=Paper=
{{Paper
|id=Vol-1494/paper2
|storemode=property
|title=Semantic Search for Scientific Publications Based on Rhetorical Structure
|pdfUrl=https://ceur-ws.org/Vol-1494/paper2.pdf
|volume=Vol-1494
|dblpUrl=https://dblp.org/rec/conf/iwost/HuangFX15
}}
==Semantic Search for Scientific Publications Based on Rhetorical Structure==
https://ceur-ws.org/Vol-1494/paper2.pdf
Semantic Search for Scientific Publications Based on
Rhetorical Structure
Lan Huang, Kai Feng, and Hao Xu*
College of Computer Science and Technology, Jilin University, Qianjin Street 2699, Changchun,
China
huanglan@jlu.edu.cn, fengkai15@mails.jlu.edu.cn, xuhao@jlu.edu.cn
*Corresponding Author
Abstract. Most scientific papers have their own rhetorical structures, which have
deeply rooted in the minds of both authors and readers, such as background, prob-
lem and discussion. However, most existing search engines for scientific publica-
tions haven‘t made good use of such semantic information. In fact, each reader
would be interested in different semantic modules of a paper, that is, certain con-
cepts or entities mentioned in different semantic parts represent various indica-
tions. In this paper, we design and implement a semantic search platform that
aims to provide semantic search for scientific publications based on rhetorical
structure. To provide better results, we initiate with the semantic model of scien-
tific papers, so as to meet the special attention of the semantic module in papers
for readers.
Keywords: semantic search, rhetorical structure, semantic annotation.
1 Introduction
Authors always hold an logical structures in their minds while they write scientific pa-
pers. Besides, each scientific paper has its own rhetorical structure such as research
background, problem statement, solution and future work. For the sake of readers to
read more targeted, some publishers even require the structured abstract that authors
must provide. Nevertheless, the traditional search does not have strong connections
with these semantic information and numerous metadata of articles. Actually, accord-
ing to the phrases or words attained from the users’, the search engine positions the
documents. These words are regarded as the ordinary characters rather than any con-
cepts.
Semantic search is an application of Semantic Web [1]. Naturally, semantic search
for scientific papers needs a huge database for publications and metadata. The model
of data becomes the fundamental point in achieving semantic search because of the
structured data, which could be read by machines, and it is also convenient for us to
compute the relations between them [5]. Several models have been devised to label
the rhetorical structure within the papers. The Harmsze Model proposes that a paper is
constituted by metadata, positioning, methods, results, interpretation and outcome [2].
Another model for science publications named ABCDE includes annotations, back-
ground, contribution, discussion and entities [6]. Thus, most papers could be labeled
by these two coarse grained models. Generally, a paper contains modules, which are
made up by metadata, background, problem, solution and discussion. And the main
problem is the detection of rhetorical components. With structured data and relation-
ships stored in the database, semantic search services could be provided. ClaimFinder
was a research prototype which delivered the search services based on the original data
[3]. The home page of this website allows users to do the keyword search and shows
the result about the concept and some relations linked in the concept. And Mimir, an
Open-Source Semantic Search Framework, could provide complex queries on account
of natural language process and this framework is built on a cloud storage platform [4].
It stores annotations, tokens, index of all the basic data and etc. Thus, it would provide
the better result than the traditional way.
For scientific publications, the meaning of a concept would be vary when it ap-
pears in different semantic modules. It is worth mentioning that different people may
pay attention to the different parts of papers. In view of that, we design and implement a
semantic search platform based on the rhetorical structure and natural language process-
ing and semantic technologies. The platform extracts the keywords in different semantic
modules of papers. Meanwhile, semantic search could use these keywords and readers
could choose the semantic module they prefer. The platform would do the search under
the rhetorical structure and retrieve the list of papers which are more accurate. Our goal
is to provide more efficient and effective search services.
2 System Design
In order to accomplish the task as much as possible, we divide the work into three parts:
semantic annotation, concept/entity detection and semantic search. Semantic annotation
is the work for adding labels of rhetorical structure for scientific papers. Concept and
entity detection is to extract the keywords under rhetorical structure and to store them.
The part of semantic search is to process keywords and to compute the results based on
semantic modules.
The structure of the system as the Figure 1 shows below.
Fig. 1. Overall architecture of the system.
The data of articles contains the title, authors, year, and the whole text of the article
and they need to be processed by means of the semantic annotation and concept and
entity detection. Then, the structured data of each article ,including the keywords of
every semantic module and the title of the article, would be stored in the database via the
system. Finally,the database would be able to accept the requirements through searching
server and to return the result.
