We propose the Parametrized Fielded Sequential Dependence Model (PFSDM) and the Parametrized Fielded Full Dependence Model (PFFDM), two novel models for entity retrieval from knowledge graphs, which infer the user's intent behind each individual query concept by dynamically estimating its projection onto the fields of structured entity representations based on a small number of statistical and linguistic features.
Automated building of taxonomies for search enginesBoris Galitsky
We build a taxonomy of entities which is intended to improve the relevance of search engine in a vertical domain. The taxonomy construction process starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (their generalization) is applied to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration.
Taxonomy and paragraph-level syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. Proposed algorithm is implemented as a part of Apache OpenNLP.Similarity project.
This document discusses different informed search strategies for artificial intelligence problems. It begins by introducing best-first search and how it selects nodes for expansion based on an evaluation function. A* search is then described, which uses an admissible heuristic function to estimate costs. The document provides an example of running A* search on a problem involving traveling between cities in Romania. It evaluates A* search and discusses variants like iterative-deepening A* and recursive best-first search that aim to reduce its space complexity issues.
Mit203 analysis and design of algorithmssmumbahelp
This document provides information about getting fully solved assignments for the 4th semester MCA program. It includes the subject code and name (MCA4040 - Analysis and Design of Algorithms), credit hours (4), and marks (60). Students are instructed to send their semester and specialization details to help.mbaassignments@gmail.com or call 08263069601 to receive solved assignments. The document includes 6 questions related to algorithm properties, sequential search, topological sort with example, Boyer-Moore algorithm, knapsack problem using memory functions, and variable length encoding and Huffman encoding.
This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.
This document summarizes a research paper on strategic argumentation and its relationship to defeasible logic. It contains the following key points:
1. Strategic argumentation involves an adversarial dialogue game where players aim to prove or disprove a claim while avoiding playing arguments that could be used against them.
2. Deciding the outcome of an argument at each turn can be computed in polynomial time, but deciding the optimal set of arguments to play (the strategic argumentation problem) is NP-complete.
3. Defeasible logic can be used to model strategic argumentation and compute argument outcomes. The complexity results also apply to defeasible semantics and grounded semantics.
The problem considered is that of finding frequent subpaths of a database of paths in a fixed undirected
graph. This problem arises in applications such as predicting congestion in network and vehicular traffic.
An algorithm, called AFS, based on the classic frequent itemset mining algorithm Apriori is developed, but
with significantly improved efficiency over Apriori from exponential in transaction size to quadratic through exploiting the underlying graph structure. This efficiency makes AFS feasible for practical input path sizes. It is also proved that a natural generalization of the frequent subpaths problem is not amenable to any solution quicker than Apriori.
A Distributed Tableau Algorithm for Package-based Description LogicsJie Bao
The document describes a distributed tableau algorithm for reasoning with modular ontologies expressed in Package-based Description Logics (P-DL). The algorithm uses multiple local reasoners, each maintaining a local tableau for a single ontology module. Local reasoners communicate by querying each other or reporting clashes to collectively construct a global tableau without fully integrating the modules. The algorithm is proven sound and complete for P-DL with acyclic module importing. It can support reasoning across modules to answer queries.
Rules for inducing hierarchies from social tagging dataHang Dong
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.
Automated building of taxonomies for search enginesBoris Galitsky
We build a taxonomy of entities which is intended to improve the relevance of search engine in a vertical domain. The taxonomy construction process starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (their generalization) is applied to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration.
Taxonomy and paragraph-level syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. Proposed algorithm is implemented as a part of Apache OpenNLP.Similarity project.
This document discusses different informed search strategies for artificial intelligence problems. It begins by introducing best-first search and how it selects nodes for expansion based on an evaluation function. A* search is then described, which uses an admissible heuristic function to estimate costs. The document provides an example of running A* search on a problem involving traveling between cities in Romania. It evaluates A* search and discusses variants like iterative-deepening A* and recursive best-first search that aim to reduce its space complexity issues.
Mit203 analysis and design of algorithmssmumbahelp
This document provides information about getting fully solved assignments for the 4th semester MCA program. It includes the subject code and name (MCA4040 - Analysis and Design of Algorithms), credit hours (4), and marks (60). Students are instructed to send their semester and specialization details to help.mbaassignments@gmail.com or call 08263069601 to receive solved assignments. The document includes 6 questions related to algorithm properties, sequential search, topological sort with example, Boyer-Moore algorithm, knapsack problem using memory functions, and variable length encoding and Huffman encoding.
This document discusses artificial intelligence for game playing. It introduces different types of games and optimal strategies for games like minimax and alpha-beta pruning. It also discusses challenges for games of imperfect information that include elements of chance, as well as techniques for heuristic evaluation and expected value calculations when chance is involved.
This document summarizes a research paper on strategic argumentation and its relationship to defeasible logic. It contains the following key points:
1. Strategic argumentation involves an adversarial dialogue game where players aim to prove or disprove a claim while avoiding playing arguments that could be used against them.
