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The purpose of this paper is to propose a three-dimensional (3D) assembly model retrieval method based on assembling semantic information to address semantic mismatches, poor accuracy and low efficiency in existing 3D assembly model retrieval methods.

The paper proposes an assembly model retrieval method. First, assembly information retrieval is performed, and 3D models that conform to the design intention of the assembly are found by retrieving the code. On this basis, because there are conjugate subgraphs between attributed adjacency graphs (AAG) that have an assembly relationship, the assembly model geometric retrieval is translated into a problem of finding AAGs with a conjugate subgraph. Finally, the frequent subgraph mining method is used to retrieve AAGs with conjugate subgraphs.

The method improved the efficiency and accuracy of assembly model retrieval.

The examples illustrate the specific retrieval process and verify the feasibility and reasonability of the assembly model retrieval method in practical applications.

The assembly model retrieval method in the paper is an original method. Compared with other methods, good results were obtained.

The purpose of this paper is to provide a detailed discussion for different types of graph queries and a different mechanism for indexing and querying graph databases.

The paper reviews the existing approaches and techniques for indexing and querying graph databases. For each approach, the strengths and weaknesses are discussed with particular emphasis on the target application domain. Based on an analysis of the state‐of‐the‐art of research literature, the paper provides insights for future research directions and untouched challenging research aspects.

Several graph indexing and querying techniques have been proposed in the literature. However, there is still a clear room for improvement and further research issues in that domain.

The paper identifies the advantages and disadvantages of the different graph indexing mechanisms and their suitability for different practical applications. The paper provides some guidelines and recommendations which are useful for future research in the area of graph databases.

The paper has practical implications for social networks, protein networks, chemical compounds, multimedia database, and semantic web.

The paper contributes to the implementation of an efficient indexing and querying mechanism for graph databases in different application domains.

Design is a time-consuming process for mechanical production. Some design structures frequently occur in different products and can be shared by multiple assembly models. Thus, identifying these structures and adding them to a design knowledge library significantly speed up the design process. Most studies addressing this issue have traditionally focused on part models and have not extended to assembly models. This paper aims to find a method for common design structure discovery in assembly models.

Computer-aided design models have a great deal of valuable information defined by different designers in the design stages, especially the assembly models, which are actually carriers of information from multiple sources. In this paper, an approach for discovering a common design structure in assembly models is proposed by comparing information from multiple sources. Assembly models are first represented as attribute connection graphs (ACGs), in which we mainly consider topological information and various attributes of parts and connections. Then, we apply a K-means clustering method based on a similarity analysis of different attributes to classify the parts and connections and transform ACGs of assemblies into type code graphs (TCGs). After this, a discovery algorithm that improves upon fast frequent subgraph mining is used to identify common design structures in assemblies.

A new method was developed for discovering common design structures in assembly models, considering the similarity of information from multiple sources and allowing some differences in the details to keep both commonalities and individualities of common design structures.

Experiments show that the proposed method is efficient and can produce a reasonable result.

This discovery method helps designers find common design structures from different assembly models and shorten the design cycle. It is an effective approach to build a knowledge library for product design that can shorten the design cycle.

The purpose of this paper is to present a generic pipeline for Resource Description Framework (RDF) graph mining to provide a comprehensive review of each step in the knowledge discovery from data process. The authors also investigate different approaches and combinations to extract feature vectors from RDF graphs to apply the clustering and theme identification tasks.

The proposed methodology comprises four steps. First, the authors generate several graph substructures (Walks, Set of Walks, Walks with backward and Set of Walks with backward). Second, the authors build neural language models to extract numerical vectors of the generated sequences by using word embedding techniques (Word2Vec and Doc2Vec) combined with term frequency-inverse document frequency (TF-IDF). Third, the authors use the well-known K-means algorithm to cluster the RDF graph. Finally, the authors extract the most relevant rdf:type from the grouped vertices to describe the semantics of each theme by generating the labels.

The experimental evaluation on the state of the art data sets (AIFB, BGS and Conference) shows that the combination of Set of Walks-with-backward with TF-IDF and Doc2vec techniques give excellent results. In fact, the clustering results reach more than 97% and 90% in terms of purity and F-measure, respectively. Concerning the theme identification, the results show that by using the same combination, the purity and F-measure criteria reach more than 90% for all the considered data sets.

The originality of this paper lies in two aspects: first, a new machine learning pipeline for RDF data is presented; second, an efficient process to identify and extract relevant graph substructures from an RDF graph is proposed. The proposed techniques were combined with different neural language models to improve the accuracy and relevance of the obtained feature vectors that will be fed to the clustering mechanism.

Social media is characterized by its volume, its speed of generation and its easy and open access; all this making it an important source of information that provides valuable insights. Content characteristics such as valence and emotions play an important role in the diffusion of information; in fact, emotions can shape virality of topics in social media. The purpose of this research is to fill the gap in event detection applied on online content by incorporating sentiment, more specifically strong sentiment, as main attribute in identifying relevant content.

The study proposes a methodology based on strong sentiment classification using machine learning and an advanced scoring technique.

The results show the following key findings: the proposed methodology is able to automatically capture trending topics and achieve better classification compared to state-of-the-art topic detection algorithms. In addition, the methodology is not context specific; it is able to successfully identify important events from various datasets within the context of politics, rallies, various news and real tragedies.

