Machine-interpretable Form Research Articles

Requiring Circularity Data in Bim With Information Delivery Specification

Implementing the circular economy model in the built environment demands high-quality information about the building stock and products. Building Information Modeling (BIM) is considered vital to capturing relevant information. This study uses a constructive research method to assess BIM support for effectively capturing circularity-related information. We identified circularity needs and transcribed them into actionable, machine-interpretable form, applying the novel Information Delivery Specification (IDS) standard. Considered use cases include material composition, quality and identity, environmental impacts, and disassembly aspects. We discuss the benefits, challenges, and suitability of modelling such information based on the Industry Foundation Classes (IFC) schema. The developed IDS specification allows for semi-automated compliance checking of BIM content. Most considered aspects can be expressed in BIM but often lack consistent terminology. Difficult to document are disassembly instructions and elements’ connections. Discussed technical limitations help rationalise the amount of information reasonable to capture in BIM. The study bridges the gap between sustainability expert demand for data and BIM deliverables. Resultant knowledge enables practitioners to increase the availability of quality data relevant for future building disassembly, reuse or adaptation.

Mining Geospatial Relationships from Text

A geospatial Knowledge Graph (KG) is a heterogeneous information network, capable of representing relationships between spatial entities in a machine-interpretable format, and has tremendous applications in logistics and social networks. Existing efforts to build a geospatial KG, have mainly used sparse spatial relationships, e.g., a district located inside a city, which provide only marginal benefits compared to a traditional database. In spite of the substantial advances in the tasks of link prediction and knowledge graph completion, identifying geospatial relationships remains challenging, particularly due to the fact that spatial entities are represented with single-point geometries, and textual attributes are frequently missing. In this study, we present GTMiner, a novel framework capable of jointly modeling Geospatial and Textual information to construct a knowledge graph, by mining three useful spatial relationships from a geospatial database, in an end-to-end fashion. The system is divided into three components: (1) a Candidate Selection module, to efficiently select a small number of candidate pairs; (2) a Relation Prediction component to predict spatial relationships between the entities; (3) a KG Refinement procedure, to improve both coverage and correctness of a geospatial knowledge graph. We carry out experiments on four cities' geospatial databases, from publicly-available sources and compare with existing algorithms for link prediction and geospatial data integration. Finally, we conduct an ablation study to motivate our design choices and an efficiency analysis to show that the time required by GTMiner for training and inference is comparable, or even shorter, than existing solutions.

An NLP-guided ontology development and refinement approach to represent and query visual information

The ubiquitous presence of surveillance systems generates massive amounts of video data. Storage and analysis of this data in real-time is a substantial challenge. There is huge potential in representing data in machine-readable and machine-interpretable format due to the presence of hidden semantics in images and videos. However, such representation requires ontology, which calls for expert domain knowledge. In this paper, a novel NLP-guided approach to generate an ontology for multimedia representation and information retrieval is proposed. A semi-automatic NLP-guided framework, which extracts all possible relations among objects is presented. This framework leverages the textual data of the domain to generate possible descriptions and actions within the domain. Relations among objects get embedded as object properties, whereas the category of an object as a class. Features and attributes of objects encode the data properties of the ontology. The proposed ontology is compared with existing multimedia ontologies and evaluated with regard to its capability to represent relations occurring in benchmark datasets, demonstrating the completeness and thorough coverage of the domain concepts. Spatial reasoning rules are established using Semantic Web Rule Language (SWRL) rules, and information retrieval is demonstrated using Description Logic (DL) and SPARQL queries. The proposed NLP-guided ontology generation approach is general enough to help in the development of ontologies for other domains as well, by providing video and textual data of the domain of interest, with limited human involvement.

Semantic Web Mining in E-Commerce Websites

Semantic web is gaining interest of lot of people due to its great power. Semantic web makes the data understandable for machines. It enables machines to interpret the data published by the web in machine interpretable form. Though lots of businesses are moving towards Ecommerce websites, but there are few problems with the current ecommerce systems due to the lack of proper standards. Daily thousands of people search for the product they want to buy and due to the inefficiency of the systems it wastes lot of time and resource of the users. Semantic web has that power to overcome those problems and it can accelerate the business to further higher level where ecommerce websites are playing very important role. This paper gives the introduction of semantic web mining and its techniques and then problems of the current ecommerce systems and how those problems can be avoided using semantic web. It discusses the detailed semantic web ecommerce architecture.

