, we propose a
significant resources for domain-specific novel framework for identifying problems and
annotation [7]. Deep learning is an efficient and solutions using semantic network analytics and
accurate technology for extracting information deep learning. The proposed method advances
from complex unstructured data (e.g. graphics, the fields of entity extraction and knowledge
text) and converting data into vector graph analysis by delineating three specific
representations [8,9]. Combining deep learning functions: 1) the BERT-CRF model is
with bibliometrics is often used to address constructed to generate textual representations
problems in science, technology, and innovation with enhancing semantics, and it is combined
(ST&I) management [10,11]. As an important with BIO tagging to identify four entity types:
area of deep learning, text representation learning research object, problem, solution, and
effectively extracts information from text data fundamental principle, improving the accuracy of
has been widely applied in data mining [12]. identifying entities; 2) the Levenshtein algorithm
Additionally, some scholars employ methods is applied to align entities, and the semantic
such as keyword network analysis and citation relations and co-occurrence associations are
analysis to construct academic knowledge graphs, integrated to construct knowledge network,
deeply exploring the relationships among comprehensively and accurately revealing the
knowledge entities in papers [13,14]. For relationships between entities; 3) the
example, Zhang et al. [15] integrate multiple combination of semantic network analytics and
relationships such as co-occurrence, citation, and topological structure analytics are applied to
co-authorship to explore the processes of thoroughly explore the correlations between the
knowledge creation, knowledge transfer, and four entity types. We use a case study on artificial
other knowledge evolution dynamics. However, intelligence domain to demonstrate the reliability
these researches ignore the specific semantic of our proposed method.
functions of keywords in different contexts,
leading to a lack of accuracy in the representation 2. Method
of knowledge structures [16]. Semantic network
analysis incorporates the rich semantic
information of keywords into network analysis, The framework of identifying scientific
problems and solutions is shown in Figure 1.
providing a more intensive and accurate analysis
for the mining of scientific problems and
solutions [17,18].
Figure 1: Framework of identifying scientific problems and solutions
61
2.1. Entity identification of scientific texts into feature vector matrices. The
Bidirectional Encoder Representation from
scientific problems and solutions Transformers (BERT), a deep learning
2.1.1. Entity concept construction technology based on the bidirectional
based on Structural Topic Model transformer architecture [23], can capture
contextual semantic information and latent
The paper data gathered is acquired from the relationships from large-scale corpora, achieving
Web of Science (WoS) and pre-processed via more precise textual semantic representations
VantagePoint (VP) [19]. [24]. BERT can process vast amounts of textual
Then, four abstract entity concepts are corpus data with a need for minimal training
constructed based on the concept of Structural datasets. Combined with the Conditional
Topic Model (STM), which includes "research Random Field (CRF) model, it can effectively
object", "problem", "solution" and "fundamental improve the efficiency and quality of text
principle". These entities serve as a structured sequence labelling [25].
representation of knowledge, characterizing First, the BERT model is trained on each of
scientific problems and solutions. The STM, an the four types of entities by using a pre-training
advancement over the Latent Dirichlet dataset and setting the model parameters. To
Allocation (LDA) topic model, extracts topics acquire enhanced vector representations that
from document-level metadata and establishes include contextual positional information, this
latent connections between these topics and the paper integrates the CRF model [26] with BERT
document data [20]. This approach facilitates the based on the embedding during the training
discovery of hidden knowledge structures within process, further training and optimizing the
texts and the accurate delineation of implicit configuration of feature function. Moreover, the
relationships among them [21]. Therefore, this multi-head self-attention mechanism of BERT is
study employs the STM to construct entity applied to better capture contextual semantic
concepts. information [27], obtaining enhanced textual
Within this knowledge structure, "research semantic representation vectors. When the model
object" refers to the research subfields, serving as converges, the well-trained BERT-CRF model is
the starting point of the research; "problem" generated.
focuses on the scientific issues to be resolved and Then, the well-trained model is used to
the goals to be achieved, jointly defining the transform the dataset into vector representations
problems space with the research object; with enhanced contextual positional information
"solution" describes the overall solution to the and multiple semantic information.
problem, representing the key steps towards
achieving the goals; "fundamental principle"
refers to the theoretical foundation underlying the
2.1.3. Entity extraction
solution methods. The four-entity concept
constructed provides a comprehensive analytical The purpose of this section is to extract
framework reflecting the essence of research scientific problem and solution entities by
literature. By capturing and mining these four transforming textual semantic vectors into
key entities, this study can excavate the scientific probabilistic representations of text sequence
problems and solutions within the paper data, labeling using BERT-CRF model and BIO
unveiling research hotspots in the domain. tagging.
