Candidate Keywords Research Articles

NG_MDERANK: A software vulnerability feature knowledge extraction method based on N‐gram similarity

AbstractAs software grows in size and complexity, software vulnerabilities are increasing, leading to a range of serious insecurity issues. Open‐source software vulnerability reports and documentation can provide researchers with great convenience for analysis and detection. However, the quality of different data sources varies, the data are duplicated and lack of correlation, which often requires a lot of manual management and analysis. In order to solve the problems of scattered and heterogeneous data and lack of correlation in traditional vulnerability repositories, this paper proposes a software vulnerability feature knowledge extraction method that combines the N‐gram model and mask similarity. The method generates mask text data based on the extraction of N‐gram candidate keywords and extracts vulnerability feature knowledge by calculating the similarity of mask text. This method analyzes the samples efficiently and stably in the environment of large sample size and complex samples and can obtain high‐value semi‐structured data. Then, the final node, relationship, and attribute information are obtained by secondary knowledge cleaning and extraction of the extracted semi‐structured data results. And based on the extraction results, the corresponding software vulnerability domain knowledge graph is constructed to deeply explore the semantic information features and entity relationships of vulnerabilities, which can help to efficiently study software security problems and solve vulnerability problems. The effectiveness and superiority of the proposed method is verified by comparing it with several traditional keyword extraction algorithms on Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) vulnerability data.

Public-Key Authenticated Encryption with Keyword Search Made Easy

Public-key searchable encryption allows keyword-associated tokens to be used to test if a ciphertext contains specific keywords. Due to the low entropies of keywords, the token holder can create ciphertexts from candidate keywords and test them using the token in hand to recover the keywords, known as inside keyword guessing attacks (IKGA). Public-key authenticated encryption with keyword search is a searchable encryption proposed to defend against such attacks. It ensures the sender's private key protects the ciphertexts from the IKGA. PAEKS schemes with reasonable security and practical efficiency remain elusive despite many proposals. This work provides a simple generic PAEKS scheme from non-interactive key exchange (NIKE) and symmetric-key equality-predicate encryption with three new constructions for the latter, respectively from pseudorandom functions (PRFs), the decision bilinear Diffie-Hellman assumption, and the learning-with-errors assumption. Instantiating our generic scheme, we derive several PAEKS schemes from the most well-known assumptions, with some of them achieving full cipher-keyword indistinguishability and full token indistinguishability in the standard model, for the first time. Our instantiated schemes allow practical implementations and outperform the existing PAEKS schemes under the same assumptions.

Keyphrase Extraction Using TextRank for Indonesian Text

Keywords are commonly used as a form of summary from scientific publications. But in determining keywords, it requires expertise in the related field and a long amount of time because you have to read and understand the entire contents of scientific publications. Keyphrase Extraction can be a solution to get relevant keywords in a short time based on titles and abstracts from scientific publications. TextRank method is used to extract keywords. This research will perform keyword extraction using the TextRank method for Indonesian text. The evaluation results of this study showed an accuracy value of 95.53% and an f1-score of 59.32% with a threshold configuration of 80% and using all keyword candidates.

General-purpose sensor message parser using recurrent neural networks with stack memory

The growth in the usage of the Internet of Things (IoT) has resulted in the deployment of diverse networks. However, the multiple networking interfaces and embedded protocols pose a significant challenge to communication compatibility. To tackle this problem and establish a flexible networking framework, we propose the implementation of a general-purpose message parser utilizing a recurrent neural network model with stack memory (RNN-SM). This parser has the ability to extract crucial keywords from the various communication network messages, which are trained on multiple network protocol specifications. During the training phase, the RNN-SM predicts candidate keywords and cross-references them with predefined keywords in an expandable dictionary, thus improving the accuracy of keyword extraction. Additionally, we have introduced the concept of minimum prediction fork level as a hyperparameter to balance the simplicity and flexibility of the RNN-SM. The proposed parser proves to be an effective solution in facilitating smooth communication between multiple devices and also has the added benefit of filtering out noise. The RNN-SM's robust keyword extraction capability holds up even in noisy environments, making it a reliable solution for the compatibility challenges posed by the IoT.

