These are the slides from a talk I presented at the Graph Processing room at FOSDEM 2013, in which I discussed my PhD topic: a query language allowing for the flexible querying of complex paths within graph structured data
This document provides an overview of logic programming and the logic programming language Prolog. It discusses declarative programming and how Prolog uses declarative rules, facts, and predicates. It explains how Prolog performs logical operations like unification and resolution to evaluate queries against its knowledge base. It provides examples of using Prolog to represent graphs, lists, arithmetic, and more.
Babar: Knowledge Recognition, Extraction and RepresentationPierre de Lacaze
Babar is a research project in the field of Artificial Intelligence. It aims to bridge together Neural AI and Symbolic AI. As such it is implemented in three different programming languages: Clojure, Python and CLOS.
The Clojure component (Clobar) implements the graphical user interface to Babar. Examples of the Clojure Hiccup library and interfacing Clojure to Javascript will be presented. The Python module (Pybar) implements the web crawling and scraping and the Neural Networks aspect of Babar. The Word Embedding and and LSTM (Long Short-Term Memory) components of Pybar will be described in detail. Finally the Common Lisp module (Lispbar) implements the Symbolic AI aspect of Babar. This latter includes an English Language Parser and Semantic Networks implemented as an in-memory Hypergraph.
We will present each of these components and target individual aspects with code examples. Specifically we will first present the web developments and Neural Networks components. Then the English Language parser will be examined in detail. We will also present the knowledge extraction aspect and bridge this with the Neural Network component.
Ultimately we will argue what can be termed "Neural AI" and "Symbolic AI" are at not at odds with each other but rather complement each other. In summary Artificial Intelligence is not a question of "brain" or "mind", but rather a question of "brain" and "mind".
Object-oriented programming (OOP) is a programming paradigm that represents concepts as "objects" that have properties and behaviors. The key OOP concepts are encapsulation, inheritance, abstraction, and polymorphism. Encapsulation groups data and functions together in classes. Inheritance allows child classes to inherit attributes and behaviors from parent classes. Abstraction hides unnecessary details and focuses on important aspects. Polymorphism allows the same methods to work with different object types. OOP aims to make code reusable, modular, and easier to maintain.
Dev Concepts: Object-Oriented ProgrammingSvetlin Nakov
What Is Object-Oriented Programming?
Watch the video lesson from Svetlin Nakov and learn more at:
https://softuni.org/dev-concepts/what-is-object-oriented-programming
Inheritance allows classes to extend and inherit properties from base classes. This creates class hierarchies where subclasses inherit and can override methods from superclasses. Inheritance promotes code reuse through extension when subclasses share the same role as the base class. Composition and delegation are alternative approaches to code reuse that may be preferable in some cases over inheritance.
This document discusses polymorphism, abstract classes, and abstract methods. It defines polymorphism as an object's ability to take on many forms and describes how it allows reference variables to refer to objects of child classes. It also distinguishes between method overloading and overriding, and explains the rules for each. Abstract classes are introduced as classes that cannot be instantiated directly but can be inherited from, and it is noted they may or may not contain abstract methods.
This document provides an overview of dictionaries, hash tables, and sets. It discusses the dictionary abstract data type and how it can be implemented using hash tables. It covers hashing, collision resolution strategies, and the .NET Dictionary<TKey, TValue> class. It also discusses sets and the HashSet<T> and SortedSet<T> classes, comparing their time complexities.
Learning with classification and clustering, neural networksShaun D'Souza
This document discusses various machine learning techniques including supervised learning methods like classification and regression as well as unsupervised learning methods like text clustering. It provides examples of applying classification to iris flower data and sentiment analysis using naive Bayes. It also discusses natural language processing tasks like part-of-speech tagging, chunking, parsing and named entity recognition and how these can be applied using tools like OpenNLP. Finally, it briefly covers document clustering and how it is used to group unlabeled documents in an unsupervised manner.
This document provides an overview of logic programming and the logic programming language Prolog. It discusses declarative programming and how Prolog uses declarative rules, facts, and predicates. It explains how Prolog performs logical operations like unification and resolution to evaluate queries against its knowledge base. It provides examples of using Prolog to represent graphs, lists, arithmetic, and more.
Babar: Knowledge Recognition, Extraction and RepresentationPierre de Lacaze
Babar is a research project in the field of Artificial Intelligence. It aims to bridge together Neural AI and Symbolic AI. As such it is implemented in three different programming languages: Clojure, Python and CLOS.
