Semantic Database Research Articles

Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation.

BackgroundAdverse drug events (ADEs) are unintended side effects of drugs that cause substantial clinical and economic burdens globally. Not all ADEs are discovered during clinical trials; therefore, postmarketing surveillance, called pharmacovigilance, is routinely conducted to find unknown ADEs. A wealth of information, which facilitates ADE discovery, lies in the growing body of biomedical literature. Knowledge graphs (KGs) encode information from the literature, where the vertices and the edges represent clinical concepts and their relations, respectively. The scale and unstructured form of the literature necessitates the use of natural language processing (NLP) to automatically create such KGs. Previous studies have demonstrated the utility of such literature-derived KGs in ADE prediction. Through unsupervised learning of the representations (features) of clinical concepts from the KG, which are used in machine learning models, state-of-the-art results for ADE prediction were obtained on benchmark data sets.ObjectiveDue to the use of NLP to infer literature-derived KGs, there is noise in the form of false positive (erroneous) and false negative (absent) nodes and edges. Previous representation learning methods do not account for such inaccuracies in the graph. NLP algorithms can quantify the confidence in their inference of extracted concepts and relations from the literature. Our hypothesis, which motivates this work, is that by using such confidence scores during representation learning, the learned embeddings would yield better features for ADE prediction models.MethodsWe developed methods to use these confidence scores on two well-known representation learning methods—DeepWalk and Translating Embeddings for Modeling Multi-relational Data (TransE)—to develop their weighted versions: Weighted DeepWalk and Weighted TransE. These methods were used to learn representations from a large literature-derived KG, the Semantic MEDLINE Database, which contains more than 93 million clinical relations. They were compared with Embedding of Semantic Predications, which, to our knowledge, is the best reported representation learning method using the Semantic MEDLINE Database with state-of-the-art results for ADE prediction. Representations learned from different methods were used (separately) as features of drugs and diseases to build classification models for ADE prediction using benchmark data sets. The methods were compared rigorously over multiple cross-validation settings.ResultsThe weighted versions we designed were able to learn representations that yielded more accurate predictive models than the corresponding unweighted versions of both DeepWalk and TransE, as well as Embedding of Semantic Predications, in our experiments. There were performance improvements of up to 5.75% in the F1-score and 8.4% in the area under the receiver operating characteristic curve value, thus advancing the state of the art in ADE prediction from literature-derived KGs.ConclusionsOur classification models can be used to aid pharmacovigilance teams in detecting potentially new ADEs. Our experiments demonstrate the importance of modeling inaccuracies in the inferred KGs for representation learning.

Ontologies: a revamped cross-disciplinary buzzword or a truly promising interdisciplinary research topic?

The following article provides an introductory overview of the different research domains (computational linguistics, termino graphy, artificial intelligence (AI), philosophy and database semantics) for which ontologies and the emerging field of the Semantic Web have become a main point of interest. It will be pointed out that each of these domains uses a different definition for an ontology. A specific ontology engineering methodology (VUB STAR Lab DOGMA) will be presented and emphasis will be put on the specific role and contribution of (multilingual) terminography in this ontology. In addition, we will explain what ontologies might offer to advance the state of the art of linguistics and terminography.

Querying Linked Ontological Data through Distributed Summarization

As the semantic web expands, ontological data becomes distributed over a large network of data sources on the Web. Consequently, evaluating queries that aim to tap into this distributed semantic database necessitates the ability to consult multiple data sources efficiently. In this paper, we propose methods and heuristics to efficiently query distributed ontological data based on a series of properties of summarized data. In our approach, each source summarizes its data as another RDF graph, and relevant section of these summaries are merged and analyzed at query evaluation time. We show how the analysis of these summaries enables more efficient source selection, query pruning and transformation of expensive distributed joins into local joins.

