The document discusses enabling collaboration on semiformal mathematical knowledge through semantic web integration. It outlines the current state of collaboration in mathematics through blogs, wikis and projects. The author proposes an integrated view of the collaboration workflow between authors, readers and reviewers to formalize, validate, present and review semiformal mathematical knowledge.
Concept Maps are very effective for language-free expression and communication of concepts visually. The fundamental structures, which are not all graphic, are also very elegant for encoding knowledge for machine processing.
The building blocks of knowledge (Nodes and Links) are NOT sufficiently "expressive & precise". HyperPlex fills this need. See the PPT by that name in https://www.slideshare.net/putchavn
Both the concepts are explained with examples.
Good for general use and a prerequisite for knowing what is knowledge and how to represent it. Leave a comment.
The document proposes the Information Bottleneck Method as a way to extract relevant information from a signal X about another signal Y. It formalizes this as finding a compressed code for X that maximizes the information about Y while minimizing the code length. This forms a bottleneck that preserves only the most relevant information. The method provides self-consistent equations to determine the optimal coding rules from X to the code and from the code to Y. It generalizes rate-distortion theory by using the relationship between X and Y to determine relevance rather than requiring an externally specified distortion function.
An Entity-Driven Recursive Neural Network Model for Chinese Discourse Coheren...ijaia
Chinese discourse coherence modeling remains a challenge taskin Natural Language Processing
field.Existing approaches mostlyfocus on the need for feature engineering, whichadoptthe sophisticated
features to capture the logic or syntactic or semantic relationships acrosssentences within a text.In this
paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse coherence evaluation
based on current English discourse coherenceneural network model. Specifically, to overcome the
shortage of identifying the entity(nouns) overlap across sentences in the currentmodel, Our combined
modelsuccessfully investigatesthe entities information into the recursive neural network
freamework.Evaluation results on both sentence ordering and machine translation coherence rating
task show the effectiveness of the proposed model, which significantly outperforms the existing strong
baseline.
The document discusses using patterns and social network analysis to manage competencies in collaborative networks. It proposes modeling a collaborative network using i* modeling to define actors, their intentions, and dependencies. Social network analysis measures like closeness and betweenness are then used to define patterns of interactions. These patterns and the network model can be used to extract competencies of members and identify competencies needed to bridge gaps between intentions and reality. The approach aims to fill the gap between intended and actual collaboration. Future work should consider contextual factors and empirical pattern evaluation.
1) The document discusses a system called MaLTe (Machine Learning from Text) that aims to extract knowledge from technical expository texts using both natural language processing and machine learning techniques.
2) MaLTe will process texts containing narratives and examples, and output a representation of the knowledge in the form of Horn clauses. Some user interaction will be required during the translation process.
3) The document outlines several challenges in applying machine learning and natural language processing to knowledge extraction from real-world texts, including their logical structure and examples. It provides an example from a tax guide to illustrate these challenges.
The document proposes a research strategy to produce computational summaries of legal cases at scale through semi-supervised learning of legal semantics. It summarizes three of the author's past papers on representing legal semantics and outlines a two-step approach: 1) Using natural language processing to automatically generate semantic interpretations of legal texts, and 2) Generalizing patterns of information extraction through unsupervised learning of semantics from a large corpus of cases. The current proposal is to initialize the model with word embeddings from legal texts and learn higher-level concepts by applying a theory of representation based on prototypes and manifolds.
Latent Semantic Analysis (LSA) is a mathematical technique for computationally modeling the meaning of words and larger units of texts. LSA works by applying a mathematical technique called Singular Value Decomposition (SVD) to a term*document matrix containing frequency counts for all words found in the corpus in all of the documents or passages in the corpus. After this SVD application, the meaning of a word is represented as a vector in a multidimensional semantic space, which makes it possible to compare word meanings, for instance by computing the cosine between two word vectors.
LSA has been successfully used in a large variety of language related applications from automatic grading of student essays to predicting click trails in website navigation. In Coh-Metrix (Graesser et al. 2004), a computational tool that produces indices of the linguistic and discourse representations of a text, LSA was used as a measure of text cohesion by assuming that cohesion increases as a function of higher cosine scores between adjacent sentences.
Besides being interesting as a technique for building programs that need to deal with semantics, LSA is also interesting as a model of human cognition. LSA can match human performance on word association tasks and vocabulary test. In this talk, Fridolin will focus on LSA as a tool in modeling language acquisition. After framing the area of the talk with sketching the key concepts learning, information, and competence acquisition, and after outlining presuppositions, an introduction into meaningful interaction analysis (MIA) is given. MIA is a means to inspect learning with the support of language analysis that is geometrical in nature. MIA is a fusion of latent semantic analysis (LSA) combined with network analysis (NA/SNA). LSA, NA/SNA, and MIA are illustrated by several examples.