The Clojure component (Clobar) implements the graphical user interface to Babar. Examples of the Clojure Hiccup library and interfacing Clojure to Javascript will be presented. The Python module (Pybar) implements the web crawling and scraping and the Neural Networks aspect of Babar. The Word Embedding and and LSTM (Long Short-Term Memory) components of Pybar will be described in detail. Finally the Common Lisp module (Lispbar) implements the Symbolic AI aspect of Babar. This latter includes an English Language Parser and Semantic Networks implemented as an in-memory Hypergraph.
We will present each of these components and target individual aspects with code examples. Specifically we will first present the web developments and Neural Networks components. Then the English Language parser will be examined in detail. We will also present the knowledge extraction aspect and bridge this with the Neural Network component.
Ultimately we will argue what can be termed "Neural AI" and "Symbolic AI" are at not at odds with each other but rather complement each other. In summary Artificial Intelligence is not a question of "brain" or "mind", but rather a question of "brain" and "mind".
Object-oriented programming (OOP) is a programming paradigm that represents concepts as "objects" that have properties and behaviors. The key OOP concepts are encapsulation, inheritance, abstraction, and polymorphism. Encapsulation groups data and functions together in classes. Inheritance allows child classes to inherit attributes and behaviors from parent classes. Abstraction hides unnecessary details and focuses on important aspects. Polymorphism allows the same methods to work with different object types. OOP aims to make code reusable, modular, and easier to maintain.
Dev Concepts: Object-Oriented ProgrammingSvetlin Nakov
What Is Object-Oriented Programming?
Watch the video lesson from Svetlin Nakov and learn more at:
https://softuni.org/dev-concepts/what-is-object-oriented-programming
Inheritance allows classes to extend and inherit properties from base classes. This creates class hierarchies where subclasses inherit and can override methods from superclasses. Inheritance promotes code reuse through extension when subclasses share the same role as the base class. Composition and delegation are alternative approaches to code reuse that may be preferable in some cases over inheritance.
This document discusses polymorphism, abstract classes, and abstract methods. It defines polymorphism as an object's ability to take on many forms and describes how it allows reference variables to refer to objects of child classes. It also distinguishes between method overloading and overriding, and explains the rules for each. Abstract classes are introduced as classes that cannot be instantiated directly but can be inherited from, and it is noted they may or may not contain abstract methods.
This document provides an overview of dictionaries, hash tables, and sets. It discusses the dictionary abstract data type and how it can be implemented using hash tables. It covers hashing, collision resolution strategies, and the .NET Dictionary<TKey, TValue> class. It also discusses sets and the HashSet<T> and SortedSet<T> classes, comparing their time complexities.
Learning with classification and clustering, neural networksShaun D'Souza
This document discusses various machine learning techniques including supervised learning methods like classification and regression as well as unsupervised learning methods like text clustering. It provides examples of applying classification to iris flower data and sentiment analysis using naive Bayes. It also discusses natural language processing tasks like part-of-speech tagging, chunking, parsing and named entity recognition and how these can be applied using tools like OpenNLP. Finally, it briefly covers document clustering and how it is used to group unlabeled documents in an unsupervised manner.
This document provides an overview of using latent semantic analysis (LSA) and the R programming language for language technology enhanced learning applications. It describes using LSA to create a semantic space to compare documents and evaluate student writings. It also demonstrates clustering terms based on their semantic similarity and visualizing networks in R. Evaluation results show LSA machine scores for essay quality had a Spearman's rank correlation of 0.687 with human scores, outperforming a pure vector space model.
This document contains the solutions to 8 questions related to Java OOP concepts such as inheritance, polymorphism, method overriding and overloading.
The questions cover topics like determining the output of sample code, identifying true/false statements about OOP concepts, explaining the differences between method overriding and overloading, designing UML class diagrams to model relationships between classes, and writing Java programs to test class hierarchies and subclasses.
Detailed explanations and code samples are provided for each question to demonstrate concepts like invoking superclass constructors, determining pass/fail conditions for student grades based on average test scores, implementing abstract classes and interfaces, and creating subclasses that extend the functionality of base classes.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
This document provides an overview of object-oriented programming concepts in C++, including objects, classes, data abstraction, encapsulation, inheritance, and polymorphism. It defines each concept, provides examples in C++ code, and explains how they are implemented and relate to each other. The document is presented as part of a mentoring program to teach OOP concepts.