SkyWords: An automatic keyword extraction system based on the skyline operator and semantic similarity

This study presents a hybrid keyword extraction method called SkyWords. It implements a novel supervised step based on the skyline operator and the majority voting principle for high-quality candidate keyword selection and an unsupervised step based on contextual semantics for the candidate keyword ranking. To achieve this, firstly, we build a feature vector database using the features of known keywords and then apply the skyline operator to retrieve the dominating feature vectors. To select the candidate keywords of a document, we compare each word of the document against the dominating feature vectors and assume the words that are as good as the majority of the dominating feature vectors as candidate keywords. To obtain the final set of keywords, we rank the candidate keywords based on their semantic similarity to the document based on their vector representation using the MPNet sentence transformer. We conducted experiments on six benchmark scholarly datasets to evaluate the performance of SkyWords and compared the results against eleven baseline keyword extraction systems. The experimental results show that the proposed novel keyword selection algorithm reduced the number of candidate keywords by several folds. Moreover, SkyWords achieved statistically significant improvements over the baseline methods in precision, recall, and F1 score. Compared to the baseline regarding ranking-based metrics, SkyWords achieved the highest mean average precision score for all datasets and the highest mean reciprocal rank score for all datasets but one. Furthermore, SkyWords extracted more relevant keywords than the baseline methods.

Peer recommendation by using pattern mining to generate candidate keywords in attributed graphs

Peer recommendation by using pattern mining to generate candidate keywords in attributed graphs

Key Phrases Extraction Method Based on a New Ranking Function

A new key phrases extraction method based on statistical approaches and contextual embedding models is proposed. The proposed method identifies the main candidate keywords for a document based on their frequencies and then weights them based on their similarity values to the document. The frequency of each type of n-gram is calculated independently to ensure fair competition between them at the ranking stage. In addition, to assess the importance of a candidate keyword in a document, context-based embeddings are used to represent both the document and its candidate keywords in preparation for assessing their similarity. To prevent bias in similarity values in long keywords, a function has been introduced to normalize similarity scores of each candidate based on its length. The final score for the importance of a keyword is determined by a score function that returns the harmonic mean of the frequency and the similarity. The proposed method is tested using five different benchmark datasets against several state-of-art baselines including TF-IDF, TextRank, and Yake. The proposed method outperforms all baselines in terms of the MAP@K ranking metric.

MGRank: A keyword extraction system based on multigraph GoW model and novel edge weighting procedure

Keyword extraction is the process of extracting the most descriptive words from a textual document. State-of-the-art graph-based keyword extraction systems generally represent text documents using a simple graph called a graph-of-words (GoW), based on the sliding window concept. This representation of a text document requires determining a proper window size, models the document on a local scale, and allows the establishment of a single relation between two candidate keywords. In this study, we address these problems and propose a keyword extraction system called MGRank which uses a complete multigraph structure to build a GoW model to represent a text document. The completeness property of the proposed GoW model provides a means to represent a document globally and eliminates the need to determine the window-size parameter. Parallel edges allow the establishment of multiple relations between candidate keywords. In this study, we also propose a new edge-weighting procedure based on the positional distance of candidate keywords. To evaluate the performance of MGRank, we performed experiments on seven benchmark datasets and compared the results with those of six baseline methods. The experimental results show that MGRank outperforms the baseline methods statistically in precision, recall, and F1-score in almost all cases. In terms of mean average precision and mean reciprocal rank, MGRank performs statistically better than node ranking-based and statistical baseline methods and achieves on-par results with topic-based baseline methods. Furthermore, the experimental results showed that MGRank extracted the most relevant keywords.