Finally, the four types of entities in a small First, the well-trained BERT-CRF model is
number of literatures are manually identified and applied to process the text vectors using the
a pre-trained dataset is generated based on the SoftMax function [28], generating predicted
BIO tagging [22]. labels corresponding to the text sequence.
Assuming the sentence length is π, and the input
text sequence is represented as π =
2.1.2. Text vector acquisition based (π₯1 , π₯2 , β― , π₯π ) , the corresponding predicted
on BERT-CRF model label sequence is represented as π =
(π¦1 , π¦2 , β― , π¦π ). The method for calculating the
This section aims to construct an enhanced final prediction score π ππππ(π, π) for the text
semantic BERT-CRF model, transforming sequence π is:
62
π π
2.2.2. Constructing knowledge
π ππππ(π, π) = β ππ₯π,π¦π + β ππ¦πβ1 ,π¦π (1)
π=1 π=2
network integrating semantic
where ππ₯π,π¦π represents the probability of the text and co-occurrence relations
sequence element π₯π being predicted as π¦π and
ππ¦πβ1 ,π¦π is the score for the transition from label The purpose of this section is to construct a
π¦πβ1 to label π¦π . heterogeneous knowledge network including
Thus, the probability distribution matrix four types of entities, integrating semantic and
corresponding to the text sequences is obtained. co-occurrence information among entities, and
Then, this paper integrates BIO tagging [22] improving the accuracy of relationship
into the CRF layer to generate four sequence identification between entities.
labeling matrices of "research object", "problem", First, the cosine distance between entity
"solution" and "fundamental principle" based on vectors is used to measure the semantic similarity
the probability distribution matrix. Finally, the between entities. The calculation method of the
entity categories corresponding to the text semantic similarity π ππ(π, π) between entity a
sequences are identified based on the sequence and b is:
annotation results. π(π)π π(π) (2)
π ππ(π, π) =
||π(π)||2 β ||π(π)||2
where π(π) and π(π) denote the textual vectors
2.2. Knowledge network of entities a and b respectively.
construction Then the co-occurrence relation between
entities is obtained based on the literature data.
The co-occurrence association between entities a
After identifying the four types of entities and b is denoted as ππ_πππ‘ππ‘π¦(π, π) represented
corresponding to scientific problems and by the number of co-occurrences between a and
solutions, a knowledge network containing b.
multiple semantic and structural information Finally the entropy weight method [30] is
between entities is constructed. This part introduced to integrate the semantic information
includes two sections: 1) Entity alignment based and co-occurrence information between entities
on Levenshtein algorithm and 2) Constructing and get the relation between entities in the
knowledge network integrating multiple relations. knowledge network. Link weight π€πππβπ‘(π, π)
between entities a and b can be calculated as:
2.2.1. Entity alignment based on π€πππβπ‘(π, π) = πΌ β π ππ(π, π) + (3)
Levenshtein algorithm π½ β ππ_πππ‘ππ‘π¦(π, π)
where πΌ and π½ are coefficients of semantic
Considering that entities extracted from similarity and co-occurrence correlation obtained
by entropy weight method respectively, and
different literature may have multiple names
for the same entity, we apply the Levenshtein πΌ+π½=1.
method for measuring the difference between In this section we generate a knowledge
two sequences, to disambiguate. Levenshtein network πΊ = (π, πΈ, π) containing rich semantic
algorithm can consider both the contextual and structural information among entities where
information and semantic similarity, enhancing π πΈ and π denote the entities edges and edge
the accuracy of entity alignment [29]. weights in πΊ respectively.
Furthermore, this paper constructs an entity
dictionary based on expert knowledge, which 2.3. Scientific problems-solutions
is used for further checking and proofreading correlation analysis
of entity alignment results.