This document provides an overview of representation learning techniques for natural language processing (NLP). It begins with introductions to the speakers and objectives of the workshop, which is to provide a deep dive into state-of-the-art text representation techniques. The workshop is divided into four modules: word vectors, sentence/paragraph/document vectors, and character vectors. The document provides background on why text representation is important for NLP, and discusses older techniques like one-hot encoding, bag-of-words, n-grams, and TF-IDF. It also introduces newer distributed representation techniques like word2vec's skip-gram and CBOW models, GloVe, and the use of neural networks for language modeling.
Object-oriented programming is a methodology that associates data structures with operators that act on the data. It models real-world problems better than procedural programming by emphasizing objects over procedures. The key concepts of object-oriented programming are classes, objects, abstraction, encapsulation, inheritance, and polymorphism. Abstraction represents essential features without details, encapsulation combines data and methods into classes, and inheritance allows classes to acquire attributes and behaviors from parent classes.
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...NTNU
The introduction of expert knowledge when learning Bayesian Networks from data is known to be an excellent approach to boost the performance of automatic learning methods, specially when the data is scarce. Previous approaches for this problem based on Bayesian statistics introduce the expert knowledge modifying the prior probability distributions. In this study, we propose a new methodology based on Monte Carlo simulation which starts with non-informative priors and requires knowledge from the expert a posteriori, when the simulation ends. We also explore a new Importance Sampling method for Monte Carlo simulation and the definition of new non-informative priors for the structure of the network. All these approaches are experimentally validated with five standard Bayesian networks.
Read more:
http://link.springer.com/chapter/10.1007%2F978-3-642-14049-5_70
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
17. Java data structures trees representation and traversalIntro C# Book
In this chapter we will discuss tree data structures, like trees and graphs. The abilities of these data structures are really important for the modern programming. Each of this data structures is used for building a model of real life problems, which are efficiently solved using this model.
In this chapter we will understand how to define custom classes and their elements. We will learn to declare fields, constructors and properties for the classes. We will revise what a method is and we will broaden our knowledge about access modifiers and methods. We will observe the characteristics of the constructors and we will set out how the program objects coexist in the dynamic memory and how their fields are initialized. Finally, we will explain what the static elements of a class are – fields (including constants), properties and methods and how to use them properly. In this chapter we will also introduce generic types (generics), enumerated types (enumerations) and nested classes.
Declarative Multilingual Information Extraction with SystemTdiannepatricia
"Declarative Multilingual Information Extraction with SystemT" presented by Laura Chiticariu, IBM Research - Almaden as part of the Cognitive Systems Institute Speaker Series.
This document provides an overview and outline of a lesson on variables and types in Java. The key points covered include:
- Variables are names for locations in memory that hold values. Primitive data types include numerical, character, and boolean values. Complex objects are instances of classes.
- Variables are declared with a data type, name, and optional initial value. Primitive values can be output and converted between types through casting or automatic promotion.
- Expressions combine operators and operands to compute results. Operators have precedence that determines the order of evaluation. Assignment operators store the result of an expression into a variable.
- The lesson covers primitive data types, variables, expressions, output, conversion, and creating objects
Neural Models for Information RetrievalBhaskar Mitra
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models will also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
We begin this talk with a discussion on text embedding spaces for modelling different types of relationships between items which makes them suitable for different IR tasks. Next, we present how topic-specific representations can be more effective than learning global embeddings. Finally, we conclude with an emphasis on dealing with rare terms and concepts for IR, and how embedding based approaches can be augmented with neural models for lexical matching for better retrieval performance. While our discussions are grounded in IR tasks, the findings and the insights covered during this talk should be generally applicable to other NLP and machine learning tasks.
Deep neural methods have recently demonstrated significant performance improvements in several IR tasks. In this lecture, we will present a brief overview of deep models for ranking and retrieval.
This is a follow-up lecture to "Neural Learning to Rank" (https://www.slideshare.net/BhaskarMitra3/neural-learning-to-rank-231759858)
In this lesson you will learn how to use basic syntax, conditions, if-else statements and loops (for-loop, while-loop and do-while-loop) in Java and how to use the debugger.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-basic-syntax-conditions-and-loops
This document discusses machine learning algorithms for natural language processing (NLP) classification problems. It introduces decision trees as a machine learning algorithm that recursively partitions training data using hierarchical tree structures. Decision trees are learned from labeled training examples using a top-down induction approach where the training data is recursively split based on feature tests until some stopping criteria is reached. Common algorithms for learning decision trees include ID3, C4.5, and CART.