2. Deciding the outcome of an argument at each turn can be computed in polynomial time, but deciding the optimal set of arguments to play (the strategic argumentation problem) is NP-complete.
3. Defeasible logic can be used to model strategic argumentation and compute argument outcomes. The complexity results also apply to defeasible semantics and grounded semantics.
The problem considered is that of finding frequent subpaths of a database of paths in a fixed undirected
graph. This problem arises in applications such as predicting congestion in network and vehicular traffic.
An algorithm, called AFS, based on the classic frequent itemset mining algorithm Apriori is developed, but
with significantly improved efficiency over Apriori from exponential in transaction size to quadratic through exploiting the underlying graph structure. This efficiency makes AFS feasible for practical input path sizes. It is also proved that a natural generalization of the frequent subpaths problem is not amenable to any solution quicker than Apriori.
A Distributed Tableau Algorithm for Package-based Description LogicsJie Bao
The document describes a distributed tableau algorithm for reasoning with modular ontologies expressed in Package-based Description Logics (P-DL). The algorithm uses multiple local reasoners, each maintaining a local tableau for a single ontology module. Local reasoners communicate by querying each other or reporting clashes to collectively construct a global tableau without fully integrating the modules. The algorithm is proven sound and complete for P-DL with acyclic module importing. It can support reasoning across modules to answer queries.
Rules for inducing hierarchies from social tagging dataHang Dong
Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.
The document proposes adapting OWL as a more modular ontology language by addressing weaknesses in its current modularity. Specifically, OWL lacks:
1) Semantic modularity as it only supports global semantics between imported ontologies.
2) Syntactic modularity as imports can lead to tangled definitions between modules.
The paper suggests approaches to enhance OWL's modularity while maintaining backwards compatibility, such as giving imports a localized semantics or defining explicit syntactic rules to avoid nested definitions across modules.
Divide and Conquer Semantic Web with ModularJie Bao
This document provides a brief review of modular ontology language formalisms. It discusses the need for modular ontologies to address issues with large, monolithic ontologies. Several approaches to modular ontologies are summarized, including Distributed Description Logics (DDL), E-Connections, and Package-based Description Logics (P-DL). Key challenges with modular ontologies are also outlined, such as reasoning across modules and ensuring interoperability while preserving local semantics.
Representing and Reasoning with Modular OntologiesJie Bao
The document discusses representing and reasoning with modular ontologies. It introduces the need for modularity in large ontologies to enable reuse and selective knowledge hiding. It presents package-based description logics (P-DL) as a formalism for representing and reasoning with modular ontologies through package extension and importing. P-DL defines local interpretations and model projection to provide unambiguous semantics for modular ontologies while supporting both inter-module subsumption and role relations. Scope limitation modifiers and concealable reasoning are discussed to enable selective knowledge hiding across module boundaries without compromising soundness.
The document discusses problem solving agents and search algorithms. It describes problem solving as having four steps: goal formulation, problem formulation, search, and execution. It then discusses different types of problems agents may face, such as single state problems and problems with partial information. The document introduces tree search algorithms and strategies for searching a state space, such as breadth-first search. It analyzes the performance of breadth-first search and notes its exponential time and memory complexity for large problems.
This document discusses constraint satisfaction problems (CSPs) and techniques for solving them. It begins by defining CSPs as problems with variables, domains of possible values, and constraints limiting assignments. Backtracking search and heuristics like minimum remaining values are described as standard approaches. Constraint propagation techniques like forward checking and arc consistency are explained, which aim to detect inconsistencies earlier. The 4-queens problem is provided as an example CSP.
MediaEval 2015 - GTM-UVigo Systems for the Query-by-Example Search on Speech ...multimediaeval
The document describes the GTM-UVigo systems for the query-by-example search on speech task at MediaEval 2015. Two neural networks, LSTM and DNN, were used to extract phoneme posteriorgrams from speech. DNN was very slow and memory intensive, while LSTM performed well. Untangling the DNN recipe showed that components like lattice determinization and fMLLR, which help ASR, hurt performance for this task. Phoneme units were automatically selected based on their relevance to the query-document alignment path. Performance improved using a reduced set of the most suitable phoneme units.
Record linking refers to finding records that refer to the same entity across different data sources without a common identifier. This document discusses using logistic regression to classify record pairs as true or false matches. Features like string distances and attributes from related tables are used to train a logistic regression model. The trained model can then predict match probabilities for new record pairs. Storing these probabilities as "probabilistic foreign keys" allows linking records while preserving the original data and enabling manual review of uncertain matches.
This document discusses latent aspect models and topic models. It provides an overview of latent semantic indexing, latent Dirichlet allocation, and Gibbs sampling for topic models. Latent aspect models aim to capture latent semantic structure in text by reducing the dimensionality of document representations. Topic models such as latent Dirichlet allocation are probabilistic generative models that represent documents as mixtures of topics, where each topic is a distribution over words. Gibbs sampling is an algorithm for approximate Bayesian inference in topic models.
