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69
Joint inference in information extraction
 In Proceedings of the 22nd National Conference on Artificial Intelligence (2007
"... The goal of information extraction is to extract database records from text or semistructured sources. Traditionally, information extraction proceeds by first segmenting each candidate record separately, and then merging records that refer to the same entities. While computationally efficient, this ..."
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Cited by 78 (8 self)
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The goal of information extraction is to extract database records from text or semistructured sources. Traditionally, information extraction proceeds by first segmenting each candidate record separately, and then merging records that refer to the same entities. While computationally efficient, this approach is suboptimal, because it ignores the fact that segmenting one candidate record can help to segment similar ones. For example, resolving a wellsegmented field with a lessclear one can disambiguate the latter’s boundaries. In this paper we propose a joint approach to information extraction, where segmentation of all records and entity resolution are performed together in a single integrated inference process. While a number of previous authors have taken steps in this direction (e.g., Pasula et al. (2003), Wellner et al. (2004)), to our knowledge this is the first fully joint approach. In experiments on the CiteSeer and Cora citation matching datasets, joint inference improved accuracy, and our approach outperformed previous ones. Further, by using Markov logic and the existing algorithms for it, our solution consisted mainly of writing the appropriate logical formulas, and required much less engineering than previous ones.
Efficient weight learning for Markov logic networks
 In Proceedings of the Eleventh European Conference on Principles and Practice of Knowledge Discovery in Databases
, 2007
"... Abstract. Markov logic networks (MLNs) combine Markov networks and firstorder logic, and are a powerful and increasingly popular representation for statistical relational learning. The stateoftheart method for discriminative learning of MLN weights is the voted perceptron algorithm, which is ess ..."
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Cited by 57 (7 self)
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Abstract. Markov logic networks (MLNs) combine Markov networks and firstorder logic, and are a powerful and increasingly popular representation for statistical relational learning. The stateoftheart method for discriminative learning of MLN weights is the voted perceptron algorithm, which is essentially gradient descent with an MPE approximation to the expected sufficient statistics (true clause counts). Unfortunately, these can vary widely between clauses, causing the learning problem to be highly illconditioned, and making gradient descent very slow. In this paper, we explore several alternatives, from perweight learning rates to secondorder methods. In particular, we focus on two approaches that avoid computing the partition function: diagonal Newton and scaled conjugate gradient. In experiments on standard SRL datasets, we obtain orderofmagnitude speedups, or more accurate models given comparable learning times. 1
Event modeling and recognition using markov logic networks
 IN ECCV
, 2008
"... We address the problem of visual event recognition in surveillance where noise and missing observations are serious problems. Common sense domain knowledge is exploited to overcome them. The knowledge is represented as firstorder logic production rules with associated weights to indicate their con ..."
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Cited by 43 (3 self)
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We address the problem of visual event recognition in surveillance where noise and missing observations are serious problems. Common sense domain knowledge is exploited to overcome them. The knowledge is represented as firstorder logic production rules with associated weights to indicate their confidence. These rules are used in combination with a relaxed deduction algorithm to construct a network of grounded atoms, the Markov Logic Network. The network is used to perform probabilistic inference for input queries about events of interest. The system’s performance is demonstrated on a number of videos from a parking lot domain that contains complex interactions of people and vehicles.
Assieme: finding and leveraging implicit references in a web search interface for programmers
 In: 20th annual ACM Symposium on User Interface Software and Technology
, 2007
"... Programmers regularly use search as part of the development process, attempting to identify an appropriate API for a problem, seeking more information about an API, and seeking samples that show how to use an API. However, neither generalpurpose search engines nor existing code search engines curre ..."
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Cited by 30 (3 self)
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Programmers regularly use search as part of the development process, attempting to identify an appropriate API for a problem, seeking more information about an API, and seeking samples that show how to use an API. However, neither generalpurpose search engines nor existing code search engines currently fit their needs, in large part because the information programmers need is distributed across many pages. We present Assieme, a Web search interface that effectively supports common programming search tasks by combining information from Webaccessible Java Archive (JAR) files, API documentation, and pages that include explanatory text and sample code. Assieme uses a novel approach to finding and resolving implicit references to Java packages, types, and members within sample code on the Web. In a study of programmers performing searches related to common programming tasks, we show that programmers obtain better solutions, using fewer queries, in the same amount of time spent using a general Web search interface. ACM Classification
Learning Markov logic network structure via hypergraph lifting
 In Proceedings of the 26th International Conference on Machine Learning (ICML09
, 2009
"... Markov logic networks (MLNs) combine logic and probability by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. Learning MLN structure from a relational database involves learning the clauses and weights. The stateoftheart MLN structure lear ..."
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Cited by 30 (3 self)
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Markov logic networks (MLNs) combine logic and probability by attaching weights to firstorder clauses, and viewing these as templates for features of Markov networks. Learning MLN structure from a relational database involves learning the clauses and weights. The stateoftheart MLN structure learners all involve some element of greedily generating candidate clauses, and are susceptible to local optima. To address this problem, we present an approach that directly utilizes the data in constructing candidates. A relational database can be viewed as a hypergraph with constants as nodes and relations as hyperedges. We find paths of true ground atoms in the hypergraph that are connected via their arguments. To make this tractable (there are exponentially many paths in the hypergraph), we lift the hypergraph by jointly clustering the constants to form higherlevel concepts, and find paths in it. We variabilize the ground atoms in each path, and use them to form clauses, which are evaluated using a pseudolikelihood measure. In our experiments on three realworld datasets, we find that our algorithm outperforms the stateoftheart approaches. 1.
