In ontology-based data access (OBDA), database querying is enriched with an ontology that provides domain knowledge and additional vocabulary for query formulation. We identify query emptiness and predicate emptiness as two central reasoning services in this context. Query emptiness asks whether a given query has an empty answer over all databases formulated in a given vocabulary. Predicate emptiness is defined analogously, but quantifies universally over all queries that contain a given predicate. In this paper, we determine the computational complexity of query emptiness and predicate emptiness in the EL, DL-Lite, and ALC-families of description logics, investigate the connection to ontology modules, and perform a practical case study to evaluate the new reasoning services.

Job Description 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.

Google has developed some awesome mobile applications that realize visual and audio recognition. Google just recently announced an open source Natural Language Understanding (NLU) system called SyntaxNet. This NLU system is built upon Google's TensorFlow, an open source neural network framework. Google has been able to achieve an overall 90 percent accuracy rate with their system. This is quite an accomplishment from just ten years ago, where part of speech tagging consisted of simply identifying entity extraction (verbs, nouns, etc.).

Data modeling is usually one of those subjects that make people's eyes glaze over. It's not really programming, though understanding programming concepts such as objects, inheritance, polymorphism and similar multisyllabic words is usually helpful to do modeling. Perhaps the best way of thinking about modeling is to see it as a way to describe a business in clearly defined pieces. From a business perspective, if done right, data modeling can prove quite valuable. When done badly, modeling can prove worse than useless, since a big part of the value of modeling is to employ it to make predictions.

When I returned to university to do a graduate degree, I was interested to discover how certain terms are subject to "intellectual interpretation." A word that I was asked to explain during one of my earliest classes was "ontology." Since this term was absent from my dictionary, I originally confused it with "oncology." I faintly recall that oncology involves the study of tumors. After consulting a few sources, I said that ontology is the study of how things come to exist or into being. I came across another perspective although I don't recall the source: ontology is the study of how things gain relevance or become recognized, indicating that existence can be regarded as a matter of recognition. Perhaps there is no monopoly on the exact meaning. However, I would say of ontology in relation to data science, it explains how meaning is attached to data and therefore how that data gains and retains meaning. For example, if I were asked to count the number of trucks in a parking lot, it isn't obvious what should be included: small pick-ups, tow-trucks, commercial hauling vehicles, dump trucks, and maybe heavy construction trucks.

Community annotation of biological entities with concepts from multiple bio-ontologies has created large and growing repositories of ontology-based annotation data with embedded implicit relationships among orthogonal ontologies. Development of efficient data mining methods and metrics to mine and assess the quality of the mined relationships has not kept pace with the growth of annotation data. In this study, we present a data mining method that uses ontology-guided generalization to discover relationships across ontologies along with a new interestingness metric based on information theory. We apply our data mining algorithm and interestingness measures to datasets from the Gene Expression Database at the Mouse Genome Informatics as a preliminary proof of concept to mine relationships between developmental stages in the mouse anatomy ontology and Gene Ontology concepts (biological process, molecular function and cellular component). In addition, we present a comparison of our interestingness metric to four existing metrics. Ontology-based annotation datasets provide a valuable resource for discovery of relationships across ontologies. The use of efficient data mining methods and appropriate interestingness metrics enables the identification of high quality relationships.

This is the first in a series about cross format data modeling principles. In the data modeling realm, there is perhaps no example that is as ubiquitous as modelling personal names. After all, things don't get much simpler than a name: This isn't really a model, but rather an instance of a model - an actual example that proves out the model. There are, however, a number of ways that such a model can be described. What you see is a set of assertions that identify that there exists a class named "person", and that this class has two properties.

This paper describes a system that uses a hybrid of quantitative and qualitative knowledge to solve physics word problems. Such an integration of knowledge from two models is useful to come up with the correct solution for many of these problems. We have applied this hybrid model to solve word problems from projectile motion. These types of word problems have not been addressed in recent times. We have solved a set of 30 problems in this domain.

This paper presents an ontology-based application for mobile devices which is responsible for supporting groups of people with the management of their shared tasks. The ontology stores the domain knowledge about collaborative tasks, which is used to support task recognition and relocation. Such knowledge is used by a multi-agent system that consists of a group of agents representing each person in the group. The agents use plan recognition techniques to monitor the execution of tasks according to the schedules and negotiate task allocation when needed. Our techniques have been applied in a healthcare scenario which consists of a family group that takes care of an elderly person. This paper presents an ontology-based application for mobile devices which is responsible for supporting groups of people with the management of their shared tasks. % in a healthcare scenario.The ontology stores the domain knowledge about collaborative tasks, which is used to support task recognition and relocation.Such knowledge is used by a multi-agent system that consists of a group of agents representing each person in the group.The agents use plan recognition techniques to monitor the execution of tasks according to the schedules and negotiate task allocation when needed.Our techniques have been applied in a healthcare scenario which consists of a family group that takes care of an elderly person.

Instead of seeing each Machine Learning (ML) method as a "shiny new object", here is an attempt to create a unified picture. There is no consensus when it comes to an ontology for ML methods; organizational principles are simply ways to get our arms around knowledge so that we are not swamped by too many unconnected notions. A powerful organization of the concepts or Ontology of ML is based on conditional expectation. Conditional Expectation of Class'y' given input attributes, x, denoted by E[y x]. Implementation of estimation of the conditional expectation with various assumptions lead, one way or the other, to ALL the ML techniques that we have today in 2016.