The document discusses ggplot2, a grammar of graphics plotting package for R. It introduces key concepts of ggplot2 including the layered grammar of graphics model and its components. These components - data, aesthetic mappings, statistical transformations, geometric objects, scales, coordinates, and faceting - provide flexibility to build complex plots from data. The document provides examples using ggplot2 to visualize birth and death rate data and explore the diamonds dataset.
Introduction to Data structure & Algorithms - Sethuonline.com | Sathyabama Un...sethuraman R
The document discusses data structures and algorithms. It provides an overview of binary trees, binary search trees, and their traversals. It then discusses graphs and poses questions related to binary trees, binary search tree traversals, and graphs.
ESR10 Joachim Daiber - EXPERT Summer School - Malaga 2015RIILP
The document discusses using syntactic preordering models to delimit the morphosyntactic search space for machine translation of morphologically rich languages. It explores preordering dependency trees of the source language to reduce word order variations and predicting morphological attributes on the source side to inform target language word selection. Experimental results show that non-local features and jointly learning which attributes to predict can improve translation performance over baselines. The work aims to combine preordering and morphology prediction to better exploit interactions between syntactic structure and inflectional properties.
This document discusses machine learning techniques for modeling document collections. It introduces topic models, which represent documents as mixtures of topics and topics as mixtures of words. Topic models provide dimensionality reduction and allow semantic-based browsing of document collections. Variational inference methods are described for approximating the posterior distribution in topic models like LDA and correlated topic models.
The document summarizes Kenneth Emeka Odoh's presentation on recommender systems and his solution to the WSDM Challenge competition. It includes discussions of the top solutions which used techniques like light gradient boosted machines, neural networks, and ensemble modeling. It also describes Kenneth's solution using bidirectional LSTMs with techniques like batch normalization and dropout to avoid overfitting on the time series song listening data. Overall, the presentation covered many state-of-the-art recommender system techniques for sequential and time series prediction tasks.
Software tookits for machine learning and graphical modelsbutest
This document summarizes machine learning software for graphical models. It discusses discriminative models for independent data, conditional random fields for dependent data, generative models for unsupervised learning, and Bayesian models. It provides an overview of software for inference, learning, and Bayesian inference in graphical models.
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
The document compares the genetic algorithms optimization and particle swarm optimization methods for designing close range photogrammetry networks. It presents the genetic algorithm and particle swarm optimization as two popular meta-heuristic algorithms inspired by natural evolution and collective animal behavior, respectively. The document develops mathematical models representing the genetic algorithm and particle swarm optimization for close range photogrammetry network design and evaluates them in a test field to reinforce the theoretical aspects.
1) The document describes writing an MPI program to calculate a quantity called coverage from data files in a distributed manner across a cluster.
2) MPI (Message Passing Interface) is a standard for writing programs that can run in parallel on multiple processors. The program should distribute the computation efficiently across the cluster nodes and yield the same results as a serial code.
3) The MPI program structure involves initialization, processes running concurrently on nodes, communication between processes, and finalization. Communicators define which processes can communicate.
This document provides an introduction and overview of 5 papers related to topic modeling techniques. It begins with introducing the speaker and their research interests in text analysis using topic modeling. It then lists the 5 papers that will be discussed: LSA, pLSI, LDA, Gaussian LDA, and criticisms of topic modeling. The document focuses on summarizing each paper's motivation, key points, model, parameter estimation methods, and deficiencies. It provides high-level summaries of key aspects of influential topic modeling papers to introduce the topic.
Analogy is one of the most studied representatives of a family of non-classical forms of reasoning working across different domains, usually taken to play a crucial role in creative thought and problem-solving. In the first part of the talk, I will shortly introduce general principles of computational analogy models (relying on a generalization-based approach to analogy-making). We will then have a closer look at Heuristic-Driven Theory Projection (HDTP) as an example for a theoretical framework and implemented system: HDTP computes analogical relations and inferences for domains which are represented using many-sorted first-order logic languages, applying a restricted form of higher-order anti-unification for finding shared structural elements common to both domains. The presentation of the framework will be followed by a few reflections on the "cognitive plausibility" of the approach motivated by theoretical complexity and tractability considerations.
In the second part of the talk I will discuss an application of HDTP to modeling essential parts of concept blending processes as current "hot topic" in Cognitive Science. Here, I will sketch an analogy-inspired formal account of concept blending —developed in the European FP7-funded Concept Invention Theory (COINVENT) project— combining HDTP with mechanisms from Case-Based Reasoning.