Intelligent Video Tag Recommendation System Improving Video Popularity

A successful marketing activity requires attracting high social popularity to their contents, since higher popularity usually indicates stronger influence, more fame and higher revenue. In this, we focus on the question of how to improve popularity of videos sharing on websites like YouTube in mobile computing environment. Obviously, composing high quality titles and tags is beneficial for viewers to discover videos of their interests and increase their tendency to watch more videos. However, it is not an easy task for uploaders,which is especially true since the screen is tight for most mobile devices. To this end, this proposes a novel hybrid method based on multi-modal content analysis that recommends keywords for video uploaders to compose titles and tags of their videos and then to gain higher popularity. The method generates candidate keywords by integrating techniques of textual semantic analysis of original tags and recognition of video content. On one hand, taking the original keywords of a video as input, the method obtains most relevant words from WordNet and related video titles gathered from the three top video sharing sites (YouTube, Yahoo Video, Bing Video). On the other hand, through recognizing video content with deep learning technology,the method extracts the entity name of video content as candidate keywords. Finally, a TFSIM algorithm is proposed to rank the candidate keywords and the most relevant keywords are recommended to uploaders for optimizing the titles and tags of their videos

Verifiable Searchable Encryption Framework Against Insider Keyword-Guessing Attack in Cloud Storage

Searchable encryption (SE) allows cloud tenants to retrieve encrypted data while preserving data confidentiality securely. Many SE solutions have been designed to improve efficiency and security, but most of them are still susceptible to insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. Also in existing SE solutions, a semi-honest-but-curious cloud server may deliver incorrect search results by performing only a fraction of retrieval operations honestly (e.g., to save storage space). To address these two challenging issues, we first construct the basic Verifiable SE Framework (VSEF), which can withstand the inside KGA and achieve verifiable searchability. Based on the basic VSEF, we then present the enhanced VSEF to support multi-keyword search, multi-key encryption and dynamic updates (e.g., data modification, data insertion, and data deletion) at the same time, which highlights the importance of practicability and scalability of SE in real-world application scenarios. We conduct extensive experiments using the Enron email dataset to demonstrate that the enhanced VSEF achieves high efficiency while resisting to the inside KGA and supporting the verifiability of search results.

The Use of Artificial Intelligence in Literature Search and Selection of the PubMed Database

Background. A vast number of research papers are published every day on PubMed, making it difficult for scientists to retrieve relevant articles in a timely manner. Keyword-based searches are currently the most popular method, but determining a suitable set of keywords can be challenging. Moreover, searches based on keywords typically retrieve many irrelevant papers. We developed a natural language processing (NLP)-based keyword augmentation and screening (NKAS) method to help scientists easily refine their keywords in topic searches. This method can extract meaningful candidate keywords from the titles and abstracts of an initial search using prior knowledge, knowledge graphs, and machine learning. The method was tested on three atrial fibrillation topics. When the NKAS was applied, the number of remaining papers was less than those in the original search but showed much higher precision (73.83% vs. 34.6%) and recall (98.4% vs. 59.93%) compared with those of the original search results. In conclusion, the NKAS method showed that NLP and other artificial intelligence techniques can help enhance both the search comprehensiveness and accuracy. These results suggest a great potential for the application of artificial intelligence methods in medical publication searches and other text-based applications.

A Method of Domain Dictionary Construction for Electric Vehicles Disassembly.

Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. Herein, the construction of a domain dictionary for the disassembly of electric vehicles is a research work that has important research significance. Extracting high-quality keywords from text and categorizing them widely uses information mining, which is the basis of named entity recognition, relation extraction, knowledge questions and answers and other disassembly domain information recognition and extraction. In this paper, we propose a supervised learning dictionary construction algorithm based on multi-dimensional features that combines different features of extraction candidate keywords from the text of each scientific study. Keywords recognition is regarded as a binary classification problem using the LightGBM model to filter each keyword, and then expand the domain dictionary based on the pointwise mutual information value between keywords and its category. Here, we make use of Chinese disassembly manuals, patents and papers in order to establish a general corpus about the disassembly information and then use our model to mine the disassembly parts, disassembly tools, disassembly methods, disassembly process, and other categories of disassembly keywords. The experiment evidenced that our algorithms can significantly improve extraction and category performance better than traditional algorithms in the disassembly domain. We also investigated the performance algorithms and attempts to describe them. Our work sets a benchmark for domain dictionary construction in the field of disassembly of electric vehicles that is based on the newly developed dataset using a multi-class terminology classification.

TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction.

When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with word vector clustering, RDD-WRank, was proposed. Firstly, the algorithm uses rough data reasoning to mine the association between candidate keywords, expands the search scope, and makes the results more comprehensive. Then, based on Wikipedia online open knowledge base, word embedding technology is used to integrate Word2Vec into the improved algorithm, and the word vector of TextRank lexical graph nodes is clustered to adjust the voting importance of nodes in the cluster. Compared with the traditional TextRank algorithm and the Word2Vec algorithm combined with TextRank, the experimental results show that the improved algorithm has significantly improved the extraction accuracy, which proves that the idea of using rough data reasoning can effectively improve the performance of the algorithm to extract keywords.

Effective Natural Language Processing and Interpretable Machine Learning for Structuring CT Liver-Tumor Reports

In the past, the liver tumors were reported manually in an unstructured format. There actually exists much valuable knowledge in these reports for further disease risk assessment, disease recognition and treatment recommendation. Yet, it is not easy to read and mine knowledge from the unstructured reports. Hence, how to extract the knowledge from these biomedical reports effectively and efficiently has been a challenging issue in the past decades. Although a set of Natural Language Processing techniques were proposed for Bio-medical information retrieval, few related works were made on transforming the unstructured CT liver-tumor reports into structured ones. To aim at this issue, in this paper, we propose a two-stage report structuring method by integrating effective Natural Language Processing (NLP) and interpretable machine learning. For the first stage, the candidate keywords in unstructured reports are extracted. Next, the feature keywords are determined by the feature-selection technique. For the second stage, the well-known multi-classifiers are performed, and finally the reports are labeled in a refined structure format. Further, the factor keywords in the classification model are filtered to interpret the performance. In overall, the proposed report structuring method generates a hierarchical data structure, including the common features and refined features in the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> levels/stages, respectively. To reveal the performance of proposed method, a set of evaluations were conducted and the results show that, the proposed method is more promising than the fashion neural networks such as Bert (Bidirectional Encoder Representations from Transformers) in terms of effectiveness and efficiency.

Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Keyword extraction by Term frequency-Inverse document frequency (TF-IDF) is used for text information retrieval and mining in many domains, such as news text, social contact text, and medical text. However, keyword extraction in special domains still needs to be improved and optimized, particularly in the scientific research field. The traditional TF-IDF algorithm considers only the word frequency in documents, but not the domain characteristics. Therefore, we propose the Scientific research project TF-IDF (SRP-TF-IDF) model, which combines TF-IDF with a weight balance algorithm designed to recalculate candidate keywords. We have implemented the SRP-TF-IDF model and verified that our method has better precision, recall, and F1 score than the traditional TF-IDF and TextRank methods. In addition, we investigated the parameter of our weight balance algorithm to find an optimal value for keyword extraction from scientific research projects.

GKEEP: An Enhanced Graph‐Based Keyword Extractor With Error‐Feedback Propagation for Geoscience Reports

AbstractAs the amount of published geoscience literature grows, reading and summarizing texts of large collections has become a challenging task. Publication keywords can be considered basic components of knowledge structure representations and have been used to reveal knowledge concerning research domains. In contrast to data used in other research domains, the works on textual geoscience data that entail keyword extraction are limited. In this paper, we propose an unsupervised algorithm, the graph‐based keyword extractor with error‐feedback propagation (GKEEP), that enhances graph‐based keyword extraction approaches by using an error‐feedback mechanism similar to the concept of backpropagation. The proposed approach comprises the following steps. A preprocessed document is used as the input of the proposed model and is represented as a weighted undirected graph, where the vertices represent words and the edges represent the cooccurrence relationship between the words constrained by a window size. Subsequently, its nodes are ranked by their importance scores calculated by a graph‐based ranking algorithm. Consequently, all the words have their own scores, and they are used to compute the scores of keyword candidates. Subsequently, the Word2Vec method is applied to recalculate the scores of keyword candidates and rank the keyword candidates to select the final keyword. It also utilizes error feedback to boost the rankings of the most salient terms that would otherwise be deemed less important. With empirical experiments on two real data sets (including our newly built data set), the proposed GKEEP model outperforms state‐of‐the‐art unsupervised models and the existing graph‐based ranking models. The proposed method can effectively reflect intrinsic keyword semantics and interrelationships.

