Search results

Large collections of patent documents disclosing novel, non-obvious technologies are publicly available and beneficial to academia and industries. To maximally exploit its potential, searching these patent documents has increasingly become an important topic. Although much research has processed a large size of collections, a few studies have attempted to integrate both patent classifications and specifications for analyzing user queries. Consequently, the queries are often insufficiently analyzed for improving the accuracy of search results. This paper aims to address such limitation by exploiting semantic relationships between patent contents and their classification.

The contributions are fourfold. First, the authors enhance similarity measurement between two short sentences and make it 20 per cent more accurate. Second, the Graph-embedded Tree ontology is enriched by integrating both patent documents and classification scheme. Third, the ontology does not rely on rule-based method or text matching; instead, an heuristic meaning comparison to extract semantic relationships between concepts is applied. Finally, the patent search approach uses the ontology effectively with the results sorted based on their most common order.

The experiment on searching for 600 patent documents in the field of Logistics brings better 15 per cent in terms of F-Measure when compared with traditional approaches.

The research, however, still requires improvement in which the terms and phrases extracted by Noun and Noun phrases making less sense in some aspect and thus might not result in high accuracy. The large collection of extracted relationships could be further optimized for its conciseness. In addition, parallel processing such as Map-Reduce could be further used to improve the search processing performance.

The experimental results could be used for scientists and technologists to search for novel, non-obvious technologies in the patents.

High quality of patent search results will reduce the patent infringement.

The proposed ontology is semantically enriched by integrating both patent documents and their classification. This ontology facilitates the analysis of the user queries for enhancing the accuracy of the patent search results.

The purpose of this paper is to obtain more accurate matching between the request and the release of web service.

This paper adopts Levenshtein distance algorithm to calculate the name similarity between publishing service and request service, employs cosine theorem to compute the text similarity, and uses semantic distance to count the input‐output similarity, then filters out the low similarity and bad reputation services to structure the candidate service set.

The qualitative and quantitative analysis of the scheme is given in this paper. The experimental results show that the multi‐level matching filtering algorithm can obviously improve the recall ratio and precision ratio of web service discovery.

This paper proposes a similarity‐based filtering algorithm for multi‐level matching.

This paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.

A design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.

The study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.

The originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

Contractors must check the provisions that may cause disputes in the specifications to manage project risks when bidding for a construction project. However, since the specification is mainly written regarding many national standards, determining which standard each section of the specification is derived from and whether the content is appropriate for the local site is a labor-intensive task. To develop an automatic reference section identification model that helps complete the specification review process in short bidding steps, the authors proposed a framework that integrates rules and machine learning algorithms.

The study begins by collecting 7,795 sections from construction specifications and the national standards from different countries. Then, the collected sections were retrieved for similar section pairs with syntactic rules generated by the construction domain knowledge. Finally, to improve the reliability and expandability of the section paring, the authors built a deep structured semantic model that increases the cosine similarity between documents dealing with the same topic by learning human-labeled similarity information.

The integrated model developed in this study showed 0.812, 0.898, and 0.923 levels of performance in NDCG@1, NDCG@5, and NDCG@10, respectively, confirming that the model can adequately select document candidates that require comparative analysis of clauses for practitioners.

The results contribute to more efficient and objective identification of potential disputes within the specifications by automatically providing practitioners with the reference section most relevant to the analysis target section.

This paper aims to apply vector space model (VSM)-PCR model to compute the similarity of Fault zone ontology semantics, which verified the feasibility and effectiveness of the application of VSM-PCR method in uncertainty mapping of ontologies.

The authors first define the concept of uncertainty ontology and then propose the method of ontology mapping. The proposed method fully considers the properties of ontology in measuring the similarity of concept. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM and uses membership degree or correlation to express the level of uncertainty.

It provides a relatively better accuracy which verified the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.

The future work will focus on exploring the similarity measure and combinational methods in every dimension.

This paper presents an uncertain mapping method of ontology concept based on three-dimensional combination weighted VSM, namely, VSM-PCR. It expands the single VSM of concept meaning or instance set to the “meaning, properties, instance” three-dimensional VSM. The model uses membership degree or correlation which is used to express the degree of uncertainty; as a result, a three-dimensional VSM is obtained. The authors finally provide an example to verify the feasibility and effectiveness of VSM-PCR method in treating the uncertainty mapping of ontology.

Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue.

This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships.

The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored.

Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems.

This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

The purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.

This study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.

The semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.

This study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

The success of web services involved the adoption of this technology by different service providers through the web, which increased the number of web services, as a result making their discovery a tedious task. The UDDI standard has been proposed for web service publication and discovery. However, it lacks sufficient semantic description in the content of web services, which makes it difficult to find and compose suitable web services during the analysis, search, and matching processes. In addition, few works on semantic web services discovery take into account the user’s profile. The purpose of this paper is to optimize the web services discovery by reducing the search space and increasing the number of relevant services.

The authors propose a new approach for the semantic web services discovery based on the mobile agent, user profile and metadata catalog. In the approach, each user can be described by a profile which is represented in two dimensions: personal dimension and preferences dimension. The description of web service is based on two levels: metadata catalog and WSDL.

First, the semantic web services discovery reduces the number of relevant services through the application of matching algorithm “semantic match”. The result of this first matching restricts the search space at the level of UDDI registry, which allows the users to have good results for the “functional match”. Second, the use of mobile agents as a communication entity reduces the traffic on the network and the quantity of exchanged information. Finally, the integration of user profile in the service discovery process facilitates the expression of the user needs and makes intelligible the selected service.

To the best knowledge of the authors, this is the first attempt at implementing the mobile agent technology with the semantic web service technology.

Modern Enterprise Web Application development can exploit third-party software components, both internal and external to the enterprise, that provide access to huge and valuable data sets, tested by millions of users and often available as Web application programming interfaces (APIs). In this context, the developers have to select the right data services and might rely, to this purpose, on advanced techniques, based on functional and non-functional data service descriptive features. This paper focuses on this selection task where data service selection may be difficult because the developer has no control on services, and source reputation could be only partially known.

The proposed framework and methodology are apt to provide advanced search and ranking techniques by considering: lightweight data service descriptions, in terms of (semantic) tags and technical aspects; previously developed aggregations of data services, to use in the selection process of a service the past experiences with the services when used in similar applications; social relationships between developers (social network) and their credibility evaluations. This paper also discusses some experimental results regarding the plan to expand other experiments to check how developers feel using the approach.

In this paper, a data service selection framework that extends and specializes an existing one for Web APIs selection is presented. The revised multi-layered model for data services is discussed and proper metrics relying on it, meant for supporting the selection of data services in a context of Web application design, are introduced. Model and metrics take into account the network of social relationships between developers, to exploit them for estimating the importance that a developer assigns to other developers’ experience.

This research, with respect to the state of the art, focuses attention on developers’ social networks in an enterprise context, integrating the developers’ credibility assessment and implementing the social network-based data service selection on top of a rich framework based on a multi-perspective model for data services.

This study examines users' information selection strategy on knowledge-sharing platforms from the individual level, peer level and societal level. Though previous literature has explained these three levels separately, few have simultaneously examined their impacts and identified the dominant one according to their effect strengths. The study aims to fill this research gap of the competitions among different levels of information selection mechanisms. Besides, this study also proposes a three-step decision-tree approach to depict the consumption process, including the decision of first-time exposure, the decision of continuous consumption and the decision of feedback behavior participation.

This study analyzed a clickstream dataset of a Chinese information technology blogging site, CSDN.net. Employing a sequential logit model, it examined the impacts of self-level interest similarity, peer-level interest similarity and global popularity simultaneously on each turning point in the consumption process.

The authors’ findings indicate that self-level interest similarity is the most dominant factor influencing users to browse a knowledge-sharing blog, followed by peer-level interest similarity and then global popularity. All three mechanisms have consistent influences on decision-making in continuous information consumption. Surprisingly, the authors find self-level interest similarity negatively influences users to give feedback on knowledge-sharing blogs.

This paper fulfills the research gap of the dominance among three-levels of selection mechanisms. This study's findings not only could contribute to information consumption studies by providing theoretical insights on audience behavior patterns, but also help the industry advance its recommendation algorithm design and improve users' experience satisfaction.

Peer review – The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0475