Annotating korean text documents with linked data resources

Semantic annotation refers to the process of annotating documents using the ontology in order to data becomes meaningful. Most of the techniques and methods of the field of semantic annotation and retrieval are used for dealing broadly in the English language. This paper aims to enhance the process of information retrieval for Arabic language that depends on the ontology in the process of document annotation. To achieve this aim, it is determined and processed the problems of the Arabic language through the proposed approach. This paper depends on semantic annotation based on ontology and Resource Description Framework (RDF). The results achieved high precision and high recall for the semantic annotation based on the proposed approach.

PurposeThree-dimensional computer-aided design (CAD) assembly model has become important resource for design reuse in enterprises, which implicates plenty of design intent, assembly intent, design experience knowledge and functional structures. To acquire quickly CAD assembly models associated with specific functions by using product function requirement information in the product conceptual design phase for model reuse, this paper aims to find an approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model before functional semantic-based assembly retrieval.Design/methodology/approachAn approach for structure-function correlations analysis and functional semantic annotation of CAD assembly model is proposed. First, the product knowledge model is constructed based on ontology including design knowledge and function knowledge. Then, CAD assembly model is represented by part attributed adjacency graph and partitioned into multiple functional regions. Assembly region and flow-activity region are defined for structure-function correlations analysis of CAD assembly model. Meanwhile, the extraction process of assembly region and flow-activity region is given in detail. Furthermore, structure-function correlations analysis and functional semantic annotation are achieved by considering comprehensively assembly structure and assembled part shape structure in CAD assembly model. After that, a structure-function relation model is established based on polychromatic sets for expressing explicitly and formally relationships between functional structures, assembled parts and functional semantics.FindingsThe correlation between structure and function is analyzed effectively, and functional semantics corresponding to structures in CAD assembly model are labeled. Additionally, the relationships between functional structures, assembled parts and functional semantics can be described explicitly and formally.Practical implicationsThe approach can be used to help designers accomplish functional semantic annotation of CAD assembly models in model repository, which provides support for functional semantic-based CAD assembly retrieval in the product conceptual design phase. These assembly models can be reused for product structure design and assembly process design.Originality/valueThe paper proposes a novel approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model. Functional structures in assembly model are extracted and analyzed from the point of view of assembly structure and function part structure. Furthermore, the correlation relation between structures, assembled parts and functional semantics is expressed explicitly and formally based on polychromatic sets.

Focusing on the current dispersed and fragmented business process modeling approaches, the functions are not complete, the interfaces are not uniform, the support formats are diverse, and the technologies are different. In order to solve this problem, this paper uses ontology technology and business process modeling annotations, adopts semantic annotation approach, systematically integrates the advantages of existing approachs and improves the semantic annotation approach to improve the degree of automation and accuracy, and proposes a semantic business process modelling approach. The approach is the custom-order-oriented processing business process, and based on the activity matching and ontology similarity matching approachs in the semantic annotation approach in the modeling process. Thus systematically integrating existing technology tools to build semantic business process models for custom orders. The model for 3D printing jewelry, automotive, clothing and other industries e-commerce custom manufacturing process for experimental applications. At the same time, the proposed approach builds a custom order ontology library and generates a custom order semantic business process knowledge base.

In recent years, Brazil’s federal judicial system has embraced digitalization, making a large amount of legal process information available to citizens and legal experts. Despite the advances, a significant portion of the data produced and stored in legal systems presents itself in the form of natural language text, including numerous petitions and legal decisions. This creates barriers for automated querying and analysis of legal process data, especially considering the importance of the content of legal decisions in these tasks. In this paper, we report on an automated semantic annotation pipeline for judicial decision texts obtained from the official National Uniformization Panel (TNU) jurisprudence website. NLP models are trained in a few-shot learning context with a training set annotated by legal experts. The semantic annotation approach is evaluated using precision and recall. The results of the semantic annotation are produced into RDF-based nanopublications aligned with a reference domain ontology. The annotations are accompanied with provenance information including identification of the machine learning model used.

Integrating the extracted facts with an existing knowledge base has raised an urgent need to address the problem of entity linking. Specifically, entity linking is the task to link the entity mention in text with the corresponding real world entity in the existing knowledge base. However, this task is challenging due to name ambiguity, textual inconsistency, and lack of world knowledge in the knowledge base. Several methods have been proposed to tackle this problem, but they are largely based on the co-occurrence statistics of terms between the text around the entity mention and the document associated with the entity. In this paper, we propose LINDEN, a novel framework to link named entities in text with a knowledge base unifying Wikipedia and WordNet, by leveraging the rich semantic knowledge embedded in the Wikipedia and the taxonomy of the knowledge base. We extensively evaluate the performance of our proposed LINDEN over two public data sets and empirical results show that LINDEN significantly outperforms the state-of-the-art methods in terms of accuracy.