On the Security of Verifiable Searchable Encryption Schemes

With cloud services, data users can retrieve encrypted data while preserving data confidentiality. However, this new paradigm suffers from many security concerns. A major concern is how to avoid insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. To address this issue, recently, two verifiable searchable encryption schemes (published in IEEE Transactions on Cloud Computing, doi: 10.1109/TCC.2020.2989296) in cloud storage were proposed which enjoys many desirable features. In this letter, we demonstrate that the schemes are insecure against insider keyword-guessing attack. Specifically, we show that the adversary can derive the keywords in an off-line manner.

Predicting Bug Priority Using Topic Modelling in Imbalanced Learning Environments

Manual classification of bug reports is time-consuming as the reports are received in large quantities. Alternatively, this project proposed automatic bug prediction models to classify the bug reports. The topics or the candidate keywords are mined from the developer description in bug reports using RAKE algorithm and converted into attributes. These attributes together with the target attribute—priority level—construct the training datasets. Naïve Bayes, logistic regression, and decision tree learner algorithms are trained, and the prediction quality was measured using area under recursive operative characteristics curves (AUC) as AUC does not consider the biasness in datasets. The logistics regression model outperforms the other two models providing the accuracy of 0.86 AUC whereas the naïve Bayes and the decision tree learner recorded 0.79 AUC and 0.81 AUC, respectively. The bugs can be classified without developer involvement and logistic regression is also a potential candidate as naïve Bayes for bug classification.

FLAKE: Fuzzy Graph Centrality-based Automatic Keyword Extraction

Abstract Keyword extraction is one of the most important aspects of text mining. Keywords help in identifying the document context. Many researchers have contributed their work to keyword extraction. They proposed approaches based on the frequency of occurrence, the position of words or the similarity between two terms. However, these approaches have shown shortcomings. In this paper, we propose a method that tries to overcome some of these shortcomings and present a new algorithm whose efficiency has been evaluated against widely used benchmarks. It is found from the analysis of standard datasets that the position of word in the document plays an important role in the identification of keywords. In this paper, a fuzzy logic-based automatic keyword extraction (FLAKE) method is proposed. FLAKE assigns weights to the keywords by considering the relative position of each word in the entire document as well as in the sentence coupled with the total occurrences of that word in the document. Based on the above data, candidate keywords are selected. Using WordNet, a fuzzy graph is constructed whose nodes represent candidate keywords. At this point, the most important nodes (based on fuzzy graph centrality measures) are identified. Those important nodes are selected as final keywords. The experiments conducted on various datasets show that proposed approach outperforms other keyword extraction methodologies by enhancing precision and recall.

NER‐RAKE: An improved rapid automatic keyword extraction method for scientific literatures based on named entity recognition

AbstractThe proposing of an accurate and efficient model for automatic keyword extraction of scientific literature is conducive to promoting the development of academic text mining research, such as literature retrieval optimization, research topic discovery, topic evolution analysis, emerging trend detection and so on, with the continuous expansion of digital academic resources. A keyword extraction method is proposed called NER‐RAKE which combines Named Entity Recognition (NER) process with Rapid automatic keyword extraction (RAKE), Bidirectional Long Short‐Term Memory Network Conditional Random Field (BiLSTM‐CRF) is used to recognize the domain entities in the scientific literature so as to enrich the list of candidate keywords divided by RAKE, and the frequency threshold is set to ensure the validity of candidate keywords. NER‐RAKE is better than RAKE in accuracy and recall rate through the keyword extraction experiment in the computer science field. It is effective to combine the process of NER with RAKE, which can improve the efficiency of keyword extraction.