Topic modeling using big data analytics can analyze large datasets. It involves installing Hadoop on multiple nodes for distributed processing, preprocessing data into a desired format, and using modeling tools to parallelize computation and select algorithms. Topic modeling identifies patterns in corpora to develop new ways to search, browse, and summarize large text archives. Tools like Mallet use algorithms like LDA and PLSI to achieve topic modeling on Hadoop, applying it to analyze news articles, search engine rankings, genetic and image data, and more.
Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of...FedorNikolaev
In this work, we propose a novel retrieval model that incorporates term dependencies into structured document retrieval and apply it to the task of ERWD. In the proposed model, the document field weights and the relative importance of unigrams and bigrams are optimized with respect to the target retrieval metric using a learning-to-rank method.
The increasing amount of valuable semi-structured data has become available online. In this talk, we overview the state of the art in entity ranking over structured data ("linked data").
The document proposes adapting OWL as a more modular ontology language by addressing weaknesses in its current modularity. Specifically, OWL lacks:
1) Semantic modularity as it only supports global semantics between imported ontologies.
2) Syntactic modularity as imports can lead to tangled definitions between modules.
The paper suggests approaches to enhance OWL's modularity while maintaining backwards compatibility, such as giving imports a localized semantics or defining explicit syntactic rules to avoid nested definitions across modules.
Divide and Conquer Semantic Web with ModularJie Bao
This document provides a brief review of modular ontology language formalisms. It discusses the need for modular ontologies to address issues with large, monolithic ontologies. Several approaches to modular ontologies are summarized, including Distributed Description Logics (DDL), E-Connections, and Package-based Description Logics (P-DL). Key challenges with modular ontologies are also outlined, such as reasoning across modules and ensuring interoperability while preserving local semantics.
Representing and Reasoning with Modular OntologiesJie Bao
The document discusses representing and reasoning with modular ontologies. It introduces the need for modularity in large ontologies to enable reuse and selective knowledge hiding. It presents package-based description logics (P-DL) as a formalism for representing and reasoning with modular ontologies through package extension and importing. P-DL defines local interpretations and model projection to provide unambiguous semantics for modular ontologies while supporting both inter-module subsumption and role relations. Scope limitation modifiers and concealable reasoning are discussed to enable selective knowledge hiding across module boundaries without compromising soundness.
The document discusses problem solving agents and search algorithms. It describes problem solving as having four steps: goal formulation, problem formulation, search, and execution. It then discusses different types of problems agents may face, such as single state problems and problems with partial information. The document introduces tree search algorithms and strategies for searching a state space, such as breadth-first search. It analyzes the performance of breadth-first search and notes its exponential time and memory complexity for large problems.
This document discusses constraint satisfaction problems (CSPs) and techniques for solving them. It begins by defining CSPs as problems with variables, domains of possible values, and constraints limiting assignments. Backtracking search and heuristics like minimum remaining values are described as standard approaches. Constraint propagation techniques like forward checking and arc consistency are explained, which aim to detect inconsistencies earlier. The 4-queens problem is provided as an example CSP.
MediaEval 2015 - GTM-UVigo Systems for the Query-by-Example Search on Speech ...multimediaeval
The document describes the GTM-UVigo systems for the query-by-example search on speech task at MediaEval 2015. Two neural networks, LSTM and DNN, were used to extract phoneme posteriorgrams from speech. DNN was very slow and memory intensive, while LSTM performed well. Untangling the DNN recipe showed that components like lattice determinization and fMLLR, which help ASR, hurt performance for this task. Phoneme units were automatically selected based on their relevance to the query-document alignment path. Performance improved using a reduced set of the most suitable phoneme units.
Record linking refers to finding records that refer to the same entity across different data sources without a common identifier. This document discusses using logistic regression to classify record pairs as true or false matches. Features like string distances and attributes from related tables are used to train a logistic regression model. The trained model can then predict match probabilities for new record pairs. Storing these probabilities as "probabilistic foreign keys" allows linking records while preserving the original data and enabling manual review of uncertain matches.
This document discusses latent aspect models and topic models. It provides an overview of latent semantic indexing, latent Dirichlet allocation, and Gibbs sampling for topic models. Latent aspect models aim to capture latent semantic structure in text by reducing the dimensionality of document representations. Topic models such as latent Dirichlet allocation are probabilistic generative models that represent documents as mixtures of topics, where each topic is a distribution over words. Gibbs sampling is an algorithm for approximate Bayesian inference in topic models.
The document discusses ggplot2, a grammar of graphics plotting package for R. It introduces key concepts of ggplot2 including the layered grammar of graphics model and its components. These components - data, aesthetic mappings, statistical transformations, geometric objects, scales, coordinates, and faceting - provide flexibility to build complex plots from data. The document provides examples using ggplot2 to visualize birth and death rate data and explore the diamonds dataset.
Introduction to Data structure & Algorithms - Sethuonline.com | Sathyabama Un...sethuraman R
The document discusses data structures and algorithms. It provides an overview of binary trees, binary search trees, and their traversals. It then discusses graphs and poses questions related to binary trees, binary search tree traversals, and graphs.
