We take the category system in Wikipedia as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexicosyntactic matching. As a result we are able to derive a large scale taxonomy containing a large amount of subsumption, i.e. isa, relations. We evaluate the quality of the created resource by comparing it with ResearchCyc, one of the largest manually annotated ontologies, as well as computing semantic similarity between words in benchmarking datasets.

Combining first-order logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Unfortunately, it does not have the full power of first-order logic, because it is only defined for finite domains. This paper extends Markov logic to infinite domains, by casting it in the framework of Gibbs measures (Georgii, 1988). We show that a Markov logic network (MLN) admits a Gibbs measure as long as each ground atom has a finite number of neighbors. Many interesting cases fall in this category. We also show that an MLN admits a unique measure if the weights of its non-unit clauses are small enough. We then examine the structure of the set of consistent measures in the non-unique case. Many important phenomena, including systems with phase transitions, are represented by MLNs with non-unique measures. We relate the problem of satisfiability in first-order logic to the properties of MLN measures, and discuss how Markov logic relates to previous infinite models. 1

The field of inductive logic programming (ILP) has made steady progress, since the first ILP workshop in 1991, based on a balance of developments in theory, implementations and applications. More recently there has been an increased emphasis on Probabilistic ILP and the related fields of Statistical Relational Learning (SRL) and Structured Prediction. The goal of the current paper is to consider these emerging trends and chart out the strategic directions and open problems for the broader area of structured machine learning for the next 10 years.

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We propose a novel Adaptive Markov Chain Monte Carlo algorithm to compute the partition function. In particular, we show how to accelerate a flat histogram sampling technique by significantly reducing the number of “null moves ” in the chain, while maintaining asymptotic convergence properties. Our experiments show that our method converges quickly to highly accurate solutions on a range of benchmark instances, outperforming other state-of-the-art methods such as IJGP, TRW, and Gibbs sampling both in run-time and accuracy. We also show how obtaining a so-called density of states distribution allows for efficient weight learning in Markov Logic theories. 1

AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as input. However, building action models from scratch is a difficult and time-consuming task, even for experts. In this paper, we develop an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of observed plans. In fact, our approach works when no or partial intermediate states are given. These example plans are obtained by an observation agent who does not know the logical encoding of the actions and the full state information between the actions. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted MAX-SAT) problem and solves it using a MAX-SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness of ARMS empirically. © 2006 Elsevier B.V. All rights reserved.

AI planning has become more and more important in many real-world domains such as military applications and intelligent scheduling. However, planning systems require complete specifications of domain models, which can be difficult to encode, even for domain experts. Thus, research on effective and efficient methods to construct domain models or applicability conditions for planning automatically has become a hot topic for researchers. In this paper, we review our previous work ARMS, which can learn the applicability conditions for planning under STRIPS representations. Moreover, we provide two extensions to our ARMS system, LAMP, which can learn complex action models in PDDL representations with quantifiers and logical implications, and HTN-Learner, which can simultaneously learn method preconditions and action models in hierarchical task network (HTN) models. Our experimental results show that the two proposed algorithms could effectively learn complex action models and HTN models, thus having the ability to effectively acquire applicability conditions and relationships between actions in AI planning. 1

Maclin, to my committee members, and to the Machine Learning Group at the University of Wisconsin-Madison.

Abstract — Genealogy research is centered on collecting records about an individual from various sources and combining the information to gain a larger historical perspective about that individual, commonly in the form of a pedigree. Data extraction, the internet, and other technological advancements have made large amounts of digital genealogical data more accessible. Discovering the relevancy of a digital record to a given pedigree involves determining if the individual described in the record is in actuality an individual within the pedigree. This process is called Genealogical Record Linkage (GRL). GRL can be automated through data mining and techniques by creating machine learned models from hand labeled comparisons. In this paper, we compare two such models-a tabular approach and a graph based stacking approach-and report the successful application of both on a large, post-blocking database. We also note the successful integration of these approaches in an open source distributed genealogy program that finds relevant machetes to a given pedigree from multiple online repositories. I.