Computer Science Ontology Research Articles

Aggregate Functions in Categorical Data Skyline Search (CDSS) for Multi-keyword Document Search

Literature review is the first step in starting research for a deep understanding of the research interest. However, finding literature relevant to research interests is difficult and takes time. Skyline query is a method that can be used for filtering. An object p is said to dominate object q if p equals q on all of its attributes, and p is at least better than q on one attribute. Categorical Data Skyline Search (CDSS) is an algorithm that can filter skyline objects in categorical data types such as documents. CDSS uses Extended Distance Wu and Palmer (DEWP) to calculate the distance between the user query and document keywords. The document keywords and user queries are represented as nodes in the ACM CCS ontology, and documents are assumed to be represented by a single keyword. This study aims to use the CDSS algorithm to search for skyline documents represented by more than one keyword by adding an aggregate function (average, minimum, maximum) to the CDSS algorithm, especially in calculating DEWP. This study used the thesis documents from the IPB University computer science department. Document keywords will be extracted using the Term Frequency-Inverse Term Frequency (TF-IDF) method. The collected keywords will be mapped in a mixed ontology tree that refers to the Association of Computing Machinery Computing Classification System 2012 (ACM CCS 2012) and Computer Science Ontology (CSO) as ontology standards in computer science. The skyline query algorithm for determining skyline documents is Block Nested Loop (BNL). The evaluation method uses the skyline ratio of each aggregate function in the CDSS. Based on the ratio value, CDSS using the maximum DEWP has the most relevant skyline results compared to the average DEWP and minimum DEWP.

Attention–Survival Score: A Metric to Choose Better Keywords and Improve Visibility of Information

In this paper, we propose a method to aid authors in choosing alternative keywords that help their papers gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, simultaneously, be keywords with fewer competitors. The competitors are derived from other papers containing the same keywords. Having fewer competitors would allow an author’s paper to have a higher consult frequency. In order to recommend keywords, we must first determine an attention–survival score. The attention score is obtained using the popularity of a keyword. The survival score is derived from the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high attention–survival score. We used ontologies to ensure that alternative keywords proposed by our method are semantically related to the original authors’ keywords that they wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative and input keywords. To test the sensibility of the ontology, we used two sources: WordNet and the Computer Science Ontology (CSO). Finally, we launched a survey for the human validation of our algorithm using keywords from Web of Science papers and three ontologies: WordNet, CSO, and DBpedia. We obtained good results from all our tests.

Trend analysis and forecasting of publication activities by Indian computer science researchers during the period of 2010–23

AbstractHuge collections of published research documents are available in various repositories in Indian universities and research organizations. The efficient retrieval, estimation of research trends, and identification of research gaps with the global trend in different research areas, can be a great guiding tool for regulating research in the appropriate direction. This research attempts to analyse the trend of research activities carried out by Indian researchers in the discipline of Computer Science from 2010 to 19 and forecast the upcoming research trend for the years 2020–23. A repository of the abstracts based on domains given in the Computer Science Ontology (CSO) published by the Indian researchers is developed from the Scopus database. Document Index Graph document representation model is used to store the repository, shared phrases across the documents are extracted and phrase‐based similarity is computed. Combining the Single‐term and Phrase‐based similarity, the hybrid similarity is generated and similar documents are clustered using the DBSCAN clustering technique. Topics are identified for each cluster using the Latent Dirichlet Allocation algorithm and are automatically labelled using CSO. For each topic, the trend analysis and forecasting have been done using the Auto‐Regressive Integrated Moving Average. For the assessment of the forecasting performance, the dataset from 2010 to 17 is used as a training dataset and 2018–19 as a testing dataset. The average forecasting for the year 2018 for all CSO domains belongs to the Good forecasting category with Mean Absolute Percentage Error (MAPE) 18.34, and 2019 shows reasonable forecasting with MAPE 30.20 as per the MAPE interpretation given by Lewis. For each topic the average forecasting for years 2018–19 shows either Highly accurate, Good or Reasonable forecasting. As a result, the top four domains for the years 2020–23 are also identified which can help initial researchers in the identification of a relevant topic for research and exploration.