ESR10 Joachim Daiber - EXPERT Summer School - Malaga 2015RIILP
The document discusses using syntactic preordering models to delimit the morphosyntactic search space for machine translation of morphologically rich languages. It explores preordering dependency trees of the source language to reduce word order variations and predicting morphological attributes on the source side to inform target language word selection. Experimental results show that non-local features and jointly learning which attributes to predict can improve translation performance over baselines. The work aims to combine preordering and morphology prediction to better exploit interactions between syntactic structure and inflectional properties.
This document discusses machine learning techniques for modeling document collections. It introduces topic models, which represent documents as mixtures of topics and topics as mixtures of words. Topic models provide dimensionality reduction and allow semantic-based browsing of document collections. Variational inference methods are described for approximating the posterior distribution in topic models like LDA and correlated topic models.
The document summarizes Kenneth Emeka Odoh's presentation on recommender systems and his solution to the WSDM Challenge competition. It includes discussions of the top solutions which used techniques like light gradient boosted machines, neural networks, and ensemble modeling. It also describes Kenneth's solution using bidirectional LSTMs with techniques like batch normalization and dropout to avoid overfitting on the time series song listening data. Overall, the presentation covered many state-of-the-art recommender system techniques for sequential and time series prediction tasks.
Software tookits for machine learning and graphical modelsbutest
This document summarizes machine learning software for graphical models. It discusses discriminative models for independent data, conditional random fields for dependent data, generative models for unsupervised learning, and Bayesian models. It provides an overview of software for inference, learning, and Bayesian inference in graphical models.
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
The document compares the genetic algorithms optimization and particle swarm optimization methods for designing close range photogrammetry networks. It presents the genetic algorithm and particle swarm optimization as two popular meta-heuristic algorithms inspired by natural evolution and collective animal behavior, respectively. The document develops mathematical models representing the genetic algorithm and particle swarm optimization for close range photogrammetry network design and evaluates them in a test field to reinforce the theoretical aspects.
1) The document describes writing an MPI program to calculate a quantity called coverage from data files in a distributed manner across a cluster.
2) MPI (Message Passing Interface) is a standard for writing programs that can run in parallel on multiple processors. The program should distribute the computation efficiently across the cluster nodes and yield the same results as a serial code.
3) The MPI program structure involves initialization, processes running concurrently on nodes, communication between processes, and finalization. Communicators define which processes can communicate.
This document provides an introduction and overview of 5 papers related to topic modeling techniques. It begins with introducing the speaker and their research interests in text analysis using topic modeling. It then lists the 5 papers that will be discussed: LSA, pLSI, LDA, Gaussian LDA, and criticisms of topic modeling. The document focuses on summarizing each paper's motivation, key points, model, parameter estimation methods, and deficiencies. It provides high-level summaries of key aspects of influential topic modeling papers to introduce the topic.
Analogy is one of the most studied representatives of a family of non-classical forms of reasoning working across different domains, usually taken to play a crucial role in creative thought and problem-solving. In the first part of the talk, I will shortly introduce general principles of computational analogy models (relying on a generalization-based approach to analogy-making). We will then have a closer look at Heuristic-Driven Theory Projection (HDTP) as an example for a theoretical framework and implemented system: HDTP computes analogical relations and inferences for domains which are represented using many-sorted first-order logic languages, applying a restricted form of higher-order anti-unification for finding shared structural elements common to both domains. The presentation of the framework will be followed by a few reflections on the "cognitive plausibility" of the approach motivated by theoretical complexity and tractability considerations.
In the second part of the talk I will discuss an application of HDTP to modeling essential parts of concept blending processes as current "hot topic" in Cognitive Science. Here, I will sketch an analogy-inspired formal account of concept blending —developed in the European FP7-funded Concept Invention Theory (COINVENT) project— combining HDTP with mechanisms from Case-Based Reasoning.
Topic modeling using big data analytics can analyze large datasets. It involves installing Hadoop on multiple nodes for distributed processing, preprocessing data into a desired format, and using modeling tools to parallelize computation and select algorithms. Topic modeling identifies patterns in corpora to develop new ways to search, browse, and summarize large text archives. Tools like Mallet use algorithms like LDA and PLSI to achieve topic modeling on Hadoop, applying it to analyze news articles, search engine rankings, genetic and image data, and more.
Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of...FedorNikolaev
In this work, we propose a novel retrieval model that incorporates term dependencies into structured document retrieval and apply it to the task of ERWD. In the proposed model, the document field weights and the relative importance of unigrams and bigrams are optimized with respect to the target retrieval metric using a learning-to-rank method.
The increasing amount of valuable semi-structured data has become available online. In this talk, we overview the state of the art in entity ranking over structured data ("linked data").
