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A table is a convenient way to store, structure, and present data. Tables are an attractive knowledge source in various applications, including knowledge graph engineering. However, a lack of understanding of the semantic structure and meaning of their content may reduce the effectiveness of this process. Hence, the restoration of tabular semantics and the development of knowledge graphs based on semantically annotated tabular data are highly relevant tasks that have attracted a lot of attention in recent years. We propose a hybrid approach using heuristics and machine learning methods for the semantic annotation of relational tabular data and knowledge graph populations with specific entities extracted from the annotated tables. This paper discusses the main stages of the approach, its implementation, and performance testing. We also consider three case studies for the development of domain-specific knowledge graphs in the fields of industrial safety inspection, labor market analysis, and university activities. The evaluation results revealed that the application of our approach can be considered the initial stage for the rapid filling of domain-specific knowledge graphs based on tabular data.

Background There is strong scientific evidence linking obesity and overweight to the risk of various cancers and to cancer survivorship. Nevertheless, the existing online information about the relationship between obesity and cancer is poorly organized, not evidenced-based, of poor quality, and confusing to health information consumers. A formal knowledge representation such as a Semantic Web knowledge base (KB) can help better organize and deliver quality health information. We previously presented the OC-2-KB (Obesity and Cancer to Knowledge Base), a software pipeline that can automatically build an obesity and cancer KB from scientific literature. In this work, we investigated crowdsourcing strategies to increase the number of ground truth annotations and improve the quality of the KB. Methods We developed a new release of the OC-2-KB system addressing key challenges in automatic KB construction. OC-2-KB automatically extracts semantic triples in the form of subject-predicate-object expressions from PubMed abstracts related to the obesity and cancer literature. The accuracy of the facts extracted from scientific literature heavily relies on both the quantity and quality of the available ground truth triples. Thus, we incorporated a crowdsourcing process to improve the quality of the KB. Results We conducted two rounds of crowdsourcing experiments using a new corpus with 82 obesity and cancer-related PubMed abstracts. We demonstrated that crowdsourcing is indeed a low-cost mechanism to collect labeled data from non-expert laypeople. Even though individual layperson might not offer reliable answers, the collective wisdom of the crowd is comparable to expert opinions. We also retrained the relation detection machine learning models in OC-2-KB using the crowd annotated data and evaluated the content of the curated KB with a set of competency questions. Our evaluation showed improved performance of the underlying relation detection model in comparison to the baseline OC-2-KB. Conclusions We presented a new version of OC-2-KB, a system that automatically builds an evidence-based obesity and cancer KB from scientific literature. Our KB construction framework integrated automatic information extraction with crowdsourcing techniques to verify the extracted knowledge. Our ultimate goal is a paradigm shift in how the general public access, read, digest, and use online health information.

Agriculture and biological engineering are the foundation of agricultural modernization, and related countries have also issued relevant policies to guide the development of agriculture and biotechnology. Agricultural and biological engineering experts are the intellectual resources in the field. At present, there is less research on the management and maintenance of agricultural and biological engineering experts, and there is a lack of software systems in this area. In order to realize the management and maintenance of agricultural and biological engineering expert information, a service platform for the international agricultural and biological engineering expert system based on the B/S framework has been developed. The background of the system used C# as the development language, and the foreground used JavaScript technology and Bootstrap. The software adopted ASP.NET MVC as the web development framework and used the Entity Framework to operate the SQL Server background database. The system has the function of searching and querying agricultural and biological engineering expert information according to keywords, and implements the functions of adding, deleting, and modifying data records, and the function of generating spreadsheets and importing spreadsheet data. The development of this system provides effective management tools for the maintenance and construction of agricultural and biological engineering expert databases and lays a good foundation for the construction of agricultural and biological engineering think tanks.

Current developments in the medical domain, not unlike many other sectors, are marked by the growing digitalization of data, including patient records, study results, clinical guidelines or imagery. This trend creates the opportunity for the development of innovative decision support systems to assist physicians in making a diagnosis or preparing a treatment plan. Similar conditions hold for the Web, where massive amounts of raw text are to be processed and interpreted automatically, e.g. to eventually add new information to a knowledge base. To this end, complex tasks need to be solved, requiring one or more interpretation algorithms (e.g. image- or natural language processors) to be chosen and executed based on heterogeneous data. We, therefore, propose the first approach to a semantic framework for sequential decision making and develop the foundations of a Linked agent who executes interpretation algorithms available as Linked APIs [43] on a data-driven, declarative basis [45] by integrating structured knowledge formalized with the Resource Description Framework (RDF), and having access to meta components for planning and learning from experience. We evaluate our framework based on automatically processing brain images, the ad-hoc combination of surgical phase recognition algorithms and experiential learning to optimally pipeline entity linking approaches.

Entity recognition and disambiguation （ERD） is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework （EUEF）, which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis.

To obtain comparable high query performance with relational databases, diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework (RDF) data and SPARQL query. Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality principle. However, existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics. Although semantic caching approaches work effectively in this case, little work has been done in this area. In this paper, we try to improve SPARQL query performance with semantic caching approaches, i.e., SPARQL algebraic expression tree (AET) based caching and entity caching. Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches. The approaches are implemented on a two-level-storage structure. The main memory stores the most frequently accessed cache items, and items swapped out are stored on the disk for future possible reuse. Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches. Comparisons with previous research are also provided.

Between 2006 and 2010 the World Bank sought to unmask the role of science, technology, and innovation in Ugandan industry. This report presents insights from this research based on case studies of six sectors: agriculture, health, energy, information and communication technology (ICT), transport, and logistics. Based on more than 80 interviews cutting across Uganda's small and medium-sized enterprises, universities, and government entities, the report's findings are intended to offer the government and its partners in industry increased clarity about how better to harness science, technology, and innovation to propel the economy. Enabling implementation of the recent Uganda national science, technology, and innovation policy is a parallel goal of the report. The policy articulates the government's intent to foster research and development that builds the human capital that Uganda requires for a knowledge-based economy. The case studies from which this report's recommendations are drawn depict a diverse range of experiences across industrial sectors in terms of generating, applying, and adapting science and technology to contribute to Uganda's development. Despite the relatively small size of the country's investments in science and technology, the past 20 years have seen considerable advances in building capacity in science and technology, developing related institutions and human resources, advancing collaboration and communication, and expanding the base of available knowledge. But given Uganda's limited investments in science and technology, policies should prioritize near-term investments that benefit key sectors. This report identifies those near-term investments as well as longer-term ones (three to five years in the future).