In this passage, the system contains two main functions. The first one is searching
papers based on rhetorical structure. The second one is enabling readers to annotate
semantic modules for scientific papers at their wills. The system would take the measure
of statistics to achieve the final semantic modules.
2.1 Semantic annotation
Before semantic annotation, we patternized basic rhetorical structure of scientific pub-
lications that could be modified or extended. The experiment of this passage pays em-
phasis on general rhetorical structure of scientific papers. For the convenience of us to
discuss, we entitled them as background, problem and solution respectively and there
are two ways to make semantic annotation. The first way needs the help of readers. As
the Figure 2 shows below, once the button clicked, the webpage would copy the text
selected and send it to the server. Then the system would extract the keywords through
the information the system stored.
Fig. 2. Readers complete the semantic annotation.
The second one to make the semantic annotation is achieved on the basis of LATEX,
which could manage text with labels. Meanwhile, we have designed some labels like
“\background”, “\problem” and “\solution”. If the authors use them, the system could
extract the keywords through the labels.
2.2 Concept and entity detection
The same concept could be distinguished easily for its different meaning in different
rhetorical structure. The system should guarantee that the concept and entity detection
relies on the accurate rhetorical structures.
In order to extract keywords, the system uses a program named Jieba word segmen-
tation, which is a java program for word segmentation. The traditional technique the
program used is named TF-IDF, which made good use of simple but effective ways to
extract the keywords. According to the term frequency and inverse document frequency
of the words appearing in the document, the system filter out common words and pre-
serve vital words. Once the system got the keywords, it would store them with the link
pointing to the article in the database as the Figure 3 shows.
Fig. 3. The attributes to store the keywords.
The first column is an identifier for each article. The second column is about the
keywords of ”background module”. The next column deal with ”problem module” and
the fourth column concerns ”solution module”. The fifth column stores the title of ar-
ticle. It presents the results in descending order by the score that ranks the statistical
significance in each semantic module. It would have fifteen to twenty keywords in each
semantic module of each article.
3 Semantic Search
Semantic search provides insight into unstructured documents stored by extracting the
relevant keywords and index statistics in the database. Then, it is also used to identify
these keywords and index similar or related documents.
According to the keywords extracted before, the platform could retrieve the key-
words in the semantic modules. In this passage, we take the score into full consideration
by both the weight and the published year. Since the keywords of a semantic module
of each article at the level of importance is not the same. To be specific, the hit key-
word in the first position of the article is assigned to three points. Then, the second one
represents two points and the following keywords are all amounted one point. Besides,
the score of published year is two in the past decade. And the articles published ten
years before amounted one point. Through the calculation of the searching server, it
then return the list of articles. The communication of data between the readers and the
database is as Figure 4 shows below.
Fig. 4. The process of query requirement.
First, readers input the keywords. The searching server sends a query requirement
to database. Then the database returns the data to searching server. Finally, through the
processing of the server, an ordered list would return to readers.
Readers could search the keyword and decide which semantic module they prefer.
So when the platform shows the result, it could focus on the scientific papers to hit
the keywords in the semantic module selected just now. For instance, when readers
search the keywords under the “solution module”, semantic search would search the
keywords in the solution column (key solution) of database and return all the papers hit
the keywords in their solutions. As the Figure 5 shows, the platform lists all the papers
using clustering to solve some problems.
Fig. 5. The result semantic search returned.
Besides, the platform could search in another way. With the help of semantic match-
ing, the platform could calculate all the relationships among the keywords, including
“more general” and “less general” relationship. In this experiment, when the platform
is searching “data mining”, an auxiliary program for semantic matching would find all
the words which have relationships with “data mining”. And Figure 6 shows the result
when readers searched “data mining” as the keywords. Since “k-means” is less general
than “data mining”, some papers about k-means will be returned.
4 Conclusion and Future Work
The traditional search returns the correct results and also returns too many of the articles
that are not so accurate. And the semantic search narrowed the range of the paper listed
Fig. 6. The semantic search result.
through the semantic annotation. From this view, searching scientific articles based on
the rhetorical structure becomes more rapid and accurate.
The platform is a preliminary experiment. And the next phase of work is to label
entity and implement the across-language platform. By tagging entity, readers could
understand the involved concept easily and find articles more accurate.
5 Acknowledgements
This work is supported by the National Natural Science Foundation of China (No.
61300147), China Postdoctoral Science Foundation (No. 2014M551185), and Science
and Technology Program of Changchun (No. 14GH014).
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