The AIDA Dashboard: A Web Application for Assessing and Comparing Scientific Conferences

Scientific conferences are essential for developing active research communities, promoting the cross-pollination of ideas and technologies, bridging between academia and industry, and disseminating new findings. Analyzing and monitoring scientific conferences is thus crucial for all users who need to take informed decisions in this space. However, scholarly search engines and bibliometric applications only provide a limited set of analytics for assessing research conferences, preventing us from performing a comprehensive analysis of these events. In this paper, we introduce the AIDA Dashboard, a novel web application, developed in collaboration with Springer Nature, for analyzing and comparing scientific conferences. This tool introduces three major new features: 1) it enables users to easily compare conferences within specific fields (e.g., Digital Libraries) and time-frames (e.g., the last five years); 2) it characterises conferences according to a 14K research topics from the Computer Science Ontology (CSO); and 3) it provides several functionalities for assessing the involvement of commercial organizations, including the ability to characterize industrial contributions according to 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). We evaluated the AIDA Dashboard by performing both a quantitative evaluation and a user study, obtaining excellent results in terms of quality of the analytics and usability.

AIDA: A knowledge graph about research dynamics in academia and industry

AbstractAcademia and industry share a complex, multifaceted, and symbiotic relationship. Analyzing the knowledge flow between them, understanding which directions have the biggest potential, and discovering the best strategies to harmonize their efforts is a critical task for several stakeholders. Research publications and patents are an ideal medium to analyze this space, but current data sets of scholarly data cannot be used for such a purpose because they lack a high-quality characterization of the relevant research topics and industrial sectors. In this paper, we introduce the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21 million publications and 8 million patents according to the research topics drawn from the Computer Science Ontology. 5.1 million publications and 5.6 million patents are further characterized according to the type of the author’s affiliations and 66 industrial sectors from the proposed Industrial Sectors Ontology (INDUSO). AIDA was generated by an automatic pipeline that integrates data from Microsoft Academic Graph, Dimensions, DBpedia, the Computer Science Ontology, and the Global Research Identifier Database. It is publicly available under CC BY 4.0 and can be downloaded as a dump or queried via a triplestore. We evaluated the different parts of the generation pipeline on a manually crafted gold standard yielding competitive results.

New trends in scientific knowledge graphs and research impact assessment

New trends in scientific knowledge graphs and research impact assessment

CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

Classifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

Searching relevant papers for software engineering secondary studies: Semantic Scholar coverage and identification role

Searching relevant papers is a fundamental task for the elaboration of secondary studies. This task is known to be tedious and time-consuming when it is made manually, especially with the presence of several academic repositories. Recently, Semantic Scholar has emerged as a new artificial intelligence-based search engine enabling a set of valuable features. The present study investigates the role of Semantic Scholar in retrieving relevant papers for performing secondary studies in software engineering. For this sake, an examination is performed to check the ability of Semantic Scholar to locate included papers in recent and well-established secondary studies. Afterwards, a hybrid and automatic search strategy is introduced making use of Semantic Scholar as a sole search engine and it incorporates: automatic search, snowballing, and use of Computer Science Ontology (CSO) and Software Engineering Body of Knowledge (SWEBOK) for refining queries. The proposed strategy is validated by replicating the search of high-quality secondary studies in the software engineering field. To guarantee objectivity, a systematic search is conducted of recent secondary studies published in the field since 2015. For the coverage test, Semantic Scholar is examined to locate primary papers of selected secondary studies and identify missing venues. The proposed search strategy is used to check the ability to retrieve primary papers of each secondary study. The systematic search yielded 20 high-quality secondary studies with 1337 distinct primary papers. The coverage test revealed that Semantic Scholar covers 98.88% of the papers. The proposed search strategy enabled the full replication of 13 studies and more than 90% for the 7 remaining studies.

Automating Computer Science Ontology Extension With Classification Techniques

In information technology, an ontology is a knowledge structure consisting of the definitions and relations of information within one or even multiple domains. This semantically represented information is helpful for tasks such as document classification and item recommendation in recommender systems. However, as big data prevails, manually extending existing ontologies with up-to-date terminologies becomes challenging due to the tedious and time-consuming process and the expensive cost of expert manual labor. This study aims to achieve a fully automatic ontology extension.We propose a novel "Direct" approach for extending an existing Computer Science Ontology (CSO). This approach consists of two steps: initially extending the CSO with new topics and using this extended graph to obtain the new topic’s node embeddings as inputs for training classifiers. However, this initial extension still contains many noisy links; therefore, the classifier later acts as a filter and a link predictor. We experiment with various traditional machine learning and recent deep learning models and then compare them using our Direct approach. We also propose two evaluation procedures to decide the best-performing model and approach: the novel Wikipedia-based F1w score and the total number of resulting links. Furthermore, manual evaluation by four human experts is conducted to conclude the reliability of our proposed approach and evaluation procedure. This study concludes that the Direct approach’s Gaussian Naive Bayes model produces the most valid and reliable links, and we, therefore, use it to further extend the CSO with hundreds of new CS topics and links.