Converting neXtProt into Linked Data and nanopublications

The development of Linked Data provides the opportunity for databases to supply extensive volumes of biological data, information, and knowledge in a machine interpretable format to make previously isolated data silos interoperable. To increase ease of use, often databases incorporate annotations from several different resources. Linked Data can overcome many formatting and identifier issues that prevent data interoperability, but the extensive cross incorporation of annotations between databases makes the tracking of provenance in open, decentralized systems especially important. With the diversity of published data, provenance information becomes critical to providing reliable and trustworthy services to scientists. The nanopublication system addresses many of these challenges. We have developed the neXtProt Linked Data by serializing in RDF/XML annotations specific to neXtProt and started employing the nanopublication model to give appropriate attribution to all data. Specifically, a use case demonstrates the handling of post-translational modification (PTM) data modeled as nanopublications to illustrate the how the different levels of provenance and data quality thresholds can be captured in this model.

LB2CO: A Semantic Ontology Framework for B2C eCommerce Transaction on the Internet

Business ontology can enhance the successful development of complex enterprise system; this is being achieved through knowledge sharing and the ease of communication between every entity in the domain. Through human semantic interaction with the web resources, machines to interpret the data published in a machine interpretable form under web. However, the theoretical practice of business ontology in eCommerce domain is quite a few especially in the section of electronic transaction, and the various techniques used to obtain efficient communication across spheres are error prone and are not always guaranteed to be efficient in obtaining desired result due to poor semantic integration between entities. To overcome the poor semantic integration this research focuses on proposed ontology called LB2CO, which combines the framework of IDEF5 & SNAP as an analysis tool, for automated recommendation of product and services and create effective ontological framework for B2C transaction & communication across different business domains that facilitates the interoperability & integration of B2C transactions over the web.

Automatic Ontology Generation for Musical Instruments Based on Audio Analysis

In this paper we present a novel hybrid system that involves a formal method of automatic ontology generation for web-based audio signal processing applications. An ontology is seen as a knowledge management structure that represents domain knowledge in a machine interpretable format. It describes concepts and relationships within a particular domain, in our case, the domain of musical instruments. However, the different tasks of ontology engineering including manual annotation, hierarchical structuring and organization of data can be laborious and challenging. For these reasons, we investigate how the process of creating ontologies can be made less dependent on human supervision by exploring concept analysis techniques in a Semantic Web environment. In this study, various musical instruments, from wind to string families, are classified using timbre features extracted from audio. To obtain models of the analysed instrument recordings, we use K-means clustering to determine an optimised codebook of Line Spectral Frequencies (LSFs), or Mel-frequency Cepstral Coefficients (MFCCs). Two classification techniques based on Multi-Layer Perceptron (MLP) neural network and Support Vector Machines (SVM) were tested. Then, Formal Concept Analysis (FCA) is used to automatically build the hierarchical structure of musical instrument ontologies. Finally, the generated ontologies are expressed using the Ontology Web Language (OWL). System performance was evaluated under natural recording conditions using databases of isolated notes and melodic phrases. Analysis of Variance (ANOVA) were conducted with the feature and classifier attributes as independent variables and the musical instrument recognition F-measure as dependent variable. Based on these statistical analyses, a detailed comparison between musical instrument recognition models is made to investigate their effects on the automatic ontology generation system. The proposed system is general and also applicable to other research fields that are related to ontologies and the Semantic Web.

