In this work we study a rational extension $SROEL^R T$ of the low complexity description logic SROEL, which underlies the OWL EL ontology language. The extension involves a typicality operator T, whose semantics is based on Lehmann and Magidor's ranked models and allows for the definition of defeasible inclusions. We consider both rational entailment and minimal entailment. We show that deciding instance checking under minimal entailment is in general $\Pi^P_2$-hard, while, under rational entailment, instance checking can be computed in polynomial time. We develop a Datalog calculus for instance checking under rational entailment and exploit it, with stratified negation, for computing the rational closure of simple KBs in polynomial time.

Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very large knowledge bases. In this paper, we propose KG2Vec, a novel approach to Knowledge Graph Embedding based on the skip-gram model. Instead of using a predefined scoring function, we learn it relying on Long Short-Term Memories. We evaluated the goodness of our embeddings on knowledge graph completion and show that KG2Vec is comparable to the quality of the scalable state-of-the-art approaches and can process large graphs by parsing more than a hundred million triples in less than 6 hours on common hardware.

His M. IS 202 – Fall 2002.

Humans have the unique capacity to translate thoughts into words, and to infer others' thoughts from their utterances. This ability is based on mental representations of meaning that can be mapped to language, but to which we have no direct access. The approach to meaning representation that currently dominates the field of natural language processing relies on distributional semantic models, which rest on the simple yet powerful idea that words similar in meaning occur in similar linguistic contexts1. A word is represented as a semantic vector in a high-dimensional space, where similarity between two word vectors reflects similarity of the contexts in which those words appear in the language2. More recently, these models have been extended beyond single words to express meanings of phrases and sentences5,6,7, and the resulting representations predict human similarity judgments for phrase- and sentence-level paraphrases8,9.

Products and services associated with the IoT currently operate in closed ecosystems like Home Automation, in effect, they are simply networked products with linking software. Part machine to machine (M2M) and part human to machine or machine to human (H2M setup and observation and M2H alerts). The IoT is an open ecosystem, made up of billions of product, services and people ecosystems. An ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain. If you're looking for advice on how your products or services can be made ready for the IoT please contact us.

EnterpriseCyc is a fully supported version of the knowledge base and reasoning technology that includes enterprise-grade development, deployment, and administration capabilities. It can be licensed for commercial applications. Academic institutions also have the option to license ResearchCyc, a full version of the knowledge base and reasoning technology that is strictly for non-commercial research purposes. The Platforms provide a powerful knowledge representation language (CycL), a vast ontology of concepts and relations, and a formally modeled repository of knowledge about these concepts enabling you to build on decades of knowledge modeling rather than starting from a blank page. In addition, Cyc includes an inference (reasoning) engine that makes use of a large suite of custom reasoners that provide unparalleled performance over a large knowledge base and any volume of data.

The paper describes the architecture of intelligence system for automated construction of ontological knowledge bases of subject areas and the programming model subsystem management GUI.

In the modern era, abundant information is easily accessible from various sources, however only a few of these sources are reliable as they mostly contain unverified contents. We develop a system to validate the truthfulness of a given statement together with underlying evidence. The proposed system provides supporting evidence when the statement is tagged as false. Our work relies on an inference method on a knowledge graph (KG) to identify the truthfulness of statements. In order to extract the evidence of falseness, the proposed algorithm takes into account combined knowledge from KG and ontologies. The system shows very good results as it provides valid and concise evidence. The quality of KG plays a role in the performance of the inference method which explicitly affects the performance of our evidence-extracting algorithm.

Datafication is a buzzword of the last several years, that is used actively along Big Data industry. Honestly, if you would search the term'datafication' on the internet you probably won't find that much relative information about it, yet it is a word we are hearing a lot these days. However, after analyzing the topic itself, I could say that many of us understand the meaning of the term, but probably named it another way. Datafication, according to MayerSchoenberger and Cukier is the transformation of social action into online quantified data, thus allowing for real-time tracking and predictive analysis. Simply said, it is about taking previously invisible process/activity and turning it into data, that can be monitored, tracked, analysed and optimised.

Consider a number of wind turbines scattered throughout the North Sea. Each turbine is equipped with a Query processing over data streams is a key aspect of Big sensor, which continuously records temperature levels of key Data applications. For instance, algorithmic trading relies on devices within the turbine and sends those readings to a data real-time analysis of stock tickers and financial news items centre monitoring the functioning of the turbines. Temperature (Nuti et al. 2011); oil and gas companies continuously monitor levels are streamed by sensors using a ternary predicate and analyse data coming from their wellsites in order Temp, whose arguments identify the device, the temperature to detect equipment malfunction and predict maintenance level, and the time of the reading. A monitoring task in the needs (Cosad et al. 2009); network providers perform realtime data centre is to track the activation of cooling measures in analysis of network flow data to identify traffic anomalies each turbine, record temperature-induced malfunctions and and DoS attacks (Münz and Carle 2007).