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• W1849811180 abstract "Traditionally, machine learning algorithms assume that training data is provided as a set of independent instances, each of which can be described as a feature vector. In contrast, many domains of interest are inherently multi-relational, consisting of entities connected by a rich set of relations. For example, the participants in a social network are linked by friendships, collaborations, and shared interests. Likewise, the users of a search engine are related by searches for similar items and clicks to shared sites. The ability to model and reason about such relations is essential not only because better predictive accuracy is achieved by exploiting this additional information, but also because frequently the goal is to predict whether a set of entities are related in a particular way. This thesis falls within the area of Statistical Relational Learning (SRL), which combines ideas from two traditions within artificial intelligence, first-order logic and probabilistic graphical models, to address the challenge of learning from multi-relational data. We build on one particular SRL model, Markov logic networks (MLNs), which consist of a set of weighted first-order-logic formulae and provide a principled way of defining a probability distribution over possible worlds. We develop algorithms for learning of MLN structure both from scratch and by transferring a previously learned model, as well as an application of MLNs to the problem of Web query disambiguation. The ideas we present are unified by two main themes: the need to deal with limited training data and the use of bottom-up learning techniques. Structure learning, the task of automatically acquiring a set of dependencies among the relations in the domain, is a central problem in SRL. We introduce BUSL, an algorithm for learning MLN structure from scratch that proceeds in a more bottom-up fashion, breaking away from the tradition of top-down learning typical in SRL. Our approach first constructs a novel data structure called a Markov network template that is used to restrict the search space for clauses. Our experiments in three relational domains demonstrate that BUSL dramatically reduces the search space for clauses and attains a significantly higher accuracy than a structure learner that follows a top-down approach. Accurate and efficient structure learning can also be achieved by transferring a model obtained in a source domain related to the current target domain of interest. We view transfer as a revision task and present an algorithm that diagnoses a source MLN to determine which of its parts transfer directly to the target domain and which need to be updated. This analysis focuses the search for revisions on the incorrect portions of the source structure, thus speeding up learning. Transfer learning is particularly important when target-domain data is limited, such as when data on only a few individuals is available from domains with hundreds of entities connected by a variety of relations. We also address this challenging case and develop a general transfer learning approach that makes effective use of such limited target data in several social network domains. Finally, we develop an application of MLNs to the problem of Web query disambiguation in a more privacy-aware setting where the only information available about a user is that captured in a short search session of 5–6 previous queries on average. This setting contrasts with previous work that typically assumes the availability of long user-specific search histories. To compensate for the scarcity of user-specific information, our approach exploits the relations between users, search terms, and URLs. We demonstrate the effectiveness of our approach in the presence of noise and show that it outperforms several natural baselines on a large data set collected from the MSN search engine." @default.
• W1849811180 created "2016-06-24" @default.
• W1849811180 creator A5008715111 @default.
• W1849811180 creator A5075943557 @default.
• W1849811180 date "2009-01-01" @default.
• W1849811180 modified "2023-09-27" @default.
• W1849811180 title "Learning with markov logic networks: transfer learning, structure learning, and an application to web query disambiguation" @default.
• W1849811180 cites W121830907 @default.
• W1849811180 cites W1492019259 @default.
• W1849811180 cites W1502166968 @default.
• W1849811180 cites W1522868916 @default.
• W1849811180 cites W1524326598 @default.
• W1849811180 cites W1532325895 @default.
• W1849811180 cites W1541752891 @default.
• W1849811180 cites W1554544485 @default.
• W1849811180 cites W1560550898 @default.
• W1849811180 cites W1585529040 @default.
• W1849811180 cites W1597005304 @default.
• W1849811180 cites W1599188306 @default.
• W1849811180 cites W1791364091 @default.
• W1849811180 cites W1877678056 @default.
• W1849811180 cites W1912083604 @default.
• W1849811180 cites W192266559 @default.
• W1849811180 cites W1947191636 @default.
• W1849811180 cites W1976719972 @default.
• W1849811180 cites W1977970897 @default.
• W1849811180 cites W1978486030 @default.
• W1849811180 cites W2000145992 @default.
• W1849811180 cites W2008652694 @default.
• W1849811180 cites W2034927834 @default.
• W1849811180 cites W2037098674 @default.
• W1849811180 cites W2047221353 @default.
• W1849811180 cites W2051434435 @default.
• W1849811180 cites W2078786834 @default.
• W1849811180 cites W2085937320 @default.
• W1849811180 cites W2095976990 @default.
• W1849811180 cites W2096658177 @default.
• W1849811180 cites W2097129520 @default.
• W1849811180 cites W2097841680 @default.
• W1849811180 cites W2098704351 @default.
• W1849811180 cites W2098723043 @default.
• W1849811180 cites W2104895009 @default.
• W1849811180 cites W2105649996 @default.
• W1849811180 cites W2106953752 @default.
• W1849811180 cites W2110325612 @default.
• W1849811180 cites W2111363262 @default.
• W1849811180 cites W2113185699 @default.
• W1849811180 cites W2113858518 @default.
• W1849811180 cites W2114861953 @default.
• W1849811180 cites W2119831128 @default.
• W1849811180 cites W2120479337 @default.
• W1849811180 cites W2121075864 @default.
• W1849811180 cites W2121906195 @default.
• W1849811180 cites W2122410182 @default.
• W1849811180 cites W2122841972 @default.
• W1849811180 cites W2123470622 @default.
• W1849811180 cites W2125027602 @default.
• W1849811180 cites W2125771191 @default.
• W1849811180 cites W2126185296 @default.
• W1849811180 cites W2128905965 @default.
• W1849811180 cites W2129564794 @default.
• W1849811180 cites W2130903752 @default.
• W1849811180 cites W2131102221 @default.
• W1849811180 cites W2134131174 @default.
• W1849811180 cites W2134153324 @default.
• W1849811180 cites W2134525017 @default.
• W1849811180 cites W2134845968 @default.
• W1849811180 cites W2136801552 @default.
• W1849811180 cites W2139873966 @default.
• W1849811180 cites W2140141795 @default.
• W1849811180 cites W2143331230 @default.
• W1849811180 cites W2144159061 @default.
• W1849811180 cites W2144429462 @default.
• W1849811180 cites W2145454741 @default.
• W1849811180 cites W2145629100 @default.
• W1849811180 cites W2145734075 @default.
• W1849811180 cites W2147071755 @default.
• W1849811180 cites W2147982124 @default.
• W1849811180 cites W2148304323 @default.
• W1849811180 cites W2150678881 @default.
• W1849811180 cites W2152314154 @default.
• W1849811180 cites W2153353285 @default.
• W1849811180 cites W2154328025 @default.
• W1849811180 cites W2156346614 @default.
• W1849811180 cites W2156493855 @default.
• W1849811180 cites W2158108973 @default.
• W1849811180 cites W2158150115 @default.
• W1849811180 cites W2158292827 @default.
• W1849811180 cites W2159080219 @default.
• W1849811180 cites W2160842254 @default.
• W1849811180 cites W2162077280 @default.
• W1849811180 cites W2162122584 @default.
• W1849811180 cites W2163302275 @default.
• W1849811180 cites W2163375626 @default.
• W1849811180 cites W2164456230 @default.
• W1849811180 cites W2164524038 @default.
• W1849811180 cites W2165744911 @default.
• W1849811180 cites W2166741250 @default.
• W1849811180 cites W2167044614 @default.
• W1849811180 cites W2168717408 @default.

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