HIV2EU: supporting standardized HIV‐2 drug resistance interpretation in Europe

Various items are complicating the treatment of HIV‐2 infected patients. Compared to HIV‐1 there is much less treatment experience, no evidence from randomized control trials, a reduced number of effective drugs and no broadly available test for viral load monitoring. In case of treatment failure there is only limited guidance and presently no easy accessible tool for nucleic acid sequence interpretation available. To solve this problem, we initiated an expert workshop to address some of these problems. A panel of experts from four different European countries voted on a rule set for interpretation of mutations in the HIV‐2 protease, reverse transcriptase and integrase. Rules were proposed by each member and were then modified during discussion by considering data gained from HIV‐1 and accumulated experience of the follow up of HIV‐2‐infected patients. Based on the HIV‐GRADE internet‐tool an online tool was developed to make the rule set easily accessible and usable. Rules were laid down for the interpretation of HIV‐2 drug resistance to NRTIs, PIs and INIs (integrase inhibitors). Due to natural resistance of HIV‐2, usage of NNRTIs and T‐20 was not recommended as part of an antiretroviral regimen for HIV‐2. These rules were then translated in a machine interpretable format (algorithm specification interface, ASI) and the HIV‐GRADE tool was extended for usage of HIV‐2 sequences. Further consensus sequences were generated from the reference sequence data set provided by Los Alamos National Laboratories. In contrast to HIV‐1, mutations were compared to a group specific consensus sequence (Group A or Group B) and not to a consensus sequence from the most predominant HIV‐2 Group A. This change was necessary due to significant differences between the various HIV‐2 strains. We developed a rule set and an automated tool for HIV‐2 drug resistance analyses. This tool and the rules will be freely available on the internet. Access to the pre‐publication versions can be granted by each of the group members. To keep the algorithm rules up‐to‐date it will be actualized on a yearly basis.

CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONS

The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.

Contemporary Semantic Web Service Frameworks: An Overview and Comparisons

The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.

A Web 3.0-driven Collaborative Multicriteria Spatial Decision Support System

Multicriteria Spatial Decision Support System (MC-SDSS) is one of the most common tools for solving the spatial decision problems. Using the MC-SDSS in the Web 2.0 environment enhances the collaborative decision making by providing a flexible problem-solving framework, where the relevant GIS-based multicriteria decision analysis (MCDA) tools and information are provided for active participation/collaboration. However, the Web 2.0-based collaborative GIS-MCDA knowledge including the user generated content (preferences/opinions) and the GIS-MCDA elements are not machine-understandable. Thus, such knowledge cannot be interpreted, processed, searched, shared, and reused by the Web-based software agents and applications. Additionally, the GIS-MCDA models are often constructed based on the knowledge and understanding that arises out of the subjective viewpoints and they lack a shared and common reference for collaboration, thus making it hard for participants to collaboratively solve the decision problem. To address these problems, this paper proposes the Web 3.0-based collaborative MC-SDSS, which integrates the Web 2.0 (a Web of people) community methods and the Semantic Web (a Web of meaning) technologies. The collaborative/participatory GIS-MCDA knowledge is semantically modeled in an ontological framework as a backbone of the Semantic Web. The ontology is needed to express the collaborative GIS-MCDA knowledge in a shared, unified, structured, meaningful, and machine-interpretable form, ready for software agents and people to interpret, process, communicate, share, and reuse it. The paper demonstrates an implementation of the proposed Web 3.0-based MC-SDSS for solving the parking site selection problem in Tehran, Iran.

Mining the Semantic Web

In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.

Design and development of linked data from The National Map

The development of linked data on the World-Wide Web provides the opportunity for the U.S. Geological Survey USGS to supply its extensive volumes of geospatial data, information, and knowledge in a machine interpretable form and reach users and applications that heretofore have been unavailable. To pilot a process to take advantage of this opportunity, the USGS is developing an ontology for The National Map and converting selected data from nine research test areas to a Semantic Web format to support machine processing and linked data access. In a case study, the USGS has developed initial methods for legacy vector and raster formatted geometry, attributes, and spatial relationships to be accessed in a linked data environment maintaining the capability to generate graphic or image output from semantic queries. The description of an initial USGS approach to developing ontology, linked data, and initial query capability from The National Map databases is presented.

CONTEMPORARY SEMANTIC WEB SERVICE FRAMEWORKS: AN OVERVIEW AND COMPARISONS

The growing proliferation of distributed information systems, allows organizations to offer their business processes to a worldwide audience through Web services. Semantic Web services have emerged as a means to achieve the vision of automatic discovery, selection, composition, and invocation of Web services by encoding the specifications of these software components in an unambiguous and machine-interpretable form. Several frameworks have been devised as enabling technologies for Semantic Web services. In this paper, we survey the prominent Semantic Web service frameworks. In addition, a set of criteria is identified and the discussed frameworks are evaluated and compared with respect to these criteria. Knowing the strengths and weaknesses of the Semantic Web service frameworks can help researchers to utilize the most appropriate one according to their needs.