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.
1. Domain Specific Modelling can be viewed as a form of theory building, where a domain is understood by developing theories about it consisting of true statements.
2. Traditionally, programming is seen as implementing a theory, but modelling languages treat the domain weakly and lack support for truly representing theories.
3. The paper proposes enhancing modelling languages to more fully support domain theories, including defining syntax, semantics, and mappings between theories and implementations.
This document provides an overview of using latent semantic analysis (LSA) and the R programming language for language technology enhanced learning applications. It describes using LSA to create a semantic space to compare documents and evaluate student writings. It also demonstrates clustering terms based on their semantic similarity and visualizing networks in R. Evaluation results show LSA machine scores for essay quality had a Spearman's rank correlation of 0.687 with human scores, outperforming a pure vector space model.
This document contains the solutions to 8 questions related to Java OOP concepts such as inheritance, polymorphism, method overriding and overloading.
The questions cover topics like determining the output of sample code, identifying true/false statements about OOP concepts, explaining the differences between method overriding and overloading, designing UML class diagrams to model relationships between classes, and writing Java programs to test class hierarchies and subclasses.
Detailed explanations and code samples are provided for each question to demonstrate concepts like invoking superclass constructors, determining pass/fail conditions for student grades based on average test scores, implementing abstract classes and interfaces, and creating subclasses that extend the functionality of base classes.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
This document provides an overview of object-oriented programming concepts in C++, including objects, classes, data abstraction, encapsulation, inheritance, and polymorphism. It defines each concept, provides examples in C++ code, and explains how they are implemented and relate to each other. The document is presented as part of a mentoring program to teach OOP concepts.
This document provides an overview of representation learning techniques for natural language processing (NLP). It begins with introductions to the speakers and objectives of the workshop, which is to provide a deep dive into state-of-the-art text representation techniques. The workshop is divided into four modules: word vectors, sentence/paragraph/document vectors, and character vectors. The document provides background on why text representation is important for NLP, and discusses older techniques like one-hot encoding, bag-of-words, n-grams, and TF-IDF. It also introduces newer distributed representation techniques like word2vec's skip-gram and CBOW models, GloVe, and the use of neural networks for language modeling.
Object-oriented programming is a methodology that associates data structures with operators that act on the data. It models real-world problems better than procedural programming by emphasizing objects over procedures. The key concepts of object-oriented programming are classes, objects, abstraction, encapsulation, inheritance, and polymorphism. Abstraction represents essential features without details, encapsulation combines data and methods into classes, and inheritance allows classes to acquire attributes and behaviors from parent classes.
An Importance Sampling Approach to Integrate Expert Knowledge When Learning B...NTNU
The introduction of expert knowledge when learning Bayesian Networks from data is known to be an excellent approach to boost the performance of automatic learning methods, specially when the data is scarce. Previous approaches for this problem based on Bayesian statistics introduce the expert knowledge modifying the prior probability distributions. In this study, we propose a new methodology based on Monte Carlo simulation which starts with non-informative priors and requires knowledge from the expert a posteriori, when the simulation ends. We also explore a new Importance Sampling method for Monte Carlo simulation and the definition of new non-informative priors for the structure of the network. All these approaches are experimentally validated with five standard Bayesian networks.
Read more:
http://link.springer.com/chapter/10.1007%2F978-3-642-14049-5_70
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
17. Java data structures trees representation and traversalIntro C# Book
In this chapter we will discuss tree data structures, like trees and graphs. The abilities of these data structures are really important for the modern programming. Each of this data structures is used for building a model of real life problems, which are efficiently solved using this model.
In this chapter we will understand how to define custom classes and their elements. We will learn to declare fields, constructors and properties for the classes. We will revise what a method is and we will broaden our knowledge about access modifiers and methods. We will observe the characteristics of the constructors and we will set out how the program objects coexist in the dynamic memory and how their fields are initialized. Finally, we will explain what the static elements of a class are – fields (including constants), properties and methods and how to use them properly. In this chapter we will also introduce generic types (generics), enumerated types (enumerations) and nested classes.