This part aims to identify the primary research
problems corresponding to the core research
objects and find the main solutions and
theoretical basis based on the topological
structure analysis of knowledge networks.
63
2.3.1. Core research objects Similarly this paper identifies the main
solutions corresponding to the primary problem
identification based on ππ and the main fundamental principle for the
PageRank algorithm corresponding solutions.
Finally we generate a series of complete
In this section PageRank algorithm is used to chains of scientific problems and solutions
measure the importance score of research objects which can be represented as "research object -
and thus identify core research objects in the problem - solution - fundamental principle".
knowledge network. This algorithm fully
considers multiple factors including the local 3. Case study
topological structure and semantic information of
the target node and the importance of the nodes Artificial Intelligence (AI) is a
connected with it [31] which has been widely multidisciplinary domain composed of a diverse
applied to identify core nodes in various complex and heterogeneous network of innovations. It has
knowledge networks [32]. Therefore we use emerged as a significant force driving
PageRank algorithm to rank the importance of technological innovation [33]. This field
research objects in knowledge network. The encompasses many emerging research questions
calculation method of the importance score and research methods offering extensive data
ππ
(π) of research object a can be calculated as: support for empirical analysis. Therefore this
ππ
(ππ ) (4)
ππ
(π) = π Γ βπ π=1 πΆ(π ) + (1 β π) paper analyzed the scientific problems and
π
where d is the damping factor (0 β€ π β€ 1), solutions in-depth in the AI domain to verify the
effectiveness of the proposed method.
generally 0.85, ππ denotes the entity linked to the
research object π, πΆ(ππ ) is the number of entities
linked with ππ , and π is the number of entities 3.1. Entity identification based on
linked with research object π. BERT-CRF model
Finally we sort the research objects in the
knowledge network based on the importance Following the study of Liu et al. [34] a total
score and select the top-K research objects as the of 375608 papers published between 2021 to
core research objects. The core research objects 2023 were retrieved from the Web of Science
set π is represented as: (WoS). Then VantagePoint (VP) was used to
π = {π1 , β― , ππ , β― , ππΎ } (5) process titles and abstracts of papers. Finally a
where ππ denotes the i-th core research object in total of 310456 papers were retained as the
the knowledge network and K is the number of textual corpus and a total of 3000 papers were
core research objects that has been identified. randomly selected in proportion to the
publication year as the pre-training dataset.
2.3.2. Knowledge structure-based Based on the BIO tagging the titles and abstracts
of the pre-training dataset were annotated with
entity correlation analysis "research object", "problem", "solution", and
"fundamental principle".
After identifying the core research objects Following the design in Section 2.1.2 this
within the knowledge network this section will study constructed an enhanced semantic BERT-
deeply analyze the correlation between entities in CRF model based on the textual dataset to
the domain based on the topological structure transform text data into feature vectors. During
analysis of the knowledge network. the experimental process the performance of the
First based on the link weights between the model was assessed based on evaluation metrics
core research object ππ and the research (Precision Recall and F1-score) [35] with
questions the primary problems corresponding model parameters being continuously adjusted.
to ππ are identified. The primary problems set π The optimal model was determined when the
is represented as: evaluation metrics reached their maximum
π = {π1 , β― , ππ , β― , ππ } (6) values. Finally when the Precision of the model
where ππ denotes the i-th primary problem reaches 89.2% the Recall reaches 87.4% and the
corresponding of ππ and m is the number of F1-score reaches 88.3% the optimal model was
primary problems.
64
generated. The results show that the trained comprehensively considered the semantic
BERT-CRF model exhibits better performance. similarity between entities and expert knowledge
Finally the trained BERT-CRF model and to achieve entity synonym alignment. The
BIO tagging method were used to identify four Levenshtein algorithm is employed for aligning
types of entities. We identified 24 254 "research entities within the same category and across
object" entities 23 839 "problem" entities different categories. Finally a total of 887
20 670 "solution" entities and 17 550 "research object" entities 4 136 "problem"
"fundamental principle" entities from the dataset. entities 13 858 "solution" entities and 5 518
"fundamental principle" entities were obtained.
