Approach For Semantic Annotation Research Articles

An unsupervised approach for semantic place annotation of trajectories based on the prior probability

Semantic place annotation can provide individual semantics, greatly helping the field of trajectory data mining. Most existing methods rely on annotated or external data and require retraining models following a region change, thus preventing their large-scale applications. Herein, we propose an unsupervised method denoted as UPAPP for the semantic place annotation of individual trajectories using spatiotemporal information. The Bayesian Criterion is specifically employed to decompose the spatiotemporal probability of visiting the candidate place into spatial probability, duration probability, and visiting time probability. Spatial information in two geospatial data sources is comprehensively integrated to calculate the spatial probability. In terms of the temporal probabilities, the Term Frequency–Inverse Document Frequency weighting algorithm is used to count the potential visits to different place types in the trajectories and to generate the prior probabilities of the visiting time and duration. Finally, the spatiotemporal probability of the candidate place is then combined with the importance of the place category to annotate the visited places. Experimental results in a trajectory dataset collected by 709 volunteers in Beijing showed that our method achieved an overall and average accuracy of 0.712 and 0.720, respectively, indicating that the visited places can be annotated accurately without any annotated data.

A new semantic annotation approach for software vulnerability source code

An efficient semantic annotation approach is proposed to annotate software vulnerability source code based on the vulnerability code semantic description language (VCSDL) in this paper. A set of general annotation frameworks is proposed for two basic components: basic description information of vulnerability and vulnerability source code description information in the language. Specific annotation methods are studied for these two components, according to the annotation method of the basic description information of vulnerability. Also, the corresponding attribute in the VCSDL document structure is extracted to determine the labelling of the basic information of the vulnerability. While, according to the vulnerability source code information, the semantic annotation of the source code information of the vulnerability is implemented. The experimental results show that the proposed semantic annotation approach has a better effectiveness on the annotation of datasets with a simple code structure and a smaller scale. The success rate and accuracy of the proposed annotation are higher and the false positive rate and false negative rate are lower.

A new semantic annotation approach for software vulnerability source code

A new semantic annotation approach for software vulnerability source code

JSON-LD Based Web API Semantic Annotation Considering Distributed Knowledge

Based on semantically annotated Web APIs, automatic Web API composition can be implemented easily. The operation can greatly improve efficiency of building a software system. However, in real world, semantic annotation for Web APIs will encounter various difficulties, because of their distribution and function diversity, such as disunited API description formats, response result with complex structure, shortage of business domain ontologies, semantic conflicts among distributed knowledge, and so on. To solve these difficulties, we propose a JSON-LD based Web API semantic annotation approach (JWASA). JWASA can assist professional developers to semi-automatically complete semantic annotation of Web APIs. A common Web API description ontology is firstly defined, including necessary vocabularies about invocation information, functional semantics, and non-functional features. Then, JWASA automatically converts a Web API description into a document in an united JSON format, and assist developers to semi-automatically embed semantic information of crucial elements of the API by means of a lightweight Linked Data format JSON-LD. Meanwhile, a semantic annotation specification is proposed to deal with various complex situations in Web API description, e.g: too many response parameters, no request parameters, etc. In addition, to improve efficiency of annotation, JWASA provides some extra operations, including automatic new ontology or vocabulary creation, automatic functional semantics extraction etc. Also, JWASA provides semi-automatically bridge rule generation algorithm, which can infer implied relationships among vocabularies (e.g: sub-class, super-class, equality). JWASA focuses on the semantic annotation of functionality of Web APIs, and can create effective semantic Web APIs for future API automatic composition. We implement a prototype system and carry out a series of experiments to evaluate JWASA on real Web APIs crawled from Internet. Experiments show that our approach is effective and efficient.

Structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model

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.

The BIG IoT API - Semantically Enabling IoT Interoperability

Today, internet of things (IoT) platforms offer proprietary interfaces and protocols. To enable interoperable interaction with those platforms we present the generic BIG IoT API that employs a novel approach for self-description and semantic annotation to fully adapt arbitrary IoT platforms. We have deployed this approach for multiple platforms from the mobility domain.

Hybrid approach for big data localization and semantic annotation

SummaryMost of the data concerning business‐oriented systems are still based on either NoSQL or the relational data model. On the other hand, Semantic Web data model Resource Description Framework (RDF) has become the new standard for data modeling and analysis. Due to this situation integration of NoSQL, Relational Database (RDB) and RDF data models are becoming a required feature of the systems. Many solutions like tools and languages are provided in the shape of the transformation of data from RDB to RDF. This research is aimed to compare and map data models used for transformation between NoSQL, RDB, and Semantic Web. This study will help in achieving much better and enhanced technology‐based systems for retrieval and storage of data among Big‐data annotation using Semantic Web. It is aimed to reduce the response time of queries and offer compatibility with the web and semantically enriched data format. A drugs dataset is being used and transformed to have semantical meaning embedded and linked to support big data localization. At the end of this paper, RDF graph and bar chart are used to represent transformed data after passing through the proposed model. Big data localization helps in gaining fast and accurate results.