An Advanced Analysis of Cloud Computing Concepts Based on the Computer Science Ontology

Our primary research hypothesis stands on a simple idea: The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics. And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago. We implemented our model based on Computer Science Ontology (CSO) and analyzed 44 years of publications. Then we derived the most important concepts related to Cloud Computing (CC) from the scientific collection offered by Clarivate Analytics. Our methodology includes data extraction using advanced web crawling techniques, data preparation, statistical data analysis, and graphical representations. We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology. Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.

Automated subject indexing using word embeddings and controlled vocabularies: a comparative study

Text mining methods contribute significantly to the understanding and the management of digital content, increasing the potential of entry links. This paper introduces a method for subject analysis combining topic modelling and automated labelling of the generated topics exploiting terms from existing knowledge organisation systems. A testbed was developed in which the Latent Dirichlet Allocation (LDA) algorithm was deployed for modelling the topics of a corpus of papers related to the Digital Library Evaluation domain. The generated topics were represented in the form of bags-of-words word embeddings and were utilised for retrieving terms from the EuroVoc Thesaurus and the Computer Science Ontology (CSO). The results of this study show that the domain of DL can be described with different vocabularies, but during the process of automatic labelling the context needs to be taken into account.

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

Ontologies of research areas are important tools for characterizing, exploring, and analyzing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a Web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualize sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.

Explication of Computer Science Ontology with DSpace

Ontology is the taxonomy representation of the Knowledge which enhances the capability of the representation of the data and makes data in machine readable format. It is the need of Semantic Web Technologies. Now a day’s many of the organizations want to be the part of it. Authors provide a survey of Ontology making, integration with the DSpace and its uses. The paper contributes as follows: 1) an overview of semantic technology and ontology 2) Ontology development and uses 3) Investigation of the concepts and relationships for Computer Science Domain Ontology 4) Integration and deployment of Computer Science Ontology with the DSpace. 5) Searching and filtering the results after querying the Ontology.

System Entity Structure Ontology Toolbox for MATLAB/Simulink: Used for Variant Modelling

In computer science ontology is often understood as a formal specifi cation of a shared conceptualization in form of a model. The considered doma in of conceptualiza tion is mod eling and simulation of modular, hierarchical systems. In this context, the System Entity Structure (SES) Ontology has been introduced for specifying a set of various system structure s or parameter settings. In combination with configurable dynamic models, organized in a model base (MB), the SES ontology has been advanced to a modeling and simulation framework. Based on the original SES elements and axioms as well as the introduction of some new features, the impleme ntation of an SES toolbox for MATLAB/Simulink is discussed. Subsequently, the usage of the toolbox is illustrated by a well known example of variant modeling.

Ontological research and its applications to the biomedical domain

AbstractOntology is an area of philosophy “the science of what is, of the kinds and structures of objects, properties, events and relations in every area of reality” (Smith & Welty, 2001). In computer and information science ontology is used to represent the common understanding and concepts related to a specific domain. In this sense, an ontology includes a set of generic concepts as well as their definitions and relationships. The main purpose of using an ontology is to present the shared view of the knowledge of a domain. Other advantages of ontologies are that they provide computer systems with a way to specify concepts, allow interoperability and reusability, and ultimately represent a way to relate the concepts expressed in our computer information systems with the real objects that they represent. This panel will present a general overview of the current issues on ontological research its applications in biomedicine. The panel includes presentations on the state‐of‐the‐art in ontology research as well as several applications in the biomedical domain.

MECUREO ontology and modelling tools

This paper describes MECUREO, a system that automatically constructs an ontology from a dictionary. We describe its use in the context of a dictionary of computer science. MECUREO parses this to construct a graph representing a lightweight ontology of computer science. The paper describes MECUREO's approach to parsing the dictionary and the construction of the ontology graph. We also describe MECUREO's point query facility. This generates a small ontology for use in a particular teaching context, such as single lesson. We report two evaluations of the effectiveness of the ontology construction, one based on a qualitative concept mapping experiment, and the other, on a quantitative comparison of the computer science ontology with another similar ontology.