The document discusses learning graphical models from data. It describes two main tasks: inference, which is computing answers to queries about a probability distribution described by a Bayesian network, and learning, which is estimating a model from data. It provides examples of learning for completely observed models, including maximum likelihood estimation for the parameters of a conditional Gaussian model. It also discusses supervised versus unsupervised learning of hidden Markov models, and techniques for dealing with small training sets like adding pseudocounts to estimates.
Exploiting Entity Linking in Queries For Entity RetrievalFaegheh Hasibi
Slides for the ICTIR 2016 paper: "Exploiting Entity Linking in Queries For Entity Retrieval"
The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity
linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, including the current state-of-the-art. We further show that our extension is robust against parameter settings.
Xi Zhang presented their Ph.D. dissertation which analyzed functional regression models and their application to high-frequency financial data. The presentation included:
1. An introduction to functional data analysis and the use of intraday cumulative return curves from stock price data.
2. A simulation study comparing predictive methods in functional autoregressive models, finding the estimated kernel method performed well.
3. An application of functional extensions of the Capital Asset Pricing Model to predict intraday return curves, finding simpler models with intercepts had better predictive performance than more complex models.
The document summarizes several papers presented at the SIGIR 2011 workshop on query representation and understanding.
1. One paper analyzed temporal queries using web snippets and query logs to identify queries with implicit temporal intent, finding that dates were more frequent in snippets than logs.
2. A second paper used complex network analysis to show that search queries have a kernel-periphery structure similar to natural language, with popular query segments in the kernel and rarer segments in the periphery.
3. A third paper investigated query refinement via topic analysis and learning, using latent Dirichlet allocation on query logs to identify topics to personalize candidate query suggestions for individual users.
This document discusses conditional random fields (CRFs), a discriminative structured prediction framework. CRFs model the conditional probability of labels given observations, allowing dependencies between labels and arbitrary features of the input. This is in contrast to hidden Markov models, which are generative and make strong independence assumptions. CRFs can capture long-range dependencies and are discriminatively trained to directly optimize the prediction task. Empirical results show CRFs outperform HMMs and other models on tasks involving higher-order dependencies in synthetic and real-world data like part-of-speech tagging.
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
1. AlphaZero uses self-play reinforcement learning to train a neural network to evaluate board positions and select moves. It trains offline by playing games against itself, using the results to iteratively improve its network.
2. During online play, AlphaZero uses Monte Carlo tree search with the neural network to select moves. It evaluates many random simulations of possible future games to a certain depth, using the network to approximate values beyond that depth.
3. The success of AlphaZero is due to skillfully combining known reinforcement learning techniques like self-play training, neural network function approximation, and Monte Carlo tree search with powerful computational resources.
The document summarizes three papers presented at the SIGIR 2011 workshop on query representation and understanding.
1. The first paper examines using web snippets and query logs to identify implicit temporal intents in queries by analyzing dates mentioned in snippets and previous queries. It finds snippets contain more temporal information than query logs.
2. The second paper analyzes web search query networks and finds a kernel-periphery structure, where high-degree "kernel" words differ from low-degree "peripheral" words. This structure is less pronounced than in natural language networks.
3. The third paper proposes a topic modeling approach to query refinement that generates candidate refinements, scores them based on topic relationships, and incorporates personalization
Application of Bayesian and Sparse Network Models for Assessing Linkage Diseq...Gota Morota
The document summarizes Gota Morota's master's thesis defense on applying Bayesian and sparse network models to assess linkage disequilibrium in animals and plants. The thesis aims to evaluate linkage disequilibrium (LD) using networks that capture loci associations. It first provides background on standard LD metrics and graphical models. It then describes using a Bayesian network and L1-regularized Markov network to analyze LD in dairy cattle, identifying networks of strongly associated SNPs related to milk protein yield. The thesis concludes the results support LD having a multivariate nature better described by networks than pairwise metrics alone.
Query Translation for Ontology-extended Data SourcesJie Bao
This document summarizes an approach for querying ontology-extended data sources. It describes how data sources can be semantically extended with ontologies and mappings to allow for flexible querying. It presents an approach for translating queries formulated over one ontology into equivalent queries over another ontology, while ensuring the translations are sound and complete. It discusses tools developed for ontology editing, mapping, data access and query translation over ontology-extended data sources.
Information access over linked data requires to determine
subgraph(s), in linked data's underlying graph, that correspond to the required information need. Usually, an information access framework is able to retrieve richer information by checking of a large number of possible subgraphs. However, on the ecking of a large number of possible subgraphs increases information access complexity. This makes information access frameworks less eective. A large number of contemporary linked data information access frameworks reduce the complexity by introducing dierent heuristics but they suer on retrieving richer information. Or, some frameworks do not care about the complexity. However, a practically usable framework should retrieve richer information with lower complexity. In linked data information access, we hypothesize that pre-processed data statistics of linked data can be used to eciently check a large number of possible subgraphs. This will help to retrieve comparatively richer information with lower data access complexity. Preliminary evaluation of our proposed hypothesis shows promising performance.