Declarative Multilingual Information Extraction with SystemTdiannepatricia
"Declarative Multilingual Information Extraction with SystemT" presented by Laura Chiticariu, IBM Research - Almaden as part of the Cognitive Systems Institute Speaker Series.
This document provides an overview and outline of a lesson on variables and types in Java. The key points covered include:
- Variables are names for locations in memory that hold values. Primitive data types include numerical, character, and boolean values. Complex objects are instances of classes.
- Variables are declared with a data type, name, and optional initial value. Primitive values can be output and converted between types through casting or automatic promotion.
- Expressions combine operators and operands to compute results. Operators have precedence that determines the order of evaluation. Assignment operators store the result of an expression into a variable.
- The lesson covers primitive data types, variables, expressions, output, conversion, and creating objects
Neural Models for Information RetrievalBhaskar Mitra
In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models will also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
We begin this talk with a discussion on text embedding spaces for modelling different types of relationships between items which makes them suitable for different IR tasks. Next, we present how topic-specific representations can be more effective than learning global embeddings. Finally, we conclude with an emphasis on dealing with rare terms and concepts for IR, and how embedding based approaches can be augmented with neural models for lexical matching for better retrieval performance. While our discussions are grounded in IR tasks, the findings and the insights covered during this talk should be generally applicable to other NLP and machine learning tasks.
Deep neural methods have recently demonstrated significant performance improvements in several IR tasks. In this lecture, we will present a brief overview of deep models for ranking and retrieval.
This is a follow-up lecture to "Neural Learning to Rank" (https://www.slideshare.net/BhaskarMitra3/neural-learning-to-rank-231759858)
In this lesson you will learn how to use basic syntax, conditions, if-else statements and loops (for-loop, while-loop and do-while-loop) in Java and how to use the debugger.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-basic-syntax-conditions-and-loops
This document discusses machine learning algorithms for natural language processing (NLP) classification problems. It introduces decision trees as a machine learning algorithm that recursively partitions training data using hierarchical tree structures. Decision trees are learned from labeled training examples using a top-down induction approach where the training data is recursively split based on feature tests until some stopping criteria is reached. Common algorithms for learning decision trees include ID3, C4.5, and CART.
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.
1. Domain Specific Modelling can be viewed as a form of theory building, where a domain is understood by developing theories about it consisting of true statements.
2. Traditionally, programming is seen as implementing a theory, but modelling languages treat the domain weakly and lack support for truly representing theories.
3. The paper proposes enhancing modelling languages to more fully support domain theories, including defining syntax, semantics, and mappings between theories and implementations.
The document describes Dedalo, a system that automatically explains clusters of data by traversing linked data to find explanations. It evaluates different heuristics for guiding the traversal, finding that entropy and conditional entropy outperform other measures by reducing redundancy and search time. Experiments on authorship clusters, publication clusters, and library book borrowings demonstrate Dedalo's ability to discover explanatory linked data patterns within a limited domain. Future work includes extending Dedalo to handle more complex datasets by addressing issues such as sameAs linking and use of literals.
This document provides an overview of machine learning and neural network techniques. It defines machine learning as the field that focuses on algorithms that can learn. The document discusses several key components of a machine learning model, including what is being learned (the domain) and from what information the learner is learning. It then summarizes several common machine learning algorithms like k-NN, Naive Bayes classifiers, decision trees, reinforcement learning, and the Rocchio algorithm for relevance feedback in information retrieval. For each technique, it provides a brief definition and examples of applications.
The document discusses several semantic technologies developed at the Knowledge Media Institute including:
1. Knowledge Fusion (KnoFuss) which deals with integrating knowledge from heterogeneous sources by techniques like ontology matching, coreference resolution, and conflict resolution.
2. Ontology Matching (Scarlet) which matches ontologies from different products/domains by leveraging background knowledge from external sources like the Semantic Web.
3. A new ontology matching paradigm is proposed that relies on discovering and combining online ontologies dynamically to derive mappings between terms that lack syntactic overlap or structural context.
Adaptive relevance feedback in information retrievalYI-JHEN LIN
Adaptive relevance feedback aims to optimize the balance between the original query and feedback documents. The paper proposes learning an adaptive feedback coefficient based on query and feedback document characteristics. These include query and feedback document discrimination and divergence between the query and feedback. Logistic regression is used to learn weights mapping query-feedback pairs to coefficients. Experiments show the approach improves retrieval performance compared to fixed coefficients, especially when training and test data are in the same domain.