3.2. Constructing knowledge Then we integrated semantic similarity and
structural similarity between entities to build a
network in AI knowledge network in AI domain. The statistical
information on the edges between each type of
After identifying the four types of entities entity in the knowledge network is shown in
corresponding to scientific problems and Table 1.
solutions from the text dataset this paper
Table 1
Descriptive statistics of edges in knowledge network
Research Fundamental
Problem Solution
object principle
Research
/ 12683 9105 8468
object
Problem 12683 / 17691 10565
Solution 9105 17691 / 13783
Fundamental
8468 10565 13783 /
principle
According to the results of the PageRank
3.3. Entity correlation analysis algorithm, the hot research objects in artificial
intelligence domain mainly include deep learning,
neural network, medical image, facial image,
The next stage was to analyze the correlation robot, and electric system. Following the design
between entities. Following Section 2.3.1, the in Section 2.3.2, the top-2 problems were
PageRank algorithm was applied to calculate the
identified corresponding to the core research
important scores of research objects and thus objects within the knowledge network.
identify the core research objects in the Finally, we explored the correlations between
knowledge network. The hot research topics in entities and generated a series of complete chains
the artificial intelligence domain can be explored including four types of entities. The partial entity
according to the core research objects. correlation results of Top-6 core research objects
are shown in Figure 2.
65
Figure 2: Entity correlation results
Several observations can be acquired based on artificial intelligence. On the other hand, it is able
the above results. The research object represents to detect the corresponding solutions to the real
a subfield, where the problems refer to the issues problems in the scientific and technological
contained within that subfield, the solution refers practice and explore the theoretical basis behind
to the methods or technologies required to solve them, and thus realize the in-depth excavation of
the problems, and the fundamental principles the intrinsic logical connection among scientific
refer to the inherent principles involved in the problems, solutions and fundamental principles.
implementation process of the methods and
technologies. The "research object" and 3.4. Validation
"problem" together constitute the complete
scientific problem, and the "solution" and
In this part, we conducted the quantitative and
"fundamental principle" together constitute the
qualitative methods to verify the reliability of our
complete solution. For example, for the identified
"classification - image classification - neural proposed method and entity identification results.
network β feature extraction", it refers that the
neural network can be used to solve image 3.4.1. Verification of the trained
classification problems through feature model
extraction [36].
This paper identifies a complete chain of
To quantitatively verify the advantages of the
"research object - problem - solution -
combination of BERT-CRF model trained in this
fundamental principle". On the one hand, it can
paper and the BIO tagging method, we select
identify the core research objects and
three advanced models, ALBERT [37], SciBERT
corresponding primary problems in the field of
[38], and XLNet [39], for comparison
66
experiments. Referencing the model parameters optimal effect. The specific parameter settings of
of BERT-CRF in this paper, the three models the models are shown in Table 2.
were fine-tuned respectively to achieve the
Table 2
Parameter configurations of models
Our
ALBERT SciBERT XLNet
method
Maximum input
64 64 64 64
length
Training epoch 30 40 30 35
Batch size 4 16 4 8
Number of layers 12 12 12 12
Learning rate 1e-5 5e-6 1e-5 1e-6
CRF learning rate
100 / 50 /
multiplier
Then, the performance of our method was comparing with three state-of-the-art methods.
validated based on Recall and Precision by The comparison results are given in Table 3.
Table 3
The comparison of prediction performance
Fundamental
Research objects Problems Solutions
Methods principles
Recall Precision Recall Precision Recall Precision Recall Precision
Our
0.980 0.965 0.964 0.942 0.856 0.877 0.724 0.759
method
ALBERT 0.934 0.936 0.924 0.896 0.848 0.815 0.638 0.702
SciBERT 0.928 0.955 0.906 0.883 0.834 0.827 0.704 0.740
XLNet 0.962 0.919 0.944 0.907 0.838 0.859 0.680 0.741
It can be seen that our method outperforms respectively and the Precision value increases by
baseline methods in two evaluation indicators. 5.7% 1.9% and 1.8% respectively. These results
Concretely in the entity recognition of the demonstrate the combination of BERT-CRF
research objects the Recall value of our method model and BIO tagging used in this paper has
increases by 4.6% 5.2% and 1.8% respectively achieved good performance on our dataset.