Semantic Networks for Engineering Design: State of the Art and Future Directions

Abstract In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

Ontology-based approach to data exchanges for robot navigation on construction sites

As the use of autonomous Unmanned Ground Vehicles (UGV) for automated data collection from construction projects increases, construction stakeholders have become aware of a problem with inter-disciplinary semantic data sharing and exchanges between construction and robotic. Cross-domain data translation requires detailed specifications especially when it comes to semantic data translation. Building Information Modeling (BIM) and Geographic Information System (GIS) are the two digital building technologies used to capture and store semantic information for indoor structures and outdoor environments respectively. In the absence of a standard format for data exchanges between the construction and robotic domains, the tools of both industries have yet to be integrated into a coherent deployment infrastructure. In other words, the semantics of BIM-GIS cannot be automatically integrated by the robotic platforms currently being used. To enable semantic data transfer across domains, semantic web technology has been widely used in multi-disciplinary areas for interoperability. This paves the way to smarter, quicker and more precise robot navigation on construction sites. This paper develops a semantic web ontology integrating robot navigation and data collection to convey the meanings from BIM-GIS to the robot. The proposed Building Information Robotic System (BIRS) provides construction data that are semantically transferred to the robotic platform and can be used by the robot navigation software stack on construction sites. To meet this objective, first, knowledge representation between construction and robotic domains is bridged. Then, a semantic database integrated with the Robot Operating System (ROS) is developed, which can communicate with the robot and the navigation system to provide the robot with semantic building data at each step of data collection. Finally, the BIRS proposed system is validated through four case studies.

SEMANTIC NETWORKS FOR ENGINEERING DESIGN: A SURVEY

AbstractThere have been growing uses of semantic networks in the past decade, such as leveraging large-scale pre-trained graph knowledge databases for various natural language processing (NLP) tasks in engineering design research. Therefore, the paper provides a survey of the research that has employed semantic networks in the engineering design research community. The survey reveals that engineering design researchers have primarily relied on WordNet, ConceptNet, and other common-sense semantic network databases trained on non-engineering data sources to develop methods or tools for engineering design. Meanwhile, there are emerging efforts to mine large scale technical publication and patent databases to construct engineering-contextualized semantic network databases, e.g., B-Link and TechNet, to support NLP in engineering design. On this basis, we recommend future research directions for the construction and applications of engineering-related semantic networks in engineering design research and practice.

A Comparative Analysis of Semantic Web Databases Based on Scalability and Performance

Research is rapidly increasing day by day that taken too much efforts in exploring some interesting and some related publications over the internet.as we already know that every data bases have a different architecture that varies the performance in terms of storage architecture and medium. In this research paper we analyzed of two main big data types of Semantic web that iscategorized into two types (i) in memory Native (ii) Non-native Non-memory which are disk reside and Non-native is used for services management for instance, SQL, MySQL, and another is Oracle that is just used for storing purpose. Data bases is very important model specially, when any model come into existence. For instance, when we offer for storing purpose of that data then where it should have o store and then definitely it must be access efficiently. The proposed methodology consist test case for data retrieving and query optimization method to analyze performance of databases. When we talk about access data bases from any source then we query them for accessing. LUMB (Lehigh University Benchmark) is being used for testing performance and it cannot be used for storing data. Semantic Web Data (SWD) give a capability in such a way if anybody want to access / encode related data then it can be retrieved efficiently. Our main objective of research we have compared two types of SWD Native store and Non-nativestore and then we analyzed them

A novel semantic recognition framework of urban functional zones supporting urban land structure analytics based on open‐source data

AbstractTo solve problems related to urban land development, protection, and management, urban planners focus on achieving high precision and currency for fine‐grained semantic recognition of urban functional zones (UFZs). The classification of discrete functional zones and qualitative recognition of UFZs have been well studied, but the precise recognition of UFZs with explicit depictions of socioeconomic urban functions has seldom been addressed. Thus, we propose a novel open‐source data‐guided semantic urban function recognition (OSD‐SFR) framework to analyze UFZs and quantitatively link open‐source data (OSD) with the Chinese national urban function classification code. The spatial relationship between the combined semantic database and the segmented urban zones data set was measured, graded, and categorized. Kernel density and statistical methods based on a single data source of points‐of interest were used for comparative experiments. The results indicate that OSD‐SFR yields more fine‐grained delineations between urban functions and achieves more accurate recognition. Critical semantic information from different OSD sources is deeply integrated. This study provides a realistic reference, including detailed descriptions of the spatial distribution and morphological structure of UFZs, to support long‐term city planning.