Concept Maps are very effective for language-free expression and communication of concepts visually. The fundamental structures, which are not all graphic, are also very elegant for encoding knowledge for machine processing.
The building blocks of knowledge (Nodes and Links) are NOT sufficiently "expressive & precise". HyperPlex fills this need. See the PPT by that name in https://www.slideshare.net/putchavn
Both the concepts are explained with examples.
Good for general use and a prerequisite for knowing what is knowledge and how to represent it. Leave a comment.
The document proposes the Information Bottleneck Method as a way to extract relevant information from a signal X about another signal Y. It formalizes this as finding a compressed code for X that maximizes the information about Y while minimizing the code length. This forms a bottleneck that preserves only the most relevant information. The method provides self-consistent equations to determine the optimal coding rules from X to the code and from the code to Y. It generalizes rate-distortion theory by using the relationship between X and Y to determine relevance rather than requiring an externally specified distortion function.
An Entity-Driven Recursive Neural Network Model for Chinese Discourse Coheren...ijaia
Chinese discourse coherence modeling remains a challenge taskin Natural Language Processing
field.Existing approaches mostlyfocus on the need for feature engineering, whichadoptthe sophisticated
features to capture the logic or syntactic or semantic relationships acrosssentences within a text.In this
paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse coherence evaluation
based on current English discourse coherenceneural network model. Specifically, to overcome the
shortage of identifying the entity(nouns) overlap across sentences in the currentmodel, Our combined
modelsuccessfully investigatesthe entities information into the recursive neural network
freamework.Evaluation results on both sentence ordering and machine translation coherence rating
task show the effectiveness of the proposed model, which significantly outperforms the existing strong
baseline.
The document discusses using patterns and social network analysis to manage competencies in collaborative networks. It proposes modeling a collaborative network using i* modeling to define actors, their intentions, and dependencies. Social network analysis measures like closeness and betweenness are then used to define patterns of interactions. These patterns and the network model can be used to extract competencies of members and identify competencies needed to bridge gaps between intentions and reality. The approach aims to fill the gap between intended and actual collaboration. Future work should consider contextual factors and empirical pattern evaluation.
1) The document discusses a system called MaLTe (Machine Learning from Text) that aims to extract knowledge from technical expository texts using both natural language processing and machine learning techniques.
2) MaLTe will process texts containing narratives and examples, and output a representation of the knowledge in the form of Horn clauses. Some user interaction will be required during the translation process.
3) The document outlines several challenges in applying machine learning and natural language processing to knowledge extraction from real-world texts, including their logical structure and examples. It provides an example from a tax guide to illustrate these challenges.
The document proposes a research strategy to produce computational summaries of legal cases at scale through semi-supervised learning of legal semantics. It summarizes three of the author's past papers on representing legal semantics and outlines a two-step approach: 1) Using natural language processing to automatically generate semantic interpretations of legal texts, and 2) Generalizing patterns of information extraction through unsupervised learning of semantics from a large corpus of cases. The current proposal is to initialize the model with word embeddings from legal texts and learn higher-level concepts by applying a theory of representation based on prototypes and manifolds.
Latent Semantic Analysis (LSA) is a mathematical technique for computationally modeling the meaning of words and larger units of texts. LSA works by applying a mathematical technique called Singular Value Decomposition (SVD) to a term*document matrix containing frequency counts for all words found in the corpus in all of the documents or passages in the corpus. After this SVD application, the meaning of a word is represented as a vector in a multidimensional semantic space, which makes it possible to compare word meanings, for instance by computing the cosine between two word vectors.
LSA has been successfully used in a large variety of language related applications from automatic grading of student essays to predicting click trails in website navigation. In Coh-Metrix (Graesser et al. 2004), a computational tool that produces indices of the linguistic and discourse representations of a text, LSA was used as a measure of text cohesion by assuming that cohesion increases as a function of higher cosine scores between adjacent sentences.
Besides being interesting as a technique for building programs that need to deal with semantics, LSA is also interesting as a model of human cognition. LSA can match human performance on word association tasks and vocabulary test. In this talk, Fridolin will focus on LSA as a tool in modeling language acquisition. After framing the area of the talk with sketching the key concepts learning, information, and competence acquisition, and after outlining presuppositions, an introduction into meaningful interaction analysis (MIA) is given. MIA is a means to inspect learning with the support of language analysis that is geometrical in nature. MIA is a fusion of latent semantic analysis (LSA) combined with network analysis (NA/SNA). LSA, NA/SNA, and MIA are illustrated by several examples.
