We propose a family of probabilistic description logics (DLs) that are derived in a principled way from Halpern's probabilistic first-order logic. The resulting probabilistic DLs have a two-dimensional semantics similar to temporal DLs and are well-suited for representing subjective probabilities. We carry out a detailed study of reasoning in the new family of logics, concentrating on probabilistic extensions of the DLs ALC and EL, and showing that the complexity ranges from PTime via ExpTime and 2ExpTime to undecidable.

We consider existential rules (aka Datalog+) as a formalism for specifying ontologies. In recent years, many classes of existential rules have been exhibited for which conjunctive query (CQ) entailment is decidable. However, most of these classes cannot express transitivity of binary relations, a frequently used modelling construct. In this paper, we address the issue of whether transitivity can be safely combined with decidable classes of existential rules. First, we prove that transitivity is incompatible with one of the simplest decidable classes, namely aGRD (acyclic graph of rule dependencies), which clarifies the landscape of `finite expansion sets' of rules. Second, we show that transitivity can be safely added to linear rules (a subclass of guarded rules, which generalizes the description logic DL-Lite-R) in the case of atomic CQs, and also for general CQs if we place a minor syntactic restriction on the rule set. This is shown by means of a novel query rewriting algorithm that is specially tailored to handle transitivity rules. Third, for the identified decidable cases, we pinpoint the combined and data complexities of query entailment.

As you may have noticed, the discussion here talks about names because they usually hide a lot of unstated assumptions, but the reality is this is as relevant to most data structures that you have within enterprise level ontologies. The bigger take-away here is to understand that we're moving into a world where data conversions and data integration dominate, and the kind of thinking that wants to just add a couple of fields to an object to represent a name or other singleton entries (addresses, contact information, emails, companies, stores, the list is pretty much endless) is likely to get you into trouble, especially when data worlds collide. In my next article in this series (check back here for the new link) I'll look at the temporal aspect of change, as well as exploring controlled vocabularies and how they fit into contemporary data modeling.

This page provides a structured representation (serialized as HTML RDFa) of the description of the entity denoted ("referred to") by the hyperlink that anchors the About: Entity Label text at the top. The data is presented here in the form of a collection of Entity- Attribute- Value (EAV) or Subject- Predicate- Object (SPO) relations. In conformance with core Web Architecture, the same description data may also be retrieved in a variety of other negotiable serialization formats, which currently include CSV, HTML Microdata, (X)HTML RDFa, N-Triples, Turtle, N3, RDF/JSON, JSON-LD, RDF/XML, Atom, and CXML. Why is this page important? This page and its neighbors provide 5-Star Linked Data URIs (Web Super Keys) for HTTP-accessible data.

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.

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.