Ontology-based Semantic Search Research Articles

Mobilizing and Enhancing Legacy Biodiversity Data: The case of Karl Wilhelm Verhoeff's correspondence

A considerable amount of biological data is preserved as physical documents, the legacy of former explorers, collectors, researchers, and others. Mobilizing data from handwritten documents has been considered particularly challenging, with well-known cases such as the manual transcription of specimen labels and herbarium sheets by museum staff, or crowdsourced transcription of data card collections through online platforms. Here we present a pipeline of open-source software that can be used to automatically transcribe handwritten text, make it publicly available, annotate it with e.g., scientific names, extract names in Darwin Core Archive (DwC-A) for third-party reuse, and automatically recognize named entities in the machine-readable text. automatically transcribe handwritten text, make it publicly available, annotate it with e.g., scientific names, extract names in Darwin Core Archive (DwC-A) for third-party reuse, and automatically recognize named entities in the machine-readable text. We based our use case on the correspondence of the German zoologist Karl Wilhelm Verhoeff, related to the Myriapoda collection held at the Musée Zoologique de Strasbourg. The documents were processed with Transkribus (Muehlberger et al. 2019), a mostly open-source virtual research environment (OS VRE), which allows text in images to be converted into machine-readable text amenable to semantic enrichment. We achieved a character error rate as low as 5%, a remarkable result for handwritten material, as an accuracy higher than 95% for printed material is acceptable (Deutsche Forschungsgemeinschaft 2016). We then used Myriatrix (Martínez-Muñoz 2019), an instance of the Scratchpads OS VRE (Smith et al. 2011), to create bibliographic references, publish the full text, and annotate the correspondence with scientific names of myriapods. During the process, we added new scientific name spellings and combinations to the taxonomic backbone of Myriatrix and exported the full taxon classification in DwC-A via the Global Biodiversity Information Facility (GBIF) for reuse by the Global Names Architecture and its open-source tools (Patterson et al. 2016, Mozzherin et al. 2017). As a next step we are planning to subject the corrected text from Transkribus to a specific text-preprocessing workflow combining natural language processing (NLP) and machine learning (ML) techniques (Lücking et al. 2021). This includes, inter alia, a multiple annotation approach for general and bioscientific term classification in order to detect the respective entities automatically. The workflow has been developed in the framework of the Specialized Information Service Biodiversity Research (Koch et al. 2017) to make biodiversity information available via a customized and (bio-)ontology-based semantic search engine (Pachzelt et al. 2021). We recommend our comprehensive approach to natural history institutions seeking to efficiently digitize and mobilize the rich biological data present in their archival documents.

Ontology-Based Semantic Search Framework for Disparate Datasets

The public sector provides open data to create new opportunities, stimulate innovation, and implement new solutions that benefit academia and society. However, open data is usually available in large quantities and often lacks quality, accuracy, and completeness. It may be difficult to find the right data to analyze a target. There are many rich open data repositories, but they are difficult to understand and use because these data can only be used with a complex set of keyword search options, and even then, irrelevant or insufficient data may eventually be retrieved. To alleviate this situation, ontology-based semantic search has been proven to be an effective way to improve the quality of related content queries in such repositories. In this paper, we propose a new method of semantic linking and storing open government datasets of New Zealand's agriculture, land and rainfall sectors based on the use of ontology. The generated ontology can construct integrated data, in which a unified query can be applied to extract richer and more useful information. To validate our model, we showed how to link ontology manually and automatically. Manual linking requires domain experts, and automatic linking reduces the overhead of relying on domain experts to manually link concepts. The results of this method are promising in terms of improving data quality and search efficiency. In future, the proposed model can be integrated with other domain ontologies.

Ontology development for drought management information

The paper aims at integrating the data on drought management for the development of ontology-based semantic search to support the decision makers at various stages of a disaster. Ontology development comprises two processes: (1) knowledge acquisition and ontology modelling and (2) construction of ontology. In both processes, knowledge domain and ontology on drought management were collected, captured, revised and evaluated by the experts in the field of environmental engineering, water resources engineering, GIS and ontology development. The outcome of this research is the hybrid ontology for drought management information which comprised three types of ontologies: (1) river basin ontology, (2) statistics ontology and (3) task ontology. Approximately 154 concepts of drought management have been defined and classified. The ontology was constructed by using Hozo Ontology Editor. This developed ontology will be useful for ontology-based semantic search and knowledge visualisation system, which will provide extensive capabilities for accessibility and decision support system.