Surrogate models emulate expensive computer simulations. The objective is to approximate a function, $f$, of $d$ variables to a given tolerance, $\varepsilon$, using as few function values as possible, preferably $O(d)$. We explain how tractability theory provides lower bounds on the number of function values required for any possible method. We also propose method for sampling $f$ and approximating $f$ that achieves this objective and the kind of underlying structure that $f$ must have for success.
This document provides an overview of the Word2Vec deep learning technique for generating word embeddings from large text corpora. It begins with an introduction to deep learning applications in biotechnology. The document then covers the traditional one-hot encoding representation of words and its limitations. It introduces Word2Vec as a method to map words to vectors of continuous values such that similar words have similar vectors. Key aspects covered include the skip-gram architecture, negative sampling, and training Word2Vec models on large datasets. Applications to materials science literature are discussed. Finally, potential project ideas involving applying Word2Vec to biological literature and genomes are proposed.
This document summarizes key concepts in information retrieval systems and algorithms for large data sets. It discusses the differences between information retrieval and data retrieval systems. It also describes several classic models for relevance ranking in IR, including the Boolean model and vector space model. The document outlines topics like text processing, indexing, searching, and evaluation in information retrieval systems.
This document provides an overview of machine learning and various machine learning techniques. It discusses what machine learning is, different types of learning tasks like classification and regression, how performance is measured, and different types of training experiences like direct supervision and reinforcement learning. It then covers specific machine learning algorithms like classification using Rocchio's algorithm, nearest neighbor learning, Bayesian learning approaches, and text categorization using naive Bayes.
The document summarizes algorithms for learning first-order logic rules from examples, including:
1) A sequential covering algorithm that learns one rule at a time to cover examples, removing covered examples and repeating until all examples are covered or rules have low performance.
2) The learn-one-rule sub-algorithm uses a decision tree-like approach to greedily select the attribute that best splits examples according to a performance metric.
3) Variations include allowing low probability classes and using a seed example approach instead of removing covered examples between rules.
First-order logic (FOL) is a formal system used in mathematics, philosophy, linguistics, and computer science to represent knowledge about domains involving objects and relations. FOL extends propositional logic with quantifiers and predicates to describe properties of and relations between objects. Well-formed formulas in FOL involve constants, variables, functions, predicates, quantifiers, and logical connectives. The meaning and truth of FOL statements is determined with respect to a structure called a model that specifies a domain of objects and interpretations of symbols. FOL can be used to represent knowledge about many different domains and perform logical inference.
Intelligent Methods in Models of Text Information Retrieval: Implications for...inscit2006
This document summarizes a conference paper on intelligent information retrieval methods and their implications for society. It discusses topics like digital inclusion, the digital divide, effects on the work environment, intellectual property issues, privacy, security, censorship, and spam/optimization techniques used to artificially increase search engine rankings. It also describes a collaborative research project using various artificial intelligence techniques like fuzzy sets, genetic algorithms, and rough sets to improve information retrieval system usability.
This document provides an introduction to the theory of computation, including definitions of key concepts like automata theory, symbols, alphabets, strings, languages, and sets. It discusses how automata theory deals with formal models of computation and is used in areas like text processing and programming languages. Mathematical terminology is introduced, such as symbols, alphabets, strings, languages, sets, and the power and Cartesian product of alphabets. Examples are given to illustrate concepts like strings, languages, and valid versus invalid computations based on whether a string is contained within a language.
The document discusses using machine learning techniques like Gaussian processes (GPs) to optimize the configuration of software systems. It notes that software performance landscapes are often complex, with non-linear interactions between parameters and non-convex response surfaces. Measurements are also subject to noise. The document introduces an approach called TL4CO that uses multi-task Gaussian processes to model software performance across different versions/deployments, allowing it to leverage data from other versions to improve optimization. This helps address challenges in DevOps where new versions are continuously delivered.
This document proposes an approach to improve geographic information (GI) interoperability through emergent semantics. It describes using structure preserving semantic matching (SPSM) to find correspondences between semantically related nodes in graph-like representations (e.g. schemas, ontologies) while preserving structural properties. An example matching geo-services requests is provided. Evaluation on synthesized datasets showed average precision and recall of 0.78, demonstrating the potential of the approach. Future work will include extensive evaluation and extending the approach to fully developed spatial data infrastructure ontologies.