Building a Knowledge Graph Representing Causal Associations Between Risk Factors and Incidence of Breast Cancer.

This paper explores the use of semantic- and evidence-based biomedical knowledge to build the RiskExplorer knowledge graph that outlines causal associations between risk factors and chronic disease or cancers. The intent of this work is to offer an interactive knowledge synthesis platform to empower health-information-seeking individuals to learn about and mitigate modifiable risk factors. Our approach analyzes biomedical text (from PubMed abstracts), Semantic Medline database, evidence-based semantic associations, literature-based discovery, and graph database to discover associations between risk factors and breast cancer. Our methodological framework involves (a) identifying relevant literature on specified chronic diseases or cancers, (b) extracting semantic associations via knowledge mining tool, (c) building rich semantic graph by transforming semantic associations to nodes and edges, (d) applying frequency-based methods and using semantic edge properties to traverse the graph and identify meaningful multi-node NCD risk paths. Generated multi-node risk paths consist of a source node (representing the source risk factor), one or more intermediate nodes (representing biomedical phenotypes), a target node (representing a chronic disease or cancer), and edges between nodes representing meaningful semantic associations. The results demonstrate that our methodology is capable of generating biomedically valid knowledge related to causal risk and protective factors related to breast cancer.

Enabling FAIR Discovery of Rare Disease Digital Resources.

Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. To design a solution to improve FAIR for machines for the EJP RD API specification. A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.

Idea generation with Technology Semantic Network

AbstractThere are growing efforts to mine public and common-sense semantic network databases for engineering design ideation stimuli. However, there is still a lack of design ideation aids based on semantic network databases that are specialized in engineering or technology-based knowledge. In this study, we present a new methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design. The core of the methodology is to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas. We demonstrate the effectiveness in general, and use strategies and ideation outcome implications of the methodology via a case study of flying car design idea generation.

Open-world probabilistic databases: Semantics, algorithms, complexity

Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry. They are continuously extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. The state of the art to store and process such data is founded on probabilistic databases. Many systems based on probabilistic databases, however, still have certain semantic deficiencies, which limit their potential applications. We revisit the semantics of probabilistic databases, and argue that the closed-world assumption of probabilistic databases, i.e., the assumption that facts not appearing in the database have the probability zero, conflicts with the everyday use of large-scale probabilistic knowledge bases. To address this discrepancy, we propose open-world probabilistic databases, as a new probabilistic data model. In this new data model, the probabilities of unknown facts, also called open facts, can be assigned any probability value from a default probability interval. Our analysis entails that our model aligns better with many real-world tasks such as query answering, relational learning, knowledge base completion, and rule mining. We make various technical contributions. We show that the data complexity dichotomy, between polynomial time and ▪, for evaluating unions of conjunctive queries on probabilistic databases can be lifted to our open-world model. This result is supported by an algorithm that computes the probabilities of the so-called safe queries efficiently. Based on this algorithm, we prove that evaluating safe queries is in linear time for probabilistic databases, under reasonable assumptions. This remains true in open-world probabilistic databases for a more restricted class of safe queries. We extend our data complexity analysis beyond unions of conjunctive queries, and obtain a host of complexity results for both classical and open-world probabilistic databases. We conclude our analysis with an in-depth investigation of the combined complexity in the respective models.

Pixel‐level multicategory detection of visible seismic damage of reinforced concrete components

AbstractThe detection of visible damage (i.e., cracking, concrete spalling and crushing, reinforcement exposure, buckling and fracture) plays a key role in postearthquake safety assessment of reinforced concrete (RC) building structures. In this study, a novel approach based on computer‐vision techniques was developed for pixel‐level multicategory detection of visible seismic damage of RC components. A semantic segmentation database was constructed from test photos of RC structural components. Series of datasets were generated from the constructed database by applying image transformations and data‐balancing techniques at the sample and pixel levels. A deep convolutional network architecture was designed for pixel‐level detection of visible damage. Two sets of parameters were optimized separately, one to detect cracks and the other to detect all other types of damage. A postprocessing technique for crack detection was developed to refine crack boundaries, and thus improve the accuracy of crack characterization. Finally, the proposed vision‐based approach was applied to test photos of a beam‐to‐wall joint specimen. The results demonstrate the accuracy of the vision‐based approach to detect damage, and its high potential to estimate seismic damage states of RC components.