and the Precision value increases by 2.9% 1.0%
and 4.6% respectively. In the problems 3.4.2. Verification of entity
identification the Recall value of our method
increases by 4.0% 5.8% and 2.0% respectively identification
and the Precision value increases by 4.6% 5.9%
and 3.5% respectively. In the solutions In this section the qualitative method was
identification the Recall value of our method applied to verify the reliability of the entity
increases by 0.8% 2.2% and 1.8% respectively identification results by searching relevant articles
and the Precision value increases by 6.2% 5.0% published in 2021 and beyond. Table 4 shows the
and 1.8% respectively. In the entity identification detailed empirical evidence of partial entity
of fundamental principles the Recall value of our identification results.
method increases by 8.6% 2.0% and 4.4%
67
Table 4
Relevant documentary proof of partial entity identification results
Research object - problem -
No solution - fundamental Relevant documentary proof
principle
classification - image In 2021, Nadendla et al. proposed a neural network-based
1 classification - neural network β classifier by feature extraction and classification to solve the
feature extraction image classification problem [36].
clustering - deep clustering
In 2022, Hou et al. used a dual convolutional autoencoder to
performance - dual
2 extract features of multi-levels and fuse them to improve
convolutional autoencoder -
the performance of deep clustering [40].
multi-level feature fusion
medical image - medical image
In 2024, Tamilmani et al. used the convolutional neural
segmentation - convolutional
3 network with the optimal network topology to solve the
neural network - optimal
problem of medical image segmentation [41].
network topology
facial image - face recognition - In 2022, Wei-Jie et al. applied the convolutional neural
4 convolutional neural network - network by extracting the masked face features to solve the
feature extraction problem of masked facial recognition [42].
robot - local motion planning - In 2023, Garrote et al. proposed a deep reinforcement
5 deep reinforcement learning - learning strategy based on a reward model to solve the local
reward model motion planning problem of robots [43].
electrical power system -
In 2022, Gu et al. used the graph neural network based on
stability assessment - graph
6 the self-attention mechanism to evaluate the stability of the
neural network - self-attention
power system [44].
mechanism
Table 4 demonstrates the alignment between explore the linkages among research objects
our entity identification results and the literature. problems solutions and fundamental principles.
Therefore the four types of entities identified and Semantic network analytics and deep learning
the relations between entities in this paper are were combined to identify scientific problems
reliable and the effectiveness of the proposed and solutions from scientific text which provides
method has been further verified. technical intelligence for field scientific
innovation and industrial technology upgrading.
4. Conclusion In addition this method can not only explore the
association between entities reveal the primary
research problems and corresponding solutions
In this paper we proposed a novel in the field of artificial intelligence but also
methodology to identify scientific problems and discover the knowledge structure in this field and
solutions using semantic network analytics and promote the development of scientific
deep learning. First the deep learning method is knowledge network analysis methods.
applied to extract textual semantic information Several limitations of our proposed method
and identify entities capturing the hidden require further improvement: 1) The scientific
semantic association in different textual contexts and technological output of a certain field
effectively and improving the accuracy of the includes not only papers but also patents and
entity recognition. Then the machine learning product data. Further research should be
method was used to construct the knowledge conducted based on more data sources; 2) The
network fully considering the knowledge methodology of entity alignment can be further
structure and semantic structure between entities optimized. More advanced methods and
and thus containing more abundant information. professional expert knowledge could be
Finally the PageRank algorithm and semantic introduced in the future to improve the efficiency
network analytics were introduced to deeply and quality of entity alignment; 3) The evolution
mechanism of "research object - problem -
68
solution - fundamental principle" needs to be [9] R. Xiang, E. Chersoni, Q. Lu, et al, Lexical
further explored. data augmentation for sentiment analysis.
Journal of the Association for Information
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This work was supported by the National science-technology linkages: A deep
Nature Science Foundation of China Funds
learning-empowered solution. Information
(Grant No. 72274013), and Fundamental
Processing & Management, 60(2): 103255,
Research Funds for the Central Universities. 2023.
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