Typically an ontology matching technique is a combination of much different type of matchers operating at various abstraction levels such as structure, semantic, syntax, instance etc. An ontology matching technique which employs matchers at all possible abstraction levels is expected to give, in general, best results in terms of precision, recall and F-measure due to improvement in matching opportunities and if we discount efficiency issues which may improve with better computing resources such as parallel processing. A gold standard ontology matching model is derived from a model classification of ontology matching techniques. A suitable metric is also defined based on gold standard ontology matching model. A review of various ontology matching techniques specified in recent research papers in the area was undertaken to categorize an ontology matching technique as per newly proposed gold standard model and a metric value for the whole group was computed. The results of the above study support proposed gold standard ontology matching model.

The conversation around Artificial Intelligence usually revolves around technology-focused topics: machine learning, conversational interfaces, autonomous agents, and other aspects of data science, math, and implementation. However, the history and evolution of AI is also inextricably linked with waves of innovation and research breakthroughs that run headfirst into economic and technology roadblocks. There seems to be an indelible pattern of discovery, innovation, interest, investment, cautious optimism, boundless enthusiasm, realization of limitations, technological roadblocks, withdrawal of interest, and retreat of AI research back to academic settings. These waves of advance and retreat appear to be as consistent as sea waves on the shore. This pattern is vexing to technologists and investors because it doesn't follow the usual technology adoption lifecycle.

Competency Questions (CQs) are natural language questions outlining and constraining the scope of knowledge represented by an ontology. Despite that CQs are a part of several ontology engineering methodologies, we have observed that the actual publication of CQs for the available ontologies is very limited and even scarcer is the publication of their respective formalisations in terms of, e.g., SPARQL queries. This paper aims to contribute to addressing the engineering shortcomings of using CQs in ontology development, to facilitate wider use of CQs. In order to understand the relation between CQs and the queries over the ontology to test the CQs on an ontology, we gather, analyse, and publicly release a set of 234 CQs and their translations to SPARQL-OWL for several ontologies in different domains developed by different groups. We analysed the CQs in two principal ways. The first stage focused on a linguistic analysis of the natural language text itself, i.e., a lexico-syntactic analysis without any presuppositions of ontology elements, and a subsequent step of semantic analysis in order to find patterns. This increased diversity of CQ sources resulted in a 5-fold increase of hitherto published patterns, to 106 distinct CQ patterns, which have a limited subset of few patterns shared across the CQ sets from the different ontologies. Next, we analysed the relation between the found CQ patterns and the 46 SPARQL-OWL query signatures, which revealed that one CQ pattern may be realised by more than one SPARQL-OWL query signature, and vice versa. We hope that our work will contribute to establishing common practices, templates, automation, and user tools that will support CQ formulation, formalisation, execution, and general management.

We present the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g.

Abductive reasoning generates explanatory hypotheses for new observations using prior knowledge. This paper investigates the use of forgetting, also known as uniform interpolation, to perform ABox abduction in description logic (ALC) ontologies. Non-abducibles are specified by a forgetting signature which can contain concept, but not role, symbols. The resulting hypotheses are semantically minimal and each consist of a set of disjuncts. These disjuncts are each independent explanations, and are not redundant with respect to the background ontology or the other disjuncts, representing a form of hypothesis space. The observations and hypotheses handled by the method can contain both atomic or complex ALC concepts, excluding role assertions, and are not restricted to Horn clauses. Two approaches to redundancy elimination are explored for practical use: full and approximate. Using a prototype implementation, experiments were performed over a corpus of real world ontologies to investigate the practicality of both approaches across several settings.

The seminaïve algorithm can be used to materialise all consequences of a datalog program, and it also forms the basis for algorithms that incrementally update a materialisation as the input facts change. Certain (combinations of) rules, however, can be handled much more efficiently using custom algorithms. To integrate such algorithms into a general reasoning approach that can handle arbitrary rules, we propose a modular framework for computing and maintaining a materialisation. We split a datalog program into modules that can be handled using specialised algorithms, and we handle the remaining rules using the seminaïve algorithm. We also present two algorithms for computing the transitive and the symmetric-transitive closure of a relation that can be used within our framework. Finally, we show empirically that our framework can handle arbitrary datalog programs while outperforming existing approaches, often by orders of magnitude.

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular, the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.

Knowledge and information are becoming the primary resources of the emerging information society. To exploit the potential of available expert knowledge, comprehension and application skills (i.e. The ability to acquire these skills is limited for any individual human. Consequently, the capacities to solve problems based on human knowledge in a manual (i.e. We envision a new systemic approach to enable scalable knowledge deployment without expert competences. Eventually, the system is meant to instantly deploy humanity's total knowledge in full depth for every individual challenge. To this end, we propose a sociotechnical framework that transforms expert knowledge into a solution creation system. Knowledge is represented by automated algorithms (knowledge engines). Executable compositions of knowledge engines (networks of knowledge engines) generate requested individual information at runtime. We outline how these knowledge representations could yield legal, ethical and social challenges and nurture new business and remuneration models on knowledge. We identify major technological and economic concepts that are already pushing the boundaries in knowledge utilisation: E.g. in artificial intelligence, knowledge bases, ontologies, advanced search tools, automation of knowledge work, the API economy. We indicate impacts on society, economy and labour. Existing developments are linked, including a specific use case in engineering design. 1 INSTANTLY DEPLOYABLE EXPERT KNOWLEDGE - NETWORKS OF KNOWLEDGE ENGINES For decades we experience an ongoing structural shift in value creation: from agricultural and industrial production to services and, more recently, to information-and knowledgebased services. Information and knowledge are becoming primary resources of the emerging knowledge society.

This paper develops the concept of knowledge and its exchange using Semantic Web technologies. It points out that knowledge is more than information because it embodies the meaning, that is to say semantic and context. These characteristics will influence our approach to represent and to treat the knowledge. In order to be adopted, the developed system needs to be simple and to use standards. The goal of the paper is to find standards to model knowledge and exchange it with an other person. Therefore, we propose to model knowledge using UML models to show a graphical representation and to exchange it with XML to ensure the portability at low cost. We introduce the concept of ontology for organizing knowledge and for facilitating the knowledge exchange. Proposals have been tested by implementing an application on the design knowledge of a pen.

Optimization techniques play a significant role in improving description logic reasoners covering the Web Ontology Language (OWL). These techniques are essential to speed up these reasoners. Many of the optimization techniques are based on heuristic choices. Optimal heuristic selection makes these techniques more effective. The FaCT++ OWL reasoner and its Java version JFact implement an optimization technique called ToDo list which is a substitute for a traditional top-down approach in tableau-based reasoners. The ToDo list mechanism allows one to arrange the order of applying different rules by giving each a priority. Compared to a top-down approach, the ToDo list technique has a better control over the application of expansion rules. Learning the proper heuristic order for applying rules in ToDo lis} will have a great impact on reasoning speed. We use a binary SVM technique to build our learning model. The model can help to choose ontology-specific order sets to speed up OWL reasoning. On average, our learning approach tested with 40 selected ontologies achieves a speedup of two orders of magnitude when compared to the worst rule ordering choice.