Towards medical knowmetrics: representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context

In China, Prof. Hongzhou Zhao and Zeyuan Liu are the pioneers of the concept “knowledge unit” and “knowmetrics” for measuring knowledge. However, the definition on “computable knowledge object” remains controversial so far in different fields. For example, it is defined as (1) quantitative scientific concept in natural science and engineering, (2) knowledge point in the field of education research, and (3) semantic predications, i.e., Subject-Predicate-Object (SPO) triples in biomedical fields. The Semantic MEDLINE Database (SemMedDB), a high-quality public repository of SPO triples extracted from medical literature, provides a basic data infrastructure for measuring medical knowledge. In general, the study of extracting SPO triples as computable knowledge unit from unstructured scientific text has been overwhelmingly focusing on scientific knowledge per se. Since the SPO triples would be possibly extracted from hypothetical, speculative statements or even conflicting and contradictory assertions, the knowledge status (i.e., the uncertainty), which serves as an integral and critical part of scientific knowledge has been largely overlooked. This article aims to put forward a framework for Medical Knowmetrics using the SPO triples as the knowledge unit and the uncertainty as the knowledge context. The lung cancer publications dataset is used to validate the proposed framework. The uncertainty of medical knowledge and how its status evolves over time indirectly reflect the strength of competing knowledge claims, and the probability of certainty for a given SPO triple. We try to discuss the new insights using the uncertainty-centric approaches to detect research fronts, and identify knowledge claims with high certainty level, in order to improve the efficacy of knowledge-driven decision support.

Applying Code Transform Model to Newly Generated Program for Improving Execution Performance

The existing programs inside the voice assistant machine prompt human-machine interaction in response to a request from a user. However, the crucial problem is that the machine often may not give a proper answer to the user or cannot work out the existing program execution efficiently. Therefore, this study proposes a novel transform method to replace the existing programs (called sample programs in this paper) inside the machine with newly generated programs through code transform model GPT-2 that can reasonably solve the problem mentioned above. In essence, this paper introduces a theoretical estimation in statistics to infer at least a number of generated programs as required so as to guarantee that the best one can be found within them. In addition, the proposed approach not only imitates a voice assistant system with filtering redundant keywords or adding new keywords to complete keyword retrieval in semantic database but also checks code similarity and verifies the conformity of the executive outputs between sample programs and newly generated programs. According to code checking and program output verification, the processes can expedite transform operations efficiently by removing the redundant generated programs and finding the best-performing generated program. As a result, the newly generated programs outperform the sample programs because the proposed approach reduces the number of code lines by 32.71% and lowers the program execution time by 24.34%, which is of great significance.

Environmental problems of the development of transport in the transboundary region as exemplified by the Baikal basin and its cartographic study

Geoinformation support in the form of large cartographic atlases of complex content is intended to decide the issue of the Baikal region development, as the Lake Baikal catchment basin has a special nature management regime. Therefore transport maps among other industry maps are important for the analysis of regional development of the region. The compilation of transport maps requires presentation of all objects, processes, phenomena and interactions of the transport complex. In order to streamline the structure of thematic layer formation and the system of cartographic signs for displaying transport, a semantic classification of cartographic signs of transport systems was developed. Initially, we analyzed a large number of maps from various geographic atlases of various territorial levels. Then we generated a semantic database of transport maps. The developed classification streamlined the structure of the thematic layers of transport maps and, generally, the system of cartographic signs for transport layer. In the event of compiling a map of transport impact on the environment, we selected several semantic parameters that characterize the transport complex and individual indicators of transport impact on the environment. Consequently we created a map of transport impact on the environment in the Baikal basin. Then we analyzed the transport impact within the Lake Baikal catchment area on the basis of the created maps, as well as other literature and statistical sources. This analysis enabled conclusion about the fact that the greatest pollution of the Lake Baikal catchment area comes from automobile and water transport.

