That's the direction taken by startup Cognonto, co-founded by Michael Bergman, a man whose history in the AI, Machine Learning, Semantic technologies, Internet search and data arenas goes back a long way. That includes his additional duties as CEO of Structured Dynamics, birthplace of UMBEL (Upper-level Mapping and Binding Exchange Layer), a knowledge graph and vocabulary for interoperating Web-accessible information, which had its latest update in May. As far as the new Cognonto venture, whose initial fruits are the Cognonto Platform and KBpedia knowledge structure, Bergman says it's been in gestation for about eight years. "The'aha' moment came when we realized how many of the large-scale QA systems were basing their knowledge structure around Wikipedia," Bergman says. "We realized this was a huge storehouse of very useful information, but one that everyone reinvented every time they brought in their own system," from Siri to Viv to IBM Watson and the Google Knowledge Graph.

Privacy policies are used to communicate company data practices to consumers and must be accurate and comprehensive. Each policy author is free to use their own nomenclature when describing data practices, which leads to different ways in which similar information types are described across policies. A formal ontology can help policy authors, users and regulators consistently check how data practice descriptions relate to other interpretations of information types. In this paper, we describe an empirical method for manually constructing an information type ontology from privacy policies. The method consists of seven heuristics that explain how to infer hypernym, meronym and synonym relationships from information type phrases, which we discovered using grounded analysis of five privacy policies. The method was evaluated on 50 mobile privacy policies which produced an ontology consisting of 355 unique information type names. Based on the manual results, we describe an automated technique consisting of 14 reusable semantic rules to extract hypernymy, meronymy, and synonymy relations from information type phrases. The technique was evaluated on the manually constructed ontology to yield .95 precision and .51 recall.

DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. It extends concepts of Inductive Logic Programming and Relational Learning to the Semantic Web in order to allow powerful data analysis. DL-Learner is used for data analysis tasks within other tools such as ORE and RDFUnit. Technically, it uses refinement operator based, pattern-based and evolutionary techniques for learning on structured data. It also offers a plugin for Protégé, which can give suggestions for axioms to add.

Colfax now offers a 2-hour Hands-On Workshop (HOW) video on the best practices for performance optimization for Intel Xeon Phi processor (formerly Knights Landing). Use links below the video to navigate the 10 episodes.

Unification in Description Logics has been introduced as a means to detect redundancies in ontologies. We try to extend the known decidability results for unification in the Description Logic $\mathcal{EL}$ to disunification since negative constraints can be used to avoid unwanted unifiers. While decidability of the solvability of general $\mathcal{EL}$-disunification problems remains an open problem, we obtain NP-completeness results for two interesting special cases: dismatching problems, where one side of each negative constraint must be ground, and local solvability of disunification problems, where we consider only solutions that are constructed from terms occurring in the input problem. More precisely, we first show that dismatching can be reduced to local disunification, and then provide two complementary NP-algorithms for finding local solutions of disunification problems.

Inspired by the development of semantic technologies in recent years, in statistical analysis field the traditional methodology of designing, publishing and consuming statistical datasets is evolving to so-called "Linked Statistical Data" by associating semantics with dimensions, attributes and observation values based on Linked Data design principles. The representation of datasets is no longer a combination of magic words and numbers. Everything is becoming meaningful when URIs replace their positions as dereferencable resources, which further establishes the relations between resources implicitly and automatically. Different datasets are no longer isolated and all datasets share a globally, uniquely and uniformly defined structure. At this point, it is time to start building data-oriented applications and services with the traditional statistical computing languages such as R, while benefiting from the omnipotent semantic power of the SPARQL query language.

The Salesforce announcement of Einstein this week -- impressive as it was -- reminded me that marketers sometimes use terms like machine learning, artificial intelligence (AI) and even automation interchangeably. Businesses have long been infatuated with the word intelligence – business intelligence, relationship intelligence, and media intelligence – are all overused terms from the last decade, for example. In my mind, all of these descriptors are a stretch. Intelligence means something very specific: it's the disposition, composition and strength of an enemy. Yet business executives love war analogies and here we are mashing these terms together again. We like to use the term AI because it sounds more sophisticated.

In this role, you'll be part of our European consulting team that is helping clients to design and deliver innovative solutions based on Cognitive Computing approaches - in particular based on IBM WATSON technology. We're looking for experienced professionals who have proven expertise in one or multiple of the areas of Artificial Intelligence, Natural Language Processing, Semantic Technologies, Information Retrieval or Machine Learning. You'll provide advisory and implementation expertise to our clients including: use case and business case development for Cognitive Computing solutions; proof of concept execution to prove the value of Cognitive Computing use cases; solution outline and design of Cognitive Computing systems; as well as supporting business development activities.You'll have strong experience in designing and building innovative solutions based on the above technologies but you will also be have the expertise and architectural mindset to relate and integrate such solutions with existing client system infrastructures, such as e.g. Proven hands-on experience in conducting analyses on unstructured as well as structured / semi-structured data and in working with state-of the art technologies in Cognitive Computing will be expected. Examples of such technologies include but are not limited to: Natural Language Processing or Information Retrieval (e.g.

Dataset from the U.S. Department of Education that includes various metrics on outcomes from degree-granting undergraduate institutions from 1996-2015, including student debt, college completion rates, job placement, and more

Efficient usage of the knowledge provided by the Linked Data community is often hindered by the need for domain experts to formulate the right SPARQL queries to answer questions. For new questions they have to decide which datasets are suitable and in which terminology and modelling style to phrase the SPARQL query. In this work we present an evolutionary algorithm to help with this challenging task. Given a training list of source-target node-pair examples our algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The learned patterns can be visualised to form the basis for further investigation, or they can be used to predict target nodes for new source nodes. Amongst others, we apply our algorithm to a dataset of several hundred human associations (such as "circle - square") to find patterns for them in DBpedia. We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with > 7.9 billion triples. Further, we use the resulting SPARQL queries to mimic human associations with a Mean Average Precision (MAP) of 39.9 % and a Recall@10 of 63.9 %.