Knuth's Definitions of Data and Information; Proposed Definition of KNOWLEDGE Putcha Narasimham
The words data and information are used without sufficient delineation of HOW, WHERE and WHEN to use them. They are at times used interchangeably and the dictionary meanings which seek to distinguish data and information end up with cyclic references. The use of these terms in computer science and information technology also follow the same colloquial trend with some pseudo-scientific attributions (raw facts are data and processed data is information) that do not pass simple tests of validity or rigor.
At times the word knowledge is used to explain the meanings of data and information, compounding the confusion and not having its own meaning. Donald E Knuth’s definitions of data and information are sufficiently precise and rigorous to be called scientific. Unfortunately Knuth's definitions of "data & information" do NOT turn up in the search results of Google, Yahoo, Bing, WolframAlpha. It seems perhaps the SINGLE AUTHENTIC source is vanishing. One definition of "data" which comes very close is from Dictionary of Military and Associated Terms.
They are discussed and used as foundation to define knowledge.
Knuth’s definition of “information” includes the word “meaning” which itself is a very complex and is wrongly defined in most places (according to me). I have a proposed definition for meaning also…too long). It is NOT essential to bring in the concept of "meaning" in the definition of "information". This is a new addition (09OCT13).
The available definitions of knowledge are examined and contrasted with Knuth’s definition of Data and Information. It is argued that “knowledge” refers to the “ability of a person or entity” “to provide data or information” “in response to a query”. This provides basis for knowledge representation, authoring and processing (separately described).
Neuro-cognitive and psychological linguistics present important area of multidisciplinary research.
In this paper we have described some possible applications of mathematical methods to neuro-cognitive linguistics. In neuro-cognitive study of language, neural architecture and neuropsychological mechanism of verbal cognition are basis of a vector–based modeling. A comparison of human mental space to a vector space is an effective way of analyzing of human semantic vocabulary, mental representations and rules of clustering and mapping in typologically different languages.
Euclidean and non-Euclidean spaces can be applied for a description of human semantic vocabulary and high order structures reflecting internal and external features of object and action (event). Vector analysis of word meaning and basic syntax structures offers new methodological opportunities to interpret effect of semantic and pragmatic forces at morphology and syntax levels.
Non-linear and metaphoric transformations present specific complex phenomenon to be described in 3D and other N-dimensional spaces in the framework of quantum semantics.
Keywords: Mental mapping, human mental lexicon, embodied and symbolic cognition, verbal cognition, semantic space, scalar, vector space, mental transformation, semantic gravity.
International Journal of Computational Engineering Research (IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Cooperating Techniques for Extracting Conceptual Taxonomies from TextFulvio Rotella
The current abundance of electronic documents requires automatic techniques that support the users in understanding their content and extracting useful information. To this aim, it is important to have conceptual taxonomies that express common sense and implicit relationships among concepts. This work proposes a mix of several tech niques that are brought to cooperation for learning them automatically. Although the work is at a preliminary stage, interesting initial results suggest to go on extending and improving the approach.
More details can be found here:
http://www.di.uniba.it/~loglisci/MCP2011/mce2011.pdf
Ontological knowledge integration for Bayesian network structure learningUniversity of Nantes
Intégration de connaissances ontologiques pour l'apprentissage des réseaux bayésiens
5èmes Journées thématiques "Apprentissage Artificiel & Fouille de Données", 28 juin 2012, Université Paris 13, France.
(title in French, but slides english)
This document discusses topic models, LDA, and related concepts. It begins with an overview of LDA and how it uses a graphical model approach for unsupervised learning. Various inference methods for LDA are discussed, including variational inference and Gibbs sampling. The document also covers extensions like correlated topic models and dynamic topic models, as well as applications and researchers in the field. Key concepts covered include posterior approximation, sampling, variational methods, and optimization.
Meetup 22/2/2018 - Artificiële Intelligentie & Human ResourcesDigipolis Antwerpen
The document discusses how artificial intelligence (AI) is impacting and will continue to impact human resources (HR) and the future of work. It describes how AI can automate certain cognitive processes like communicating, storing and retrieving information, sorting, filtering, pattern recognition, and decision making. The document also discusses using neural networks to generate inferential roles or predictions about what sentences are likely to follow from a given sentence to understand semantics. It provides an example of a model trained on a natural language inference dataset that is able to generate simple inferences. The document argues that understanding language involves drawing inferences, so inference should be at the core of models of semantic cognition.