PP385 Using Common Data Models And Data Networks For Evidence Generation In Health Technology Assessment

IntroductionDifferences between healthcare datasets in structure, content, and coding systems are widely recognized as significant barriers to generating robust evidence for regulatory and medical decision making. As a result, there is a growing interest in using common data models embedded within large data networks. By standardizing the structure, contents, and semantics of disparate healthcare databases, common data models like the Observational and Medical Outcomes Partnerships common data model (OMOP-CDM) enable multidatabase studies to be undertaken at speed and in a transparent way. To date, little attention has been given to their potential role in health technology assessment (HTA).MethodsWe identify the uses of observational data in generating evidence in HTA, some common analytical challenges faced in their estimation, and the infrastructural, technical, and data reusability constraints that limit its wider use. We discuss where and how the OMOP-CDM could overcome these barriers in relation to different types of evidence requirements.ResultsThe OMOP-CDM increases the interoperability of otherwise disparate datasets, allowing reliable evidence to be generated from multidatabase studies at speed and transparently. The current analytical tools are best suited for clinical characterization and population-level effect estimation. Further developments to these tools are required to support analyses common in HTA like parametric survival modeling. Differences in costing methods as well as the structure of healthcare delivery between countries may limit the feasibility and value of standardization.ConclusionsThe OMOP-CDM has the potential to support reliable and timely evidence generation in HTA. The analytical tools should be further developed to support common HTA use cases.

Розробка інформаційної системи семантичної ідентифікації і документування результатів неформального й інформального навчання

We analyse modern approaches to the interaction of the educational services market with the labour market through recognition of non-formal and informal learning outcomes. The authors prove the expediency of semantic technologies used for information processing at the knowledge level. ESCO classifier that combines services of the labour market and the educational market is examined in detail. This classifier allows jobseekers and employers from different EU member states to handle information on learning outcomes more efficiently. The analyzes of practical ESCO classifier usage on example of the European Employment Service (EURES) demonstrates that this approach provides all necessary information for jobseekers abroad. The ontological analysis is an efficient way to model the ideas of various subject domains and it allows to represent formally their semantics. Hence, the research analyzes the ontological model for this information system and its components; in particular, the authors provide the described content of the ESCO ontological model which consists of three elements – occupations, skills and qualifications. The paper offers an information system for semantic identification and documentation of non-formal and informal learning outcomes based on the ESCO ontological model. The authors select and analyze the main stages of the system development: an ontology scheme in the Protege editor is created; the process of integrating the ontology in the RDF-repository is described; the architecture and user interface of the suggested information system are developed. Semantic graphs database GraphDB is used for ontology storage. To implement the addition and modification of data in GraphDB repository, connectors are used. Access to the RDF-repository is provided with SPARQL query language, whose outcomes are represented as result sets or RDF-graphs. In addition, the research justifies the use of React framework for development of the information system user interface. The architecture and user interface of information system proposed in this research work allow to carry out the semantic identification and documentation of non-formal and informal learning outcomes. Thus, they increase the efficiency of managing the information about these outcomes.

A semantic graph database for the interoperability of 3D GIS data

In the last decades, the use of information management systems in the building data processing led to radical changes to the methods of data production, documentation and archiving. In particular, the possibilities, given by these information systems, to visualize the 3D model and to formulate queries have placed the question of the information sharing in digital format. The integration of information systems represents an efficient solution for defining smart, sustainable and resilient projects, such as conservation and restoration processes, giving the possibilities to combine heterogeneous data. GIS provides a robust data storage system, a definition of topological and semantic relationships and spatial queries. 3D GIS makes possible the creation of three-dimensional model in a geospatial context. To promote the interoperability of GIS data, the present research aims first to analyse methods of conversion in CityGML and IndoorGML model, defining an ontological domain. This has led to the creation of a new enriched model, based on connections among the different elements of the urban model in GIS environment, and to the possibility to formulate queries based on these relations. The second step consists in collecting all data translated into a specific format that fill a graph database in a semantic web environment, while maintaining those relationships. The semantic web technology represents an efficient tool of interoperability that leaves open the possibility to import BIM data in the same graph database and to join both GIS and BIM models. The outcome will offer substantial benefits during the entire project life cycle. This methodology can also be applied to cultural heritage where the information management plays a key role.