Semantic Annotating of Text Documents: Basic Concepts and Taxonomic Approach

One of the tools for the semantic enrichment of the content of information resources is semantic annotating, which makes it possible to comment on and evaluate annotated resources and their fragments and to carry out a semantic search on their basis. The use of the taxonomic approach simultaneously allows classifying the subjects of annotating and generating new scientometric indicators. This paper considers the essence of semantic annotating, defines the basic concepts, discusses the general model of semantic annotation and taxonomic approach to presenting the semantics of annotations, and gives the examples of taxonomies based on various properties of annotations. The implementation of semantic annotating in the Socionet scientific information system is considered as an example.

A collaborative approach for semantic time-based video annotation using gamification

Efficient access to large scale video assets, may it be our life memories in our hard drive or a broadcaster archive which the company is eager to sell, requires content to be conveniently annotated. Manually annotating video content is, however, an intellectually expensive and time-consuming process. In this paper we argue that crowdsourcing, an approach that relies on a remote task force to perform activities that are costly or time-consuming using traditional methods, is a suitable alternative and we describe a solution based on gamification mechanisms for collaboratively collecting timed metadata. Tags introduced by registered players are validated based on a collaborative scoring mechanism that excludes erratic annotations. Voting mechanisms, enabling users to approve or refuse existing tags, provide an extra guarantee on the quality of the annotations. The sense of community is also created as users may watch the crowd’s favourite moments of the video provided by a summarization functionality. The system was tested with a pool of volunteers in order to evaluate the quality of the contributions. The results suggest that crowdsourced annotation can describe objects, persons, places, etc. correctly, as well as be very accurate in time.

A Lightweight Semantic Web-based Approach for Data Annotation on IoT Gateways

Internet of Things (IoT) applications rely on networks composed of set of heterogeneous sensors and smart devices, which have the capability to constantly, observe the surroundings and gather data. This heterogeneity is reflected on raw data gathered by such type of systems. Consequently, the task of high-level IoT applications to interpret such data and detect events in the real world is more complex. Moreover, data heterogeneity leads to the lack of interoperability between IoT applications. Semantic Web (SW) technologies have been widely adopted to model and integrate data from different sources on the web; extending them to the IoT domain can be used to mitigate the aforementioned challenges. Semantically annotating IoT data is a fundamental step toward developing smarter and interoperable IoT applications. However, this type of process requires a large amount of computing resources, especially in scenarios where a large number of sensors is expected to be involved such as smart city. To address these challenges, we propose a lightweight semantic annotation approach that can be implemented on resource-constrained IoT gateways connected to a limited number of sensors. To evaluate the feasibility of the proposed approach, we have carried out a set of experimentations using a middleware prototype implementation. Several benchmarks are considered such as: Data size, response time, and resource utilization.

A user-oriented semantic annotation approach to knowledge acquisition and conversion

Semantic annotation on natural language texts labels the meaning of an annotated element in specific contexts, and thus is an essential procedure for domain knowledge acquisition. An extensible and coherent annotation method is crucial for knowledge engineers to reduce human efforts to keep annotations consistent. This article proposes a comprehensive semantic annotation approach supported by a user-oriented markup language named UOML to enhance annotation efficiency with the aim of building a high quality knowledge base. UOML is operable by human annotators and convertible to formal knowledge representation languages. A pattern-based annotation conversion method named PAC is further proposed for knowledge exchange by utilizing automatic pattern learning. We designed and implemented a semantic annotation platform Annotation Assistant to test the effectiveness of the approach. By applying this platform in a long-term international research project for more than three years aiming at high quality knowledge acquisition from a classical Chinese poetry corpus containing 52,621 Chinese characters, we effectively acquired 150,624 qualified annotations. Our test shows that the approach has improved operational efficiency by 56.8%, on average, compared with text-based manual annotation. By using UOML, PAC achieved a conversion error ratio of 0.2% on average, significantly improving the annotation consistency compared with baseline annotations. The results indicate the approach is feasible for practical use in knowledge acquisition and conversion.

Semantic annotations for semantic interoperability in a product lifecycle management context

The interoperability among a variety of systems, in or across manufacturing enterprises, has been widely accepted as one of the important factors that affect the efficiency of production. Many research works, related to the interoperability at different levels, have been carried out to tackle the information exchange, transformation, discovery and reuse. One of the main challenges in these research works is to overcome the semantic heterogeneity in the exchanged information between enterprise applications along the life cycle of a product. As a possible solution to support the semantic interoperability issue, semantic annotations have gained more and more attention. This paper identifies several existing drawbacks and proposes a formal semantic annotation approach to support the semantic enrichment of models in a product lifecycle management context.