This study fills the gap of topic detection applied on online content by building on the assumption that important events trigger strong sentiment among the society. In addition, classic topic detection algorithms require tuning in terms of number of topics to search for. This methodology involves scoring the posts and, thus, does not require limiting the number topics; it also allows ordering the topics by relevance based on the value of the score.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2019-0373

In the context of information retrieval, text genre is as important as its content, and knowledge of the text genre enhances the search engine features by providing customized retrieval. The purpose of this study is to explore and evaluate the use of stylometric analysis, a quantitative analysis for the linguistics features of text, to support the task of automated text genre detection for Classical Arabic text.

Unsupervised clustering and supervised classification were applied on the King Saud University Corpus of Classical Arabic texts (KSUCCA) using the most frequent words in the corpus (MFWs) as stylometric features. Four popular distance measures established in stylometric research are evaluated for the genre detection task.

The results of the experiments show that stylometry-based genre clustering and classification align well with human-defined genre. The evidence suggests that genre style signals exist for Classical Arabic and can be used to support the task of automated genre detection.

This work targets the task of genre detection in Classical Arabic text using stylometric features, an approach that has only been previously applied to Arabic authorship attribution. The study also provides a comparison of four distance measures used in stylomtreic analysis on the KSUCCA, a corpus with over 50 million words of Classical Arabic using clustering and classification.

Digital library sampling is used to obtain a collection of random literature records from the backend database, which is a crucial issue for a variety of important purposes in many online digital library applications. Digital libraries can only be accessed through their query interfaces. The challenge is how to ensure the randomness of the sample via the autonomous query interface.

This paper presents an iterative and incremental approach to obtain samples through the query interface of a digital library. In the approach, a novel graph model, query-related graph, is proposed to transform the flat literature records into a graph structure, and samples are obtained iteratively by traveling the query-related graph. Besides query-related graph, the key components, query generation, termination condition and amending deviation, are also discussed in detail.

The extensive experiments over two real digital libraries, ISTIC and IEEE Xplore, show the proposed approach results in a better performance. First, the approach is very effective to obtain high-quality samples which are evaluated by the measure “sample deviation.” Second, the sampling process is very efficient by only submitting fewer random queries. Third, the approach is robust.

This sampling approach is limited by the query interfaces on a web page. In rare cases (<3 per cent), this approach cannot access query interfaces by sophisticated techniques.

Digital library sampling is very useful for a variety of important purposes: subject distribution analysis, literature quality evaluation, digital library size estimation, source selection in digital library integration and content freshness evaluation.

Myriads of online digital libraries can be accessed online. Digital library sampling is a useful way to understand digital libraries for many important applications.

Most of the attributes of a digital library query interface have infinite values, such as keyword attributes, which cannot be handled effectively by the existing sampling approaches.

The communication link in the engineering of Internet of Vehicle (IOV) is more frequent than the communication link in the Mobile ad hoc Network (MANET). Therefore, the highly dynamic network routing reliability problem is a research hotspot to be solved.

The graph theory is used to model the MANET communication diagram on the highway and propose a new reliable routing method for internet of vehicles based on graph theory.

The expanded graph theory can help capture the evolution characteristics of the network topology and predetermine the reliable route to promote quality of service (QoS) in the routing process. The program can find the most reliable route from source to the destination from the MANET graph theory.

The good performance of the proposed method is verified and compared with the related algorithms of the literature.

The purpose of this paper is to propose an approach for conceptualizing science based on collaboration and competences of researchers.

The research is conducted by exploratory analysis of collaboration and competences using case studies from humanistic, engineering, natural sciences and a general topic.

The findings show that by applying the proposed approach on bibliographic data that readily exist for many national sciences as well as for international scientific communities, one can obtain useful new insights into the research. The approach is demonstrated with the following exploratory findings: identification of important connections and individual researchers that connect the community of anthropologists; collaboration of technical scientists in the community of anthropologists caused by an interdisciplinary research project; connectivity, interdisciplinary and structure of artificial intelligence, nanotechnology and a community based on a general topic; and identifying research interest shift described with concretization and topic-shift.

As demonstrated with the practical implementation (http://scienceatlas.ijs.si/), users can obtain information of the most relevant competences of a researcher and his most important collaborators. It is possible to obtaining researchers, community structure and competences of an arbitrary research topic.

The map for collaboration and competences of a complete science can be a crucial tool for policy-making. Social scientists can use the results of the proposed approach to better understand and direct the development of science.

Originality and value of the paper is in combining text (competences) and network (research collaboration and co-authoring) approaches for exploring science. Additional values give the results of analysis that demonstrate the approach.

The paper addresses the problem of what drives the formation of latent discussion communities, if any, in the blogosphere: topical composition of posts or their authorship? The purpose of this paper is to contribute to the knowledge about structure of co-commenting.

The research is based on a dataset of 17,386 full text posts written by top 2,000 LiveJournal bloggers and over 520,000 comments that result in about 4.5 million edges in the network of co-commenting, where posts are vertices. The Louvain algorithm is used to detect communities of co-commenting. Cosine similarity and topic modeling based on latent Dirichlet allocation are applied to study topical coherence within these communities.

Bloggers unite into moderately manifest communities by commenting roughly the same sets of posts. The graph of co-commenting is sparse and connected by a minority of active non-top commenters. Communities are centered mainly around blog authors as opinion leaders and, to a lesser extent, around a shared topic or topics.

The research has to be replicated on other datasets with more thorough hand coding to ensure the reliability of results and to reveal average proportions of topic-centered communities.

Knowledge about factors around which co-commenting communities emerge, in particular clustered opinion leaders that often attract such communities, can be used by policy makers in marketing and/or political campaigning when individual leadership is not enough or not applicable.

The research contributes to the social studies of online communities. It is the first study of communities based on co-commenting that combines examination of the content of commented posts and their topics.