A New Key Agreement Protocol Using BDP and CSP in Non Commutative GroupsEswar Publications
The available key agreement schemes using number theoretic, elliptic curves etc are common for cryptanalysts and associated security is vulnerable. This vulnerability further increases when we talk about modern efficient computers. So there is a need of providing new mechanism for key agreement with different properties so intruders get surprised and communication scenarios becomes stronger than before. In this paper, we propose a key agreement protocol which works in a non commutative group. We prove that our protocol meets the desired security attributes under the assumption that Conjugacy Search Problem and Decomposition Problem are hard in non commutative groups.
This document discusses linked data and its role in enabling the semantic web. It begins with an introduction to semantic technology and how it relates to web technology like the semantic web and web 2.0. It then describes the design and publication of linked data, including the nine steps toward linked open data. Finally, it provides examples of existing linked data sets and projects that have been created.
This document summarizes Andre Freitas' talk on AI beyond deep learning. It discusses representing meaning from text at scale using knowledge graphs and embeddings. It also covers using neuro-symbolic models like graph networks on top of knowledge graphs to enable few-shot learning, explainability, and transportability. The document advocates that AI engineers should focus on representation design and evaluating multi-component NLP systems.
The document provides an agenda for discussing network basics, research design, digging into social graph data, and doing a live demonstration of analyzing online social networks. It begins by introducing network theory and how relationships can be represented through graphs. Metrics like degree, betweenness, closeness, and eigenvector centrality are discussed for identifying key actors in a network. Visualization and computing these metrics in R are demonstrated on a drug user social network from Hartford, CT, with the goal of finding outliers and identifying important individuals.
Cmap Tools as an essential for teaching academic writingLawrie Hunter
IT tools are great, but they must take their place among other tools, some of them not recognized as technology, e.g. the paragraph is technology - didn't you knowtice?
Academic writing process: Cmaps as an essential tool (JALTCALL 2013, Matsumoto)Lawrie Hunter
The document describes a case study of using concept mapping (Cmaps) with English for Academic Purposes (EAP) students to improve their academic writing. It discusses how the students cycled between mapping texts and analyzing texts using Cmaps and text analysis tools. By mapping introductions to research papers and critiquing and revising the maps, the students were able to produce improved summaries. The case study suggests Cmaps are an effective tool for identifying rhetorical structure and aiding in academic writing.
This document discusses interlinking in linked data and the challenges of link discovery. It defines interlinking as the degree to which entities representing the same concept are linked to each other. It describes two categories of link discovery frameworks: ontology matching and instance matching. The key challenges of link discovery are computational complexity and selecting an appropriate link specification. Current approaches include domain-specific and universal frameworks, and active learning techniques can help guide selection of optimal link specifications.
Explanations in Dialogue Systems through Uncertain RDF Knowledge BasesDaniel Sonntag
We implemented a generic dialogue shell that can be configured for and applied to domain-specific dialogue applications. The dialogue system works robustly for a new domain when the application backend can automatically infer previously unknown knowledge (facts) and provide explanations for the inference steps involved. For this purpose, we employ URDF, a query engine for uncertain and potentially inconsistent RDF knowledge bases. URDF supports rule-based, first-order predicate logic as used in OWL-Lite and OWL-DL, with simple and effective top-down reasoning capabilities. This mechanism also generates explanation graphs. These graphs can then be displayed in the GUI of the dialogue shell and help the user understand the underlying reasoning processes. We believe that proper explanations are a main factor for increasing the level of user trust in end-to-end human-computer interaction systems.
Analysis of different similarity measures: SimrankAbhishek Mungoli
SimRank exploits object-to-object relationships very well and finds out the similarity between two objects.
We have used it in our project to find similar reasearch papers from DBLP dataset (DBLP Dataset provides a comprehensive list of research papers in computer science domain).
SimRank is a generic approach and its basic idea can also be applied to other domain of interests as well.
The document discusses the benefits of reflective teaching. It emphasizes that teachers should regularly evaluate their own practices through self-analysis and questioning in order to improve. Reflecting on lessons allows teachers to consider what was successful and how planning and instruction could be enhanced. Sharing reflections with other teachers provides opportunities for peer learning and improvement. Keeping notes, using structured reflection forms, and consulting additional resources can aid the reflective process. Reflective practice is an important part of continuous professional development for teachers.
Utilizing Open Data for interactive knowledge transferMonika Steinberg
The document proposes a qKAI application framework to provide interactive knowledge transfer through the use of open data, utilizing semantic web technologies and linked open data to build a hybrid data layer and incentivize user interaction through knowledge games that instantiate gaming components and sequences. The framework is intended to enhance the quality of open content through metadata analysis and user enrichment while refining interaction