The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recent years have witnessed a strong upswing in the interest in rule systems technologies—both in their own right and in combination with existing Web standards. In particular, the Semantic Web is now seen as a vast playing field for rules within the academia as well as the industry. This renewed interest motivated the development of the Rule Interchange Format (RIF), a recent W3C Web standard for...
Probabilistic programming promises to make probabilistic modeling easier by making it possible to create models using the power of programming languages, and by applying general-purpose algorithms to reason about models. We present a new probabilistic programming language named Figaro that was designed with practicality and usability in mind. Figaro can represent models naturally that have been difficult...
Building on advances in statistical-relational AI and the Semantic Web, this talk outlined how to create knowledge, how to evaluate knowledge that has been published, and how to go beyond the sum of human knowledge. If there is some claim of truth, it is reasonable to ask what evidence there is for that claim, and to not believe claims that do not provide evidence. Thus we need to publish data that...
In this paper we investigate the lack of reliability and consistency of those binary rule learners in ILP that employ the one-vs-rest binarisation technique when dealing with multi-class domains. We show that we can learn a simple, consistent and reliable multi-class theory by combining the rules of the multiple one-vs-rest theories into one rule list or set. We experimentally show that our proposed...
We present a numerical refinement operator based on multi- instance learning. In the approach, the task of handling numerical vari- ables in a clause is delegated to statistical multi-instance learning schemes. To each clause, there is an associated multi-instance classification model with the numerical variables of the clause as input. Clauses are built in a greedy manner, where each refinement adds...
Augmenting vision systems with high-level knowledge and reasoning can improve lower-level vision processes, by using richer and more structured information. In this paper we tackle the problem of delimiting conceptual elements of street views based on spatial relations between lower-level components, e.g. the element ‘house’ is composed of windows and a door in a spatial arrangement. We use structured...
Logic Programs with Annotated Disjunctions (LPADs) are a promising language for Probabilistic Inductive Logic Programming. In order to develop efficient learning systems for LPADs, it is fundamental to have high-performing inference algorithms. The existing approaches take too long or fail for large problems. In this paper we adapt to LPAD the approaches for approximate inference that have been developed...
Probabilistic logic programming formalisms permit the definition of potentially very complex probability distributions. This complexity can often make learning hard, even when structure is fixed and learning reduces to parameter estimation. In this paper an approximate Bayesian computation (ABC) method is presented which computes approximations to the posterior distribution over PRISM parameters....
Traditionally, rule learners have learned deterministic rules from deterministic data, that is, the rules have been expressed as logical statements and also the examples and their classification have been purely logical. We upgrade rule learning to a probabilistic setting, in which both the examples themselves as well as their classification can be probabilistic. The setting is incorporated in the...
Structural activity prediction is one of the most important tasks in chemoinformatics. The goal is to predict a property of interest given structural data on a set of small compounds or drugs. Ideally, systems that address this task should not just be accurate, but they should also be able to identify an interpretable discriminative structure which describes the most discriminant structural elements...
Biological processes where every gene and protein participates is an essential knowledge for designing disease treatments. Nowadays, these annotations are still unknown for many genes and proteins. Since making annotations from in-vivo experiments is costly, computational predictors are needed for different kinds of annotation such as metabolic pathway, interaction network, protein family, tissue,...
ProbLog is a recently introduced probabilistic extension of Prolog. The key contribution of this paper is that we extend ProbLog with abilities to specify continuous distributions and that we show how ProbLog’s exact inference mechanism can be modified to cope with such distributions. The resulting inference engine combines an interval calculus with a dynamic discretization algorithm into an effective...
One of the main characteristics of Semantic Web (SW) data is that it is notoriously incomplete: in the same domain a great deal might be known for some entities and almost nothing might be known for others. A popular example is the well known friend-of-a-friend data set where some members document exhaustive private and social information whereas, for privacy concerns and other reasons, almost nothing...
The Robocup 2D simulation competition [13] proposes a dynamic environment where two opponent teams are confronted in a simplified soccer game. All major teams use a fixed algorithm to control its players. An unexpected opponent strategy, not previously considered by the developers, might result in winning all matches. To improve this we use ILP to learn action descriptions of opponent players; for...
Meta-level abduction discovers missing links and unknown nodes from incomplete networks to complete paths for observations. In this work, we extend applicability of meta-level abduction to deal with networks containing both positive and negative causal effects. Such networks appear in many domains including biology, in which inhibitory effects are important in signaling and metabolic pathways. Reasoning...
Existing ILP (Inductive Logic Programming) systems are implemented in different languages namely C, Progol, etc. Also, each system has its customized format for the input data. This makes it very tedious and time consuming on the part of a user to utilize such a system for experimental purposes as it demands a thorough understanding of that system and its input specification. In the spirit of Weka...
We study reducibility of examples in several typical inductive logic programming benchmarks. The notion of reducibility that we use is related to theta-reduction, commonly used to reduce hypotheses in ILP. Whereas examples are usually not reducible on their own, they often become implicitly reducible when language for constructing hypotheses is fixed. We show that number of ground facts in a dataset...
It is well known that for certain relational learning problems, traditional top-down search falls into blind search. Recent works in Inductive Logic Programming about phase transition and crossing plateau show that no general solution can face to all these difficulties. In this context, we introduce the notion of “minimal saturation” to build non-blind refinements of hypotheses in a bidirectional...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.