A Proposed Approach for Arabic Semantic Annotation

Semantic annotation approaches link entities from a knowledge base to mentions of entities in text to provide additional content-related information. Recently increasing use of resources from the Linked Open Data (LOD) Cloud has been made to annotate text documents thanks to the network of machine-understandable, interlinked data. While existing approaches to semantic annotation in the LOD context have been proven to be well performing with the English language, many other languages in general and the Korean language in particular are still underrepresented. We investigate the applicability of existing semantic annotation approaches to the Korean language by adapting two popular approaches in the semantic annotation field and evaluating those approaches on an English-Korean bilingual sense-tagged corpus. Further, general challenges in internationalization of annotation approaches are summarized.

Semantic Annotation Tool for Annotating Arabic Web Documents

With the development of information technology, the resource description framework (RDF) occupies very important position in structure of the semantic web. An effective way to enhance the efficiency of the RDF data query is RDF closures. Our work is improve the RDF reasoning in MapReduce framework. The algorithm is based on packet classification efficiency of a few of candidate rules, as well as the NoSQL data storage avoiding repetition, corresponding to the RDF properties and concepts. The optimization algorithm of MapReduce is given a new closure generation process. Our approach use RDFS inference rules and the RDF triples in relationship between Map and Reduce operations and generate the RDF closures, improve the generate efficiency of closure. Based on RDF MapReduce algorithm for RDF closure, look forward more suitable RDF closure computing scheme on cloud computing environment.

The Internet of Things is a vast and growing virtual universe that includes medical devices, automobiles, industrial systems and a variable number of consumer electronics devices. With some 100 billion devices expected to be connected to the Internet by 2025, providing interoperability among the things on the IoT is one of the most fundamental requirements to support tracking, object addressing and discovery as well as information representation, storage, and exchange. Applying semantic techniques to IoT can support Interoperability, effective data access, integration resource discovery reasoning and processing of data knowledge extraction. In this paper, we present an overview of semantic technologies such as Semantic Web of Things (SWoT), Ontologies, Resource Description Framework (RDF), RDF Schema, Web Ontology Language (OWL), SPARQL Protocol And RDF Query Language (SPARQL), Semantic annotation and reasoning widely deployed in IoT.

Even initial feedback vertex set problem is NP-complete

PG-explorer: Resource Description Framework data exploration with property graphs

In the Semantic Web, items are represented by uniform resource identifiers (URIs) on the Web in order to describe relationships between things in resource description framework (RDF) graphs. Large scale open RDF data (linked data) describing information about various things are available. Hence, relational discovery research has been conducted using RDF data to explore relationships between things. The SPARQL protocol and RDF query language (SPARQL) is usually employed to process RDF data; however, SPARQL is not suitable for relational discovery using linked data because it is difficult to describe unknown vocabularies and relationships in SPARQL queries. In this paper, we propose an aggregation path search for RDF graphs as an extension of shortest path keyword search. The aggregation path search is devised to find RDF paths by merging multiple resource URIs into commonly reachable nodes in an RDF graph. This search method can discover new relationships between items that are connected by a common node. We present a method for treating equivalent resource URIs as a single resource URI; these equivalent URIs essentially represent the same thing based on equivalence relations in different RDF datasets. In our experiments, we show in the integration of multiple large scale RDF datasets that the integration of equivalent resources enables richer retrieval of aggregated paths.

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.

Resource Description Framework (RDF) is a semantic web specification that aims to support conceptual modeling of information about resources in the form of a triples of facts. In this paper, we note that, although RDF provides mechanisms to encode meta-information (such as source, trust, or certainty) about facts recorded in the knowledge base, existing RDF query languages and RDF stores fail to support key primitives needed in a large class of knowledge applications which associate utilities or costs on the available knowledge statements. To address this shortcoming, we propose a novel R2DF framework for utility ranked resource descriptions. We first propose a simple ranked RDF (R2DF) specification to enhance RDF triples with an application specific weight (e.g. cost). We then propose a SPARankQL query language specification, which includes a set of novel primitives on top of the SPARQL language to express top-k queries using traditional query patterns as well as novel flexible path predicates. An extended query processor engine, AR2Q, leverages novel index structures to support efficient ranked path search and includes query optimization strategies based on two key metrics: (a) proximity and (b) sub-result inter-arrival time. Experiments show that the use of these two metrics has significant impacts on the performance of top-k queries over R2DF graphs: in particular, the proximity measure helps reduce the number of path matches that need to be considered, whereas the inter-arrival measure reduces the overall execution time significantly especially when used along with proximity. The proposed strategies help obtain query plans close to optimal.

IBRI-CASONTO: Ontology-based semantic search engine

The RDF (Resource Description Framework) datamodel has been used in various domains, such as Web,government, biology etc. Now, the volume of RDF datasets is growing significantly. The explosion on the volume of RDF data raises serious challenges: how to answer SPARQL queries on large RDF data sets efficiently. Here, we present a large-scale RDF data system - TripleParallel, which implements blockbased parallel processing SPARQL queries on RDF data sets with billion triples. The system improves parallelism while strengthening the overlapping data and calculations and reduces the overall execution time of the query. TripleParallel also implements multiple parallel operations for parallel processing joins. Experimental studies with several RDF datasets, including the LUBM and the UniProt collection, demonstrate the performance gains of our approach, outperforming the previous fastest system by more than an order of magnitude.

Automatic construction of RDF with web tables

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.

PurposeThe aim of this paper is to examine a project where resource description framework (RDF) is expected to function both in the short term and over long periods. Because RDF is comparatively new, the library and information science literature has scarcely addressed the issues.Design/methodology/approachThe article analyses RDF from the perspective of the requirements for long term digital archiving, with special emphasis on the need for maintaining integrity and usability.FindingsRDF and RDFa (resource description framework in attributes) encoding can be archived, but the external references that make RDF function may change their meaning or vanish over time, unless efforts are made to archive them too and to provide time‐based context information through mechanisms like Memento. RDF‐based “triple store” databases can likewise be archived.Originality/valueThe value of this article lies in its examination of how RDF and its variants will work within the constraints of current long term digital archiving systems. RDF is relatively new, but its proponents see it as a long term enhancement of the web and of internet‐connected projects. This requires some consideration about how it will function in 100 years or more.

Business Intelligence (BI) plays important roles in executive decision making in organizations. Up-to-date information from good data sources always gives advantages to the organization. Nowadays, information in Semantic Webs is considered important source of BI data. It provides machine readable information using the Resource Description Framework (RDF). To perform BI activities on RDF documents, many research works propose direct mining on RDF documents. This paper presents a different approach for preparing BI project information from RDF data sources. A conceptual meta schema is used to describe RDF information. The meta schema is transformed into meta tables which are used to keep both RDF schema and document information. A transformation algorithm is proposed to transform the information into 5NF relational database Schemas and relations which are finally transformed into BI project data structures.