Bag of words for semantic automatic medical image annotation

A medical report contains many elements such as medical images accompanied by text descriptions. We present in this paper a new approach for semantic automatic annotation of medical images. The proposed approach uses the bag of words model to represent the visual content of the medical image combined with text descriptors based on term frequency–inverse document frequency technique and reduced by latent semantic to extract the co-occurrence between text and visual terms. In a first phase, we are interested in indexing texts and extracting all relevant terms using a thesaurus containing medical subject headings and concepts. In a second phase, medical images are indexed while recovering areas of interest which are invariant to change in scale such as light and tilt. To annotate a new medical image, we use the bag of words model to recover the feature vector. Indeed, we use the vector space model to retrieve similar medical images from the training database. The computation of the relevance value of an image according to a query image is based on the cosine function. To evaluate the performance of our proposed approach, we present an experiment carried out on five types of radiological imaging. The results showed that our approach works efficiently, especially with more images taken from the radiology of the skull.

Using Semantic Technology to Enhance ESB Capabilities

Modern enterprises consist of complex business systems. These systems need to be integrated to support enterprises operation. The SOA and ESB become an important enterprise integration architecture style for designing and implementing integration systems. But there are some limitations of todays ESB framework, such as only syntactic description of service interface, inability to perform semantic mediation and incapable process knowledge management. Therefore developers need deep and intimate knowledge to develop integration systems. We introduce an ontology-based semantic annotation approach to enrich and reconcile semantics of data, services and process models on ESB that enables data, service and process models interoperability on the semantic level through common domain ontologies.

An Ontology-Based Automatic Semantic Annotation Approach for Patent Document Retrieval in Product Innovation Design

Patent retrieval plays a very important role in product innovation design. However, current patent retrieval approaches lack semantic comprehension and association, and usually cannot capture the implicit useful knowledge at a semantic level. In order to improve the traditional patent search, this paper proposes a novel ontology-based automatic semantic annotation approach based on the thorough analysis of patent documents, which combines both structure and content characteristics, and integrates multiple techniques from various aspects. Multilayer semantic model is established to realize unified semantic representation. The approach first utilizes template schemes to extract the structure information from patent documents, and then identifies semantics of entities and relations between entities from the content based on natural language processing techniques and domain knowledge, and at last employs a heuristic pattern learning method to abstract patent technical features. Case study is provided to show that our approach can acquire multi-level patent semantic knowledge from multiple perspectives, and discover semantic correlations between patent documents, which can further promote the accurate patent semantic retrieval effectively.

Disaster planning using automated composition of semantic OGC web services: A case study in sheltering

Spatial data are crucial in disaster planning. However, because of the dynamic, urgent and uncertain nature of disasters, certain data and functionalities may be inaccessible to decision makers when they are required. Web service composition offers a possible solution whereby disaster planners can integrate spatial web services to generate new spatial data and functionalities, quickly, from existing ones. This paper proposes an automatic solution for composing OWSs (Open Geospatial Consortium Web Services) for disaster planning. A semantic annotation approach based on the Resource Description Framework (RDF) and SPARQL languages is used to describe OWSs semantically. A conceptual model for AI (Artificial Intelligence) planning is also proposed that works based on RDF and SPARQL. An AI planning algorithm was implemented based on the proposed conceptual model to compose semantic OWSs. The applicability of the proposed solution is investigated through a case study in evacuation sheltering. The case study demonstrates that the proposed automatic composition approach can enhance the efficiency of OWS integration and thereby improve the disaster management process.

SEMANTIC ANNOTATION OF EXISTING GEO-DATASETS: A CASE STUDY OF DISASTER RESPONSE IN NETHERLANDS

Abstract. Use of relevant geo-information is one of the important issues for performing different tasks and processes in disaster response phase. In order to save time and cost, services could be employed for integrating and extracting relevant up-to-date geo-information. For this purpose, semantics of geo-information should be explicitly defined. This paper presents our initial results in applying an approach for semantic annotation of existing geo-datasets. In this research the process of injecting semantic descriptions into geodatasets (information integration) is called semantic annotation. A web system architecture is presented and the process of semantic annotation is presented by using the Meta-Annotation approach. The approach is elaborated by providing an example in disaster response which utilizes geo-datasets in CityGML format and further two languages of semantic web technology: RDF and Notation3.

Annotating korean text documents with linked data resources

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.

An e‐Science data infrastructure for simulations within Grid computing environment: methods, approaches and practice

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.

SEAN: Multi-ontology semantic annotation for highly accurate closed domains

Semantic annotation has gained momentum with the emergence of the current user-generated content paradigm on the Web. The ever-growing quantity of collaborative data sources has resulted in the requirement for efficient approaches to create, integrate and retrieve information more efficiently in an environment where the users ask for accurate information. The main research challenge of the current work is using manual semantic annotation in a highly accurate closed domain, a conceptual domain with a minimal set of concepts where the benefits of adding semantics, search efficiency, optimization and the cost estimations are viable. This paper presents a semantic annotation approach for highly accurate closed domain based on multi-ontology annotation (domain and application ontologies). Key words: Semantic annotation, semantic search, highly accurate closed domains, multi-ontology.