This document provides an overview of natural language processing (NLP) including the linguistic basis of NLP, common NLP problems and approaches, sources of NLP data, and steps to develop an NLP system. It discusses tokenization, part-of-speech tagging, parsing, machine learning approaches like naive Bayes classification and dependency parsing, measuring word similarity, and distributional semantics. The document also provides advice on going from research to production systems and notes areas not covered like machine translation and deep learning methods.
Machine Learning and Artificial Neural Networks.pptAnshika865276
Machine learning and neural networks are discussed. Machine learning investigates how knowledge is acquired through experience. A machine learning model includes what is learned (the domain), who is learning (the computer program), and the information source. Techniques discussed include k-nearest neighbors algorithm, Winnow algorithm, naive Bayes classifier, decision trees, and reinforcement learning. Reinforcement learning involves an agent interacting with an environment to optimize outcomes through trial and error.
Similar to Fosdem 2013 petra selmer flexible querying of graph data (20)
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Fueling AI with Great Data with Airbyte WebinarZilliz
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Fosdem 2013 petra selmer flexible querying of graph data
1. Flexible querying of graph data
Graph processing room
FOSDEM, 2 Feb 2013
Petra Selmer
petra.selmer.uk@gmail.com
http://www.dcs.bbk.ac.uk/~lselm01/
2. Introduction
I shall be presenting my PhD topic which involves
a declarative query language allowing for the
flexible querying of graph-structured data with
complex paths.
2
3. Agenda
Who (am I)?
Why (the motivation)?
Some background info
What (is the query language and what
can it do)?
Illustrative examples
How (is it done)?
3
4. Who?
Petra Selmer
Part-time PhD student:
Birkbeck College, University of London
Prof. Alexandra Poulovassilis
Dr. Peter T. Wood
Software Architect:
University College London’s Institute of Neurology
(Wellcome Trust Centre for Neuroimaging)
4
5. Why?
Amount of graph-structured data is
growing fast
The structure of this data is
becoming more complex, especially
when multiple, heterogeneous data
sources are integrated together
The structure of the data is also
always subject to change...
5
6. Why?
Users of such systems may not be familiar with the underlying data
structure: available paths etc
The user may not be able to obtain meaningful answers (or indeed,
any answers) from the data IF the querying system is limited to exact
matching of users’ queries
Also, the user may wish to explore the data by starting from a set of
initial answers and proceeding from there
The user may additionally wish to derive some intelligence from the
connections....
The data
The query The user
6
7. Background: Ontologies
Currently part of the Semantic Web stack (Tim Berners-
Lee, RDF, triple stores)
Models a domain of interest: inferences, reasoning...
It can be thought of as a “schema” for graph data
The following inference rules are included (among
others):
Subclass: ‘History’, ‘Languages’ are subclasses of
‘Humanities’
Subproperty, Domain, Range...
7
8. What?
Data model: G = (V, E)
Very general model
V : vertices (or nodes); each labelled with some
constant
E : directed, labelled edges; labels drawn from an
alphabet {Ʃ U ‘type’}
The query language is called Flex-It (it is
declarative)
The basis is that of conjunctive regular path
queries
There are two operators which may be applied to the
original query
8
9. What?
Conjunctive regular path queries:
This is where the graph's paths to be traversed are expressed with a
regular expression
A single regular path query conjunct: (X, R, Y)
X, Y: either constants or variables
R: the regular expression
“Conjunctive”: joining multiple conjuncts; e.g. (X, R1, Y), (Y,
R2, Z), (Z, R3, A)
The Y’s are matched, the Z’s are matched etc
1) (N1, n+, ?Y):
n n p • Y = N2, N3
N1 N2 N3 N4
2) (N1, n*p, ?Y):
• Y = N4
9
10. What?
Approximation allows for the approximate matching
of labels in the path
An edit operation is applied to each edge label in
the path denoted by the regular expression:
Edit operations: insertions, deletions, inversions,
substitutions and transpositions of labels
Each operation has a ‘cost’: usually 1
Example:
Query conjunct: (X, a*.b, Y)
R = a*.b [answers returned at cost 0]
R’ = p.a*.b (insertion of ‘p’) [answers returned at cost 1]
R’’ = p.a*.b- (inversion of ‘b’) [answers returned at cost 2]
10
11. What?
Relaxation is applied by using inference
rules from an ontology (if one exists).