Sparse Kernel Learning for Image AnnotationSean Moran
The document describes an approach called Sparse Kernel Continuous Relevance Model (SKL-CRM) for image annotation. SKL-CRM learns data-adaptive visual kernels to better combine different image features like GIST, SIFT, color, and texture. It introduces a binary kernel-feature alignment matrix to learn which kernel functions are best suited to which features by directly optimizing annotation performance on a validation set. Evaluation on standard datasets shows SKL-CRM improves over baselines with fixed 'default' kernels, achieving a relative gain of 10-15% in F1 score.
The document discusses various techniques for information retrieval and language modeling approaches to IR, including:
- Clustering documents into similar groups to aid in retrieval
- Using term frequency-inverse document frequency (TF-IDF) to measure word importance in documents
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- Smoothing language models to address data sparsity issues
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Tutorial: Context In Recommender SystemsYONG ZHENG
This document provides an overview of a tutorial on context-aware recommender systems. The tutorial will cover traditional recommendation techniques, context-aware recommendation which incorporates additional contextual information such as time and location, and context suggestion. It includes an agenda with topics, background information on recommender systems and evaluation metrics, and descriptions of techniques for context-aware recommendation including context filtering and modeling.
Analysis and Modeling of Complex Data in Behavioral and Social Sciences
Joint meeting of Japanese and Italian Classification Societies
Anacapri (Capri Island, Italy), 3-4 September 2012
This is Part II of the tutorial "Entity Linking and Retrieval for Semantic Search" given at WSDM 2014 (together with E. Meij and D. Odijk). For the complete tutorial material (including slides for the other parts) visit http://ejmeij.github.io/entity-linking-and-retrieval-tutorial/
Wikipedia is the largest user-generated knowledge base. We propose a structured query mechanism, entity-relationship query, for searching entities in Wikipedia corpus by their properties and inter-relationships. An entity-relationship query consists of arbitrary number of predicates on desired entities. The semantics of each predicate is specified with keywords. Entity-relationship query searches entities directly over text rather than pre-extracted structured data stores. This characteristic brings two benefits: (1) Query semantics can be intuitively expressed by keywords; (2) It avoids information loss that happens during extraction. We present a ranking framework for general entity-relationship queries and a position-based Bounded Cumulative Model for accurate ranking of query answers. Experiments on INEX benchmark queries and our own crafted queries show the effectiveness and accuracy of our ranking method.
Similar to Parameterized Fielded Term Dependence Models for Ad-hoc Entity Retrieval from Knowledge Graph (20)
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
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Parameterized Fielded Term Dependence Models for Ad-hoc Entity Retrieval from Knowledge Graph
1. 1/31
Parameterized Fielded Term Dependence Models
for Ad-hoc Entity Retrieval from Knowledge
Graph
Fedor Nikolaev 1,2 Alexander Kotov 1 Nikita Zhiltsov 2
1Textual Data Analytics Lab, Department of Computer Science, Wayne State
University
2Kazan Federal University
3. 3/31
Entities
• Material objects or concepts in the
real world or fiction (e.g. people,
movies, conferences etc.)
• Are connected with other entities
by relations (e.g. hasGenre,
actedIn, isPCmemberOf etc.)
• Subject-Predicate-Object (SPO)
triple: subject=entity;
object=entity (or primitive data
value); predicate=relationship
between subject and object
• Many SPO triples → knowledge
graph
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Entity Retrieval from Knowledge Graph(s)
• Graph KBs are perfectly suited for addressing the information
needs that aim at finding specific objects (entities) rather
than documents
• Given the user’s information need expressed as a keyword
query, retrieve a relevant set of objects from the knowledge
graph(s)
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Typical ERWD tasks
• Entity Search
Queries refer to a particular entity.
• “Ben Franklin”
• “England football player highest paid”
• “Einstein Relativity theory”
• List Search
Complex queries with several relevant entities.
• “US presidents since 1960”
• “animals lay eggs mammals”
• Question Answering
Queries are questions in natural language.
• “Who is the architect of leaning tower of Pisa?”
• “For which label did Elvis record his first album?”
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Entity document
An entity is represented as a structured (multi-fielded) document:
names
Conventional names of the entities, such as the name
of a person or the name of an organization
attributes
All entity properties, other than names
categories
Classes or groups, to which the entity has been
assigned
similar entity names
Names of the entities that are very similar or
identical to a given entity
related entity names
Names of the entities that are part of the same RDF
triple
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Entity document example
Multi-fielded entity document for the entity Barack Obama.