Geographical and taxonomical referencing of specimens and documented species observations from within and across natural history collections is vital for ongoing species research. However, much of the historical data such as field books, diaries and specimens, are challenging to work with. They are computationally inaccessable, refer to historical place names and taxonomies, and are written in a variety of languages. In order to address these challenges and elucidate historical species observation data, we developed a workflow to (i) crowd-source semantic annotations from handwritten species observations, (ii) transform them into RDF (Resource Description Framework) and (iii) store and link them in a knowledge base. Instead of full-transcription we directly annotate digital field books scans with key concepts that are based on Darwin Core standards. Our workflow stresses the importance of verbatim annotation. The interpretation of the historical content, such a resolving a historical taxon to a current one, can be done by individual researchers after the content is published as linked open data. Through the storage of annotion provenance, who created the annotation and when, we allow multiple interpretations of the content to exist in parallel, stimulating scientific discourse. The semantic annotation process is supported by a web application, the Semantic Field Book (SFB)-Annotator, driven by an application ontology. The ontology formally describes the content and meta-data required to semantically annotate species observations. It is based on the Darwin Core standard (DwC), Uberon and the Geonames ontology. The provenance of annotations is stored using the Web Annotation Data Model. Adhering to the principles of FAIR (Findable, Accessible, Interoperable & Reusable) and Linked Open Data, the content of the specimen collections can be interpreted homogeneously and aggregated across datasets. This work is part of the Making Sense project: makingsenseproject.org. The project aims to disclose the content of a natural history collection: a 17,000 page account of the exploration of the Indonesian Archipelago between 1820 and 1850 (Natuurkundige Commissie voor Nederlands-Indie) With a knowledge base, researchers are given easy access to the primary sources of natural history collections. For their research, they can aggregate species observations, construct rich queries to browse through the data and add their own interpretations regarding the meaning of the historical content.

Linked Open Data (LOD) Cloud is a mesh of open datasets coming from different domains. Among these datasets, a notable amount of datasets belong to the life sciences domain linked together forming an interlinked “Life Sciences Linked Open Data (LSLOD) Cloud”. One of the key challenges for data publishers is to identify and establish links between newly generated domain specific datasets and LSLOD Cloud. While a number of publishing tools exist for creating links from new to existing datasets, tools to detect domain-specific relevant datasets for linking purposes are missing. In this paper, we propose an extended technique for automatically identifying relevant datasets in LSLOD Cloud for inner-ear anatomical and clinical terminologies. We validate the proposed technique with experiments over the publicly accessible LSLOD Cloud using realworld terminologies and datasets provided by clinical organizations.

Geospatial datasets in the Linking Open Data (LOD) Cloud are rather of static nature and mainly consist of information such as a name, geo coordinates, an address, or opening hours. There is no linked dataset providing dynamic information about the "popularity" of certain places or the "Visiting frequency" of users in specific contextual situations. This type of information within the LOD Cloud, however, would enable a variety of new applications based on semantically enriched location analytics. In this paper, we present Linked Crowdsourced Data as a dataset, which links real user location preferences (e.g., check-ins, ratings, or comments) as well as specific context situations (e.g., weather conditions, holiday information, or measured networks) collected via crowdsourcing to static location data. We showcase the applicability of this dataset for location analytics use cases through a map visualization and highlight its added value with exemplary SPARQL queries that allow for location requests depending on historic context information.

The Linked Open Data (LOD) cloud is a promising reality since the major content producers are offering their data on an open and linked network, through RDF (Resource Description Framework), with the aim of providing Semantic Web applications with a single global database for retrieval of related content and to perform inferences over the network. However, bases with Portuguese-language content are still incipient. In this paper we present the process of inclusion of the InferenceNet – the first resource with common sense and inferentialist knowledge in Portuguese language – on the LOD. Our main goal is to leverage the use and development of Semantic Web applications by content producers in Portuguese language. We develop and evaluated a platform, called SemWidgets, for the creation and execution of widgets able to access and reason over InferenceNet and the open linked data, like DBPedia, Yago, and Article Search API of the New York Times.KeywordsLinguistic ResourcePortuguese LanguageWeb SemanticLinked Open Data