Largescale deduplication with constraints using dedupalog
 in: Proceedings of the 25th International Conference on Data Engineering (ICDE
"... Abstract — We present a declarative framework for collective deduplication of entity references in the presence of constraints. Constraints occur naturally in many data cleaning domains and can improve the quality of deduplication. An example of a constraint is “each paper has a unique publication v ..."
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Cited by 27 (2 self)
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Abstract — We present a declarative framework for collective deduplication of entity references in the presence of constraints. Constraints occur naturally in many data cleaning domains and can improve the quality of deduplication. An example of a constraint is “each paper has a unique publication venue”; iftwo paper references are duplicates, then their associated conference references must be duplicates as well. Our framework supports collective deduplication, meaning that we can dedupe both paper references and conference references collectively in the example above. Our framework is based on a simple declarative Datalogstyle language with precise semantics. Most previous work on deduplication either ignore constraints or use them in an adhoc domainspecific manner. We also present efficient algorithms to support the framework. Our algorithms have precise theoretical guarantees for a large subclass of our framework. We show, using a prototype implementation, that our algorithms scale to very large datasets. We provide thorough experimental results over realworld data demonstrating the utility of our framework for highquality and scalable deduplication. I.
Learning Markov Logic Networks Using Structural Motifs
"... Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (45 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we presen ..."
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Cited by 24 (1 self)
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Markov logic networks (MLNs) use firstorder formulas to define features of Markov networks. Current MLN structure learners can only learn short clauses (45 literals) due to extreme computational costs, and thus are unable to represent complex regularities in data. To address this problem, we present LSM, the first MLN structure learner capable of efficiently and accurately learning long clauses. LSM is based on the observation that relational data typically contains patterns that are variations of the same structural motifs. By constraining the search for clauses to occur within motifs, LSM can greatly speed up the search and thereby reduce the cost of finding long clauses. LSM uses random walks to identify densely connected objects in data, and groups them and their associated relations into a motif. Our experiments on three realworld datasets show that our approach is 25 orders of magnitude faster than the stateoftheart ones, while achieving the same or better predictive performance. 1.
Unifying logical and statistical AI
 Proceedings of the TwentyFirst National Conference on Artificial Intelligence
, 2006
"... Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to firstorder formulas and viewing them as templates for features of Markov n ..."
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Cited by 22 (5 self)
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Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to firstorder formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on ideas from satisfiability, Markov chain Monte Carlo and knowledgebased model construction. Learning algorithms are based on the voted perceptron, pseudolikelihood and inductive logic programming. Markov logic has been successfully applied to problems in entity resolution, link prediction, information extraction and others, and is the basis of the opensource Alchemy system.
Unsupervised methods for determining object and relation synonyms on the web
 Journal of Artificial Intelligence Research
, 2009
"... The task of identifying synonymous relations and objects, or synonym resolution, is critical for highquality information extraction. This paper investigates synonym resolution in the context of unsupervised information extraction, where neither handtagged training examples nor domain knowledge is ..."
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Cited by 21 (2 self)
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The task of identifying synonymous relations and objects, or synonym resolution, is critical for highquality information extraction. This paper investigates synonym resolution in the context of unsupervised information extraction, where neither handtagged training examples nor domain knowledge is available. The paper presents a scalable, fullyimplemented system that runs in O(KN log N) time in the number of extractions, N, and the maximum number of synonyms per word, K. The system, called Resolver, introduces a probabilistic relational model for predicting whether two strings are coreferential based on the similarity of the assertions containing them. On a set of two million assertions extracted from the Web, Resolver resolves objects with 78 % precision and 68 % recall, and resolves relations with 90 % precision and 35 % recall. Several variations of Resolver’s probabilistic model are explored, and experiments demonstrate that under appropriate conditions these variations can improve F1 by 5%. An extension to the basic Resolver system allows it to handle polysemous names with 97 % precision and 95 % recall on a data set from the TREC corpus.
Unsupervised Resolution of Objects and Relations on the Web
"... The task of identifying synonymous relations and objects, or Synonym Resolution (SR), is critical for highquality information extraction. The bulk of previous SR work assumed strong domain knowledge or handtagged training examples. This paper investigates SR in the context of unsupervised informat ..."
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Cited by 20 (6 self)
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The task of identifying synonymous relations and objects, or Synonym Resolution (SR), is critical for highquality information extraction. The bulk of previous SR work assumed strong domain knowledge or handtagged training examples. This paper investigates SR in the context of unsupervised information extraction, where neither is available. The paper presents a scalable, fullyimplemented system for SR that runs in O(KN log N) time in the number of extractions N and the maximum number of synonyms per word, K. The system, called RESOLVER, introduces a probabilistic relational model for predicting whether two strings are coreferential based on the similarity of the assertions containing them. Given two million assertions extracted from the Web, RESOLVER resolves objects with 78 % precision and an estimated 68 % recall and resolves relations with 90 % precision and 35 % recall. 1