A service infrastructure for the representation, discovery, distribution and evaluation of agricultural production standards for automated compliance control

Modern agricultural production is governed by a variety of production standards that restrict and guide farming practices. Controlling the compliance of farms to these standards is currently a considerable and expensive manual effort for several stakeholders of agriculture; an effort that could be alleviated with suitable information technology. This article identifies the requirements and proposes a design for a service infrastructure that transfers the production standards in a computer encoded and machine interpretable format between the stakeholders of modern agricultural production. These encoded production standards can then have an immediate benefit for farmers and providers of Farm Management Information Systems (FMIS), ultimately enabling automated compliance control with existing farm data. The functionality of the infrastructure is demonstrated with a precision fertilisation case, where compliance to several fertilisation restrictions is controlled and confirmed automatically. The proposed REST-based service infrastructure was found sufficient in fulfilling the identified requirements. Automated compliance control for a fair proportion of production standards, despite several technical challenges, can be reasonably achieved with existing technologies as a lightweight infrastructure of REST-based Web services.

Perspectives of Semantic Web in E Commerce

The Semantic Web Technologies growing interest in human computer interactions where it enables the machines to interpret the data published in a machine interpretable form under web. Currently, E-Commerce is seriously vulnerable due to the lack of proper standards where HTML does not provide the syntax and semantics of information. Semantic Web has the potential to extremely influence the further development of the internet market, where e-commerce plays an important role. This paper discusses the semantic architecture for e-commerce using ontology language like RDF. This gives the overview about how the producer describes their resource in ontology content and how the consumer will retrieve the efficient data.

Computable visually observed phenotype ontological framework for plants

BackgroundThe ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed.ResultsWe have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research.ConclusionsThe Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community.

A Framework for Automatic Web Service Discovery Based on Semantics and NLP Techniques

As a greater number of Web Services are made available today, automatic discovery is recognized as an important task. To promote the automation of service discovery, different semantic languages have been created that allow describing the functionality of services in a machine interpretable form using Semantic Web technologies. The problem is that users do not have intimate knowledge about semantic Web service languages and related toolkits. In this paper, we propose a discovery framework that enables semantic Web service discovery based on keywords written in natural language. We describe a novel approach for automatic discovery of semantic Web services which employs Natural Language Processing techniques to match a user request, expressed in natural language, with a semantic Web service description. Additionally, we present an efficient semantic matching technique to compute the semantic distance between ontological concepts.

From legal code to digital code: making software services rights-aware

Services proliferate in myriad domains, with their seamless potentiality raising new issues such as the need to articulate the rights and obligations associated with the services. Licensing is a method for exploiting intellectual property rights in software. Licences enable developers to control how consumers use software content. Consequently, developers rely on the contracts in the form of licence agreements to protect software from unauthorised use. Conceptualising service licences and making them in machine-interpretable form would promote broader usage of services. In this paper, we will explore the concept of service licensing and how to transform service licences from human-readable form (legal code) to machine-readable form (digital code).

Constraint capture and maintenance in engineering design

AbstractThe Designers' Workbench is a system developed by the Advanced Knowledge Technologies Consortium to support designers in large organizations, such as Rolls-Royce, to ensure that the design is consistent with the specification for the particular design as well as with the company's design rule book(s). In the principal application discussed here, the evolving design is described using a jet engine ontology. Design rules are expressed as constraints over the domain ontology. Currently, to capture the constraint information, a domain expert (design engineer) has to work with a knowledge engineer to identify the constraints, and it is then the task of the knowledge engineer to encode these into the Workbench's knowledge base. This is an error-prone and time-consuming task. It is highly desirable to relieve the knowledge engineer of this task, so we have developed a system, ConEditor+, that enables domain experts themselves to capture and maintain these constraints. Further, we hypothesize that to appropriately apply, maintain, and reuse constraints, it is necessary to understand the underlying assumptions and context in which each constraint is applicable. We refer to them as “application conditions,” and these form a part of the rationale associated with the constraint. We propose a methodology to capture the application conditions associated with a constraint and demonstrate that an explicit representation (machine interpretable format) of application conditions (rationales) together with the corresponding constraints and the domain ontology can be used by a machine to support maintenance of constraints. Support for the maintenance of constraints includes detecting inconsistencies, subsumption, redundancy, fusion between constraints, and suggesting appropriate refinements. The proposed methodology provides immediate benefits to the designers, and hence, should encourage them to input the application conditions (rationales).