Field Content
names barack obama barack hussein obama ii
attributes 44th current president united states
birth place honolulu hawaii
categories democratic party united states senator
nobel peace prize laureate christian
similar entity names barack obama jr barak hussein obama
barack h obama ii
related entity names spouse michelle obama illinois state
predecessor george walker bush
9. 9/31
PRMS
P(Q|d) =
qi ∈Q j
PM(Ej|qi )PQL(qi |ej),
where
PM(Ej|w) =
PM(w|Ej)PM(Ej)
Ek ∈E PM(w|Ek)PM(Ek)
10. 10/31
SDM, FDM
Ranks w.r.t. PΛ(D|Q) = i∈{T,U,O} λi fi (Q, D)
Potential function for unigrams is QL:
fT (qi , D) = log P(qi |θD) = log
tfqi ,D + µ
cfqi
|C|
|D| + µ
SDM only considers two-word sequences in queries, FDM considers
all two-word combinations.
11. 11/31
Fielded SDM
FSDM incorporates document structure and term dependencies
into the one ranking model.
Potential function for unigrams in case of FSDM:
˜fT (qi , D) = log
j
wT
j P(qi |θj
D) = log
j
wT
j
tfqi ,Dj + µj
cf j
qi
|Cj |
|Dj| + µj
12. 12/31
FSDM limitation
In FSDM field weights are the same for all query concepts of the
same type.
Example
capitals in Europe which were host cities of summer Olympic games
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Parametric extension of FSDM
wT
qi ,j =
k
αU
j,kφk(qi , j)
• φk(qi , j) is the the k-th feature value for unigram qi in field j.
15. 13/31
Parametric extension of FSDM
wT
qi ,j =
k
αU
j,kφk(qi , j)
• φk(qi , j) is the the k-th feature value for unigram qi in field j.
• αU
j,k are feature weights that we learn.
16. 13/31
Parametric extension of FSDM
wT
qi ,j =
k
αU
j,kφk(qi , j)
• φk(qi , j) is the the k-th feature value for unigram qi in field j.
• αU
j,k are feature weights that we learn.
j
wT
qi ,j = 1, wT
qi ,j ≥ 0, αU
j,k ≥ 0, 0 ≤ φk(qi , j) ≤ 1
17. 13/31
Parametric extension of FSDM
wT
qi ,j =
k
αU
j,kφk(qi , j)
• φk(qi , j) is the the k-th feature value for unigram qi in field j.
• αU
j,k are feature weights that we learn.
j
wT
qi ,j = 1, wT
qi ,j ≥ 0, αU
j,k ≥ 0, 0 ≤ φk(qi , j) ≤ 1
PFFDM is the same, but uses full dependence model.
18. 14/31
Features
Source Feature Description CT
Collection
statistics
FP(κ, j) Posterior probability P(Ej|w). UG BG
TS(κ, j) Top SDM score on j-th field
when κ is used as a query.
BG
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Features
Source Feature Description CT
Collection
statistics
FP(κ, j) Posterior probability P(Ej|w). UG BG
TS(κ, j) Top SDM score on j-th field
when κ is used as a query.
BG
Stanford
POS
Tagger
NNP(κ) Is concept κ a proper noun? UG
NNS(κ) Is κ a plural non-proper noun? UG BG
JJS(κ) Is κ a superlative adjective? UG
Stanford
Parser
NPP(κ) Is κ part of a noun phrase? BG
NNO(κ) Is κ the only singular non-
proper noun in a noun phrase?
UG
INT Intercept feature (= 1). UG BG
20. 15/31
Parameters of PFSDM
Both PFSDM and PFFDM have F ∗ U + F ∗ B + 3 free
parameters: ˆαU, ˆαB, ˆλ .
We perform direct optimization w.r.t. target metric (e.g. MAP)
using coordinate ascent.
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Collections
1 DBPedia 3.7
• Structured version of on-line encyclopedia Wikipedia
• Provides the descriptions of over 3.5 million entities belonging
to 320 classes
2 BTC-2009
• Contains entities from multiple knowledge bases.
• Consists of 1.14 billion RDF triples.
22. 17/31
Query sets
Balog and Neumayer. A Test Collection for Entity Search in
DBpedia, SIGIR’13.
Query set Amount Query types [Pound et al., 2010]
SemSearch ES 130 Entity
ListSearch 115 Type
INEX-LD 100 Entity, Type, Attribute, Relation
QALD-2 140 Entity, Type, Attribute, Relation
Only SemSearch ES judgments are available for BTC-2009, so for
BTC-2009 we used only this query set.
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Conclusion
• Entity-centric keyword queries have an implicit structure, with
each element in that structure designating a particular aspect
in multi-fielded representation of relevant entities.
• We proposed two novel models for ad-hoc entity retrieval from
knowledge graph, which account for term dependencies and
perform feature-based projection of query concepts onto the
fields of entity documents.
• By demonstrating the possibility of inferring implicit structure
of keyword queries using linguistic attributes and simple field
statistics of query concepts, the proposed models constitute
an important step in the evolution of models for structured
document retrieval.
39. 30/31
Feature work
We hypothesize that the proposed models can be effective in other
structured information retrieval scenarios, such as product and
social graph search, and leave verification of this hypothesis to
future work.