We study description logics (DLs) supporting number restrictions on transitive roles. We first take a look at SOQ and SON with binary and unary coding of numbers, and provide algorithms for the satisfiability problem and tight complexity bounds ranging from EXPTIME to NEXPTIME. We then show that by allowing for counting only up to one (functionality), inverse roles and role inclusions can be added without losing decidability. We finally investigate DLs of the DL-Lite-family, and show that, in the presence of role inclusions, the core fragment becomes undecidable.

To ensure completeness Description Logics (DLs) are popular languages for knowledge of the method, the so-called refinement step is used that recomputes representation and reasoning. They are the underlying the abstraction based on new (sound) entailments formalism for the standardized Web Ontology Language obtained from a previous abstraction. This has the added OWL, which is widely used in many application areas. Recent benefit that not only consistency but also the full materialization years have also seen an increasing interest in ontologybased of the ABox can be computed without (rather expensive) data access, where a TBox with background knowledge, explanation computations or repeated consistency often expressed in a DL language, is used to enrich checks. This paper significantly advances the abstraction refinement datasets (ABoxes), which are then accessible via queries.

Probabilistic databases (PDBs) are usually incomplete, e.g., containing only the facts that have been extracted from the Web with high confidence. However, missing facts are often treated as being false, which leads to unintuitive results when querying PDBs. Recently, open-world probabilistic databases (OpenPDBs) were proposed to address this issue by allowing probabilities of unknown facts to take any value from a fixed probability interval. In this paper, we extend OpenPDBs by Datalog+/- ontologies, under which both upper and lower probabilities of queries become even more informative, enabling us to distinguish queries that were indistinguishable before. We show that the dichotomy between P and PP in (Open)PDBs can be lifted to the case of first-order rewritable positive programs (without negative constraints); and that the problem can become NP^PP-complete, once negative constraints are allowed. We also propose an approximating semantics that circumvents the increase in complexity caused by negative constraints.

Ontology-based data integration systems allow users to effectively access data sitting in multiple sources by means of queries over a global schema described by an ontology. In practice, datasources often contain sensitive information that the data owners want to keep inaccessible to users. In this paper, we formalize and study the problem of determining whether a given data integration system discloses a source query to an attacker. We consider disclosure on a particular dataset, and also whether a schema admits a dataset on which disclosure occurs. We provide lower and upper bounds on disclosure analysis, in the process introducing a number of techniques for analyzing logical privacy issues in ontology-based data integration.

Fact checking is an essential part of any investigative work. For linguistic, psychological and social reasons, it is an inherently human task. Yet, modern media make it increasingly difficult for experts to keep up with the pace at which information is produced. Hence, we believe there is value in tools to assist them in this process. Much of the effort on Web data research has been focused on coping with incompleteness and uncertainty. Comparatively, dealing with context has received less attention, although it is crucial in judging the validity of a claim. For instance, what holds true in a US state, might not in its neighbors, e.g., due to obsolete or superseded laws. In this work, we address the problem of checking the validity of claims in multiple contexts. We define a language to represent and query facts across different dimensions. The approach is non-intrusive and allows relatively easy modeling, while capturing incompleteness and uncertainty. We describe the syntax and semantics of the language. We present algorithms to demonstrate its feasibility, and we illustrate its usefulness through examples.

The history of Artificial Intelligence," said my computer science professor on the first day of class, "is a history of failure." This harsh judgment summed up 50 years of trying to get computers to think. Sure, they could crunch numbers a billion times faster in 2000 than they could in 1950, but computer science pioneer and genius Alan Turing had predicted in 1950 that machines would be thinking by 2000: Capable of human levels of creativity, problem solving, personality, and adaptive behavior. Maybe they wouldn't be conscious (that question is for the philosophers), but they would have personalities and motivations, like Robbie the Robot or HAL 9000. Not only did we miss the deadline, but we don't even seem to be close.

In order to compare and analyse open data across cities, standard representations or ontologies have to be created. This paper defines a shelter ontology that includes concepts of shelters, slums, households and homelessness. The design of the ontology is based upon the data requirements of ISO 37120. ISO 37120 defines 100 indicators to measure and compare city performance. There are three shelter-themed indicators defined, namely 15.1 Percentage of city population living in slums, 15.2 Number of homeless per 100 000 population, and 15.3 Percentage of households that exist without registered legal titles. This ontology enables both the representation of the ISO 37120 Shelter theme indicators' definitions, and a city's indicator values and supporting data. This enables the analysis of city indicators by intelligent agents.

Traditionally, the needs of vulnerable populations have been addressed by a plethora of public and private agencies that rely on donations of money, goods and services which they distribute based on their perception of what is needed and where. This approach, however, lacks a comprehensive understanding of the demand side as well as the ability to coordinate between various suppliers of goods and services, identify latent supply and predict future demand. To help address these issues, we have developed a knowledge-based platform that harnesses advances in several AI fields for efficient and effective provisioning of goods and services.

The Leibniz Center for Law has its roots in the former department of Computer Science & Law of the Law Faculty of the University of Amsterdam, and currently houses about 15 researchers. The Leibniz Center conducts research and provides education in the field of Artificial Intelligence and law. In the tradition of Leibniz, we focus on the development and application of techniques from Artificial Intelligence to the field of Law for the purpose of supporting legal practice, and bringing new insights to legal theory. The Leibniz Center for Law has longstanding experience on legal ontologies, automatic legal reasoning and legal knowledge-based systems, (standard) languages for representing legal knowledge and information, user-friendly disclosure of legal data, and the application of ICT in education and legal practice (e.g. It plays an important role in the development of eGovernment on both national and international level. The center provides advice on change-management issues of knowledge-intensive legal processes and the improvement of knowledge-productivity in legal organisations.

Largest free, formal ontology available, with 25,000 terms and 80,000 axioms when all domain ontologies are combined. These consist of SUMO itself, the MId-Level Ontology (MILO), and ontologies of communications, countries and regions, distributed computing and user interfaces, economy, finance, automobiles and engineering components, Food, Dining, Sports, Shopping catalogs and Hotels, geography, government and Justice, language taxonomy, media and Music, Military (general, devices, processes, people), North American Industrial Classification System, people and their Emotions, physical elements, transnational issues, transportation and its Details, viruses, world airports A-K, world airports L-Z, weapons of mass destruction. See also a large amount of instance content from DBPedia about people and the YAGO, project which includes millions of facts from Wikipedia merged with SUMO, and an initial merge of the Mondial geographical data with SUMO. The Open Biomedical Ontologies are lightly mapped to SUMO. Additional ontologies of terrorism are available on request.