Improving Access to Software Architecture Knowledge: An Ontology-based Search Approach

Every software has an architecture, but in most cases it is poorly documented. That’s especially true in virtual community for software development, where creating and updating documents is not a high priority activity and architectural knowledge remain hidden in informal and semi-structured artifacts, such as e-mails, meeting notes and Wikis. We propose a framework that enables the search for software architecture information in the artifacts generated in virtual communities environments. An ontology-based semantic search mechanism is used in the framework to retrieve not only architecture properties, but also the rationale behind decisions taken during the construction of the software. The ontologies defined are also used in the visualization of the search results, providing an interactive environment for users to explore, discover, and analyze the information. The framework is preliminary validated in the Brazilian Public Software Community context.

A Comparison Between Ontology-Based and Translation-Based Semantic Search Engines for Arabic Blogs

The information-retrieval community has focused on the retrieval of documents based on their Latin content, and have emphasised keyword-based approaches. These approaches are prone to low retrieval precision for Arabic content. The goal of our research is to develop a domain-dependent semantic-based search engine for Arabic blogs named Mudawanati, a process devised to increase the precision of results by combining natural language processing with ontologies. We have focused on the two main approaches adopted in the development of Arabic search engines: translation-based and Arabic ontology-based, and we investigated how they affect the accuracy of results returned. Prototypes are developed for the two approaches and experimental results are presented to reflect the performance and precision rates of both approaches. Precision is a measure of the importance of a semantic web document.

Ontology-based semantic search on the Web and its combination with the power of inductive reasoning

Semantic Web search is currently one of the hottest research topics in both Web search and the Semantic Web. In previous work, we have presented a novel approach to Semantic Web search, which allows for evaluating ontology-based complex queries that involve reasoning over the Web relative to an underlying background ontology. We have developed the formal model behind this approach, and provided a technique for processing Semantic Web search queries, which consists of an offline ontological inference step and an online reduction to standard Web search. In this paper, we continue this line of research. We further enhance the above approach by the use of inductive rather than deductive reasoning in the offline inference step. This increases the robustness of Semantic Web search, as it adds the important ability to handle inconsistencies, noise, and incompleteness, which are all very likely to occur in distributed and heterogeneous environments such as the Web. The inductive variant also allows to infer new (not logically deducible) knowledge (from training individuals). We report on a prototype implementation of (both the deductive and) the inductive variant of our approach in desktop search, and we provide extensive new experimental results, especially on the running time and the precision and the recall of our new approach.

Semantic Web search based on ontological conjunctive queries

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontology-based Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.

건조사양서 요구사항의 추적을 위한 온톨로지 모델과 제품구조 통합 기초 연구

Ship design requirements described in the building specification should be reflected in the design process. This paper identifies the configuration of requirements mentioned in the building specification using Ontology Representation Language (OWL). Ontology-based semantic search system specifies the requirement items. Through this extraction, building specifications mentioned for each entry are configured to the tree. Tracking requirements for ship design and a set of procedures to instruct is also used for the V model of systems engineering. The semantic search engine of robot agent and ontology can search the requirements specification document and extract the design information. Thereafter, design requirements for the tracking model that proposes the relationship between the associated BOM(bill of material) and product structure.

Ontology-based Semantic Search Technology Research

Ontology-based Semantic Search Technology Research

Research on Workflow Management of Multi-Disciplinary Collaborative Design Based on Ontology

In this paper, the mechanism of workflow management based on ontology of multi disciplinary collaborative design is studied. Based on the integrative research of key technologies of semantic technology and semantic retrieval and workflow management, an ontology-based workflow framework of multi-disciplinary collaborative design is put forward. Models of workflow-related elements on different levels were built up according to subjects related in collaborative design. An ontology-based workflow model library of multi-disciplinary collaborative design is set up by using RDF. The currently popular ontology retrieval strategy is analyzed, and an ontology model search engine in workflow of multidisciplinary collaborative design is set up by combining the keywords-based with ontology-based semantic search.

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