Achieved by applying logical relaxation of the query
conditions using the data’s ontology definition
Relaxation operations: subclass, subproperty, domain
and range
Each operation has a ‘cost’ – usually 1
Example:
We have an ontology:
Humanities (superclass)
Languages and History (subclasses of Humanities)
Assume our query states Languages may be relaxed
Languages is relaxed to Humanities:
Instances of Languages will be returned at cost 0
Instances of History will be returned at cost 1
11
12. What?
Answers are ranked according to how
closely they match the original query;
higher-cost answers have a lower ranking
All answers at a certain distance d are
ranked the same and returned before
answers at a higher distance
We allow for incremental execution: exact
answers returned first; then answers at
distance 1; ...
12
15. Query: “What work positions can I reach, having a degree in English”?
Y = the episode; Z = the job
(?Y, ?Z)
(?X, type, University),
(?X, qualif.type, EnglishStudies),
(?X, prereq+, ?Y),
(?Y, type, Work),
(?Y, job.type, ?Z)
15
16. Query: “What work positions can I reach, having a degree in English”?
Y = the episode; Z = the job
(?Y, ?Z)
(?X, type, University),
(?X, qualif.type, EnglishStudies),
(?X, prereq+, ?Y),
(?Y, type, Work),
(?Y, job.type, ?Z)
No results from User 2 will be returned...even though it is relevant!
16
17. Allowing query approximation can yield some answers:
Replacing the edge label prereq by next, at an edit cost of 1, we get this variant of the
query:
(?Y, ?Z)
(?X, type, University),
(?X, qualif.type, EnglishStudies),
APPROX(?X, prereq+, ?Y),
(?Y, type, Work),
(?Y, job.type, ?Z)
prereq+ can be approximated by next.prereq* at edit distance 1:
Result: Y = ep22, Z = AirTravelAssistant
17
18. Allowing query approximation can yield some answers:
Replacing the edge label prereq by next, at an edit cost of 1, we get this
variant of the query:
(?Y, ?Z)
(?X, type, University),
(?X, qualif.type, EnglishStudies),
APPROX(?X, prereq+, ?Y),
(?Y, type, Work),
(?Y, job.type, ?Z)
next.prereq* can be approximated by next.next.prereq*, now at edit distance 2:
Results:
Y = ep23, Z = Journalist
Y = ep24, Z = AssistantEditor
18
20. Query: “What jobs are open to me if I study English, or something similar, at University”?
(?Y, ?Z)
(?X, type, University), (?X, qualif, ?D),
RELAX (?D, type, EnglishStudies),
APPROX (?X, prereq+, ?Y),
(?Y, type, Work), (?Y, job.type, ?Z)
In addition to the answers (from User 2) obtained by the previous query, we now also have
answers from the timeline of User 3
prereq+ can be approximated by next.prereq* (distance 1) and EnglishStudies can be relaxed
– via Languages - to Humanities (distance 2), encompassing History
Result: Y = ep32, Z = PersonalAssistant (distance of 3 from original query)
20
21. Query: “What jobs are open to me if I study English, or something similar, at
University”?
(?Y, ?Z)
(?X, type, University), (?X, qualif, ?D),
RELAX (?D, type, EnglishStudies),
APPROX (?X, prereq+, ?Y),
(?Y, type, Work), (?Y, job.type, ?Z)
next.prereq* can be approximated by next.next.prereq* (distance 2), with
EnglishStudies again relaxed to Humanities (distance 2)
Results: (both at distance 4 from the original query)
Y = ep33, Z = Author
Y = e34, Z = AssociateEditor
21
22. How?
Theory
Construction of a weighted non-deterministic finite
automaton (NFA) to represent the regular expression
We apply new states and transitions to the NFA to represent the
approximation and relaxation operations
Formation of a product automaton: NFA with data
graph G
We perform a lowest cost path traversal of the product
automaton; construct query tree, do joins etc
Polynomial time complexity
Correctness of algorithms proven
22
23. How?
Implementation of prototype
Graph database: DEX (http://www.sparsity-
technologies.com/dex)
Programming language: C#
Further work
New flexible operation combining APPROX and
RELAX FLEX
Optimisation!
23
24. Any questions?
Thank you for your attention!
petra.selmer.uk@gmail.com
24