SUMMARYGrid‐based simulation usually involves large quantities of data at each stage of the simulation process. These data include simulation input and output files, intermediate results files, log and error files, associated metadata, and information capturing the processes that generate the data. The question of how to effectively store and manage data files within a Grid computing environment is increasingly becoming an important issue. This paper illustrates how we built a lightweight e‐Science infrastructure for data management within a Grid computing environment, including the integration of data curation activities into the entire Grid‐based simulation process. Rather than focusing on specific implementation details, we aim to identify the key issues and research challenges, describing how various existing technologies and tools can be best integrated to address these requirements and challenges. Although the case of quantum mechanical simulation of materials properties is used in the paper, much of the discussion is as generic as possible so that approaches, methods and practice (e.g. integrated approach, workflow taxonomy and development approach, simple but useful semantic annotation approach) can be applied to wider domains and disciplines to facilitat the digital research. A comparison between our approach and Cloud computing, and lessons learned in data management within the Grid computing environment, are also presented. Copyright © 2012 John Wiley & Sons, Ltd.

This paper proposes an ontology matching method for aligning a source ontology with target ontologies already published and linked on the Linked Open Data (LOD) cloud. This method relies on the refinement of a set of input alignments generated by existing ontology matching methods. Since the ontologies to be aligned can be expressed in several representation languages with different levels of expressiveness and the existing ontology matching methods can only be applied to some representation languages, the first step of our method consists in applying existing matching methods to as many ontology variants as possible. We then propose to apply two main strategies to refine the initial alignment set: the removal of different kinds of ambiguities between correspondences and the use of the links published on the LOD. We illustrate our proposal in the field of life sciences and environment.

The traditional means of extracting information from the Web are keyword-based search and browsing. The Semantic Web adds structured information (i.e., semantic annotations and references) supporting both activities. One of the most interesting recent developments is Linked Open Data (LOD), where information is presented in the form of facts – often originating from published domain-specific databases – that can be accessed both by a human and a machine via specific query endpoints. In this article, we argue that machine learning provides a new way to query web data, in particular LOD, by analyzing and exploiting statistical regularities. We discuss challenges when applying machine learning to the Web and discuss the particular learning approaches we have been pursuing in THESEUS. We discuss a number of applications where the Web is queried via machine learning and describe several extensions to our approaches.

With the rise of the internet, a lot of web services (WSs) are offered. However, most of these WSs lack explicit and sufficient semantic information. As a result, relevant WSs are not delivered to consumers during service discovery and WS composition and selection are so challenging. A possible solution to this is to provide the existing syntactic WSs with explicit information of their meaning through an annotation. Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. In this paper, we first conduct a literature review and then we present an approach for semantically annotating WSs based on linked open data (LOD) knowledge. The proposal uses similarity measure algorithms for matching the appropriate LOD concepts to the corresponding WS' parameters. A performance evaluation on real-world WSs was performed to validate the proposed approach.

Day by day, the amount of digital data grows exponentially. Furthermore, these data come from heterogeneous content and structure resources. Both factors are the major handicap that limits the correct functioning of information systems. Being on the same wavelength, researchers have fixed as a challenge to reach targeted information retrieval which allows an easy access to the desired resource then return relevant and correct outcomes corresponding to a specific request. Therefore, semantic annotation is chosen as a paramount solution that may handle all these requirements, while ensuring a good understanding of document content then exploit and share it readily afterward. In this paper, we treat semantic annotation of documents using multi-ontologies, by stressing the effect of that on the relevance and the quality of semantic annotation results. We present a survey of different multi-ontologies based semantic annotation approaches and conclude with a synthesis of the study made.

This paper will discuss the philosophical differences between Chi Wu Lun of Chuang Tzu and the annotations of Hsiang Kuo in terms of two parts: the ways of annotations and the philosophical ideas.The way of annotations can be divided into two aspects for explanation purposes. The two aspects are the annotation approach and the annotation method. Chi Wu Lun uses the subject value as an approach while the annotations of Hsiang Kuo use objective thinking to be its approach. Chi Wu Lun employs plain and concrete illustrations as the method while the annotations of Hsiang Kuo take argumentative analysis as its method.The philosophical ideas can be further discussed using the four aspects of key concerns, secondary concerns, patterns and the realm of thoughts. The Chi Wu Lun focuses on the existence of life while the annotations of Hsiang Kuo is concerned with political problems. Chi Wu Lun centers around Hsin while the annotations of Hsiang Kuo concentrates on Hsing. The philosophical pattern of Chi Wu Lun demonstrates is upwardly lofty while the pattern shown in annotations of Hsiang Kuo is downwardly harmonious. Chi Wu Lun dicpicts a realm of absolute tranquility while the annotations of Hsiang Kuo reaches a realm of relative tranquility.