Motivated by applications in large-scale knowledge base construction, we study the problem of scaling up a sophisticated statistical inference framework called Markov Logic Networks (MLNs). Our approach, Felix, uses the idea of Lagrangian relaxation from mathematical programming to decompose a program into smaller tasks while preserving the joint-inference property of the original MLN. The advantage is that we can use highly scalable specialized algorithms for common tasks such as classification and coreference. We propose an architecture to support Lagrangian relaxation in an RDBMS which we show enables scalable joint inference for MLNs. We empirically validate that Felix is significantly more scalable and efficient than prior approaches to MLN inference by constructing a knowledge base from 1.8M documents as part of the TAC challenge. We show that Felix scales and achieves state-of-the-art quality numbers. In contrast, prior approaches do not scale even to a subset of the corpus that is three orders of magnitude smaller.

This paper presents the design and development of a proposed rule based Decision Support System that will help students in selecting the best suitable faculty/major decision while taking admission in Gomal University, Dera Ismail Khan, Pakistan. The basic idea of our approach is to design a model for testing and measuring the student capabilities like intelligence, understanding, comprehension, mathematical concepts plus his/her past academic record plus his/her intelligence level, and applying the module results to a rule-based decision support system to determine the compatibility of those capabilities with the available faculties/majors in Gomal University. The result is shown as a list of suggested faculties/majors with the student capabilities and abilities.

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.

Seven years on from OWL becoming a W3C recommendation, and two years on from the more recent OWL 2 W3C recommendation, OWL has still experienced only patchy uptake on the Web. Although certain OWL features (like owl:sameAs) are very popular, other features of OWL are largely neglected by publishers in the Linked Data world. This may suggest that despite the promise of easy implementations and the proposal of tractable profiles suggested in OWL's second version, there is still no "right" standard fragment for the Linked Data community. In this paper, we (1) analyse uptake of OWL on the Web of Data, (2) gain insights into the OWL fragment that is actually used/usable on the Web, where we arrive at the conclusion that this fragment is likely to be a simplified profile based on OWL RL, (3) propose and discuss such a new fragment, which we call OWL LD (for Linked Data).

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Previous approaches are not high-throughput, are not generalizable or scalable, or lack sufficient data to be effective. We describe single mechanistic reactions as concerted electron movements from an electron orbital source to an electron orbital sink. We use an existing rule-based expert system to derive a dataset consisting of 2,989 productive mechanistic steps and 6.14 million non-productive mechanistic steps. We then pose identifying productive mechanistic steps as a ranking problem: rank potential orbital interactions such that the top ranked interactions yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.0% of non-productive reactions with less than a 0.1% false negative rate. Then, we train an ensemble of ranking models on pairs of interacting orbitals to learn a relative productivity function over single mechanistic reactions in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanisms at the top 89.1% of the time, rising to 99.9% of the time when top ranked lists with at most four non-productive reactions are considered. The final system allows multi-step reaction prediction. Furthermore, it is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert system does not handle.

Data mining is a new concept & an exploration and analysis of large data sets, in order to discover meaningful patterns and rules. Many organizations are now using the data mining techniques to find out meaningful patterns from the database. The present paper studies how data mining techniques can be apply to the large database. These data mining techniques give certain behavioral pattern from the database. The results which come after analysis of the database are useful for organization. This paper examines the result after applying association rule mining technique, rule induction technique and Apriori algorithm. These techniques are applied to the database of shopping mall. Market basket analysis is performing by the above mentioned techniques and some important results are found such as buying behavior.

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

In this paper, we discuss approaches to dialogue in interactive video games and interactive narrative research. We propose that situating interactive dialogue in the simplified expectations of video games is a profitable way to investigate computational dialogue simulation. Taking cues from existing physical simulations such as combat, we propose a hypothetical game environment and design goals for an embedded interactive dialogue system. We present a modular framework targeted at that environment, which is designed to enable incremental development and exploration of dialogue concepts. We describe this framework together with a work-in-progress system for simulating simple in-game negotiation dialogues.

Authoring interactive stories where the player is afforded a wide range of social interactions results in a very large space of possible social and story situations. The amount of effort required to individually author for each of these circumstances can quickly become intractable. The social AI system Comme il Faut (CiF) aims to reduce the burden on the author by providing a playable model of social interaction where the author provides reusable and recombinable representations of social norms and social interactions. Motivated through examples from an in-development video game, Prom Week, this paper provides a detailed description of the structures with which CiF represents social knowledge and how this knowledge is employed to simulate social interactions between characters.

The paper concerns selected rule modularization techniques. Three visual methods for inference specification for modularized rule- bases are described: Drools Flow, BPMN and XTT2. Drools Flow is a popular technology for workflow or process modeling, BPMN is an OMG standard for modeling business processes, and XTT2 is a hierarchical tab- ular system specification method. Because of some limitations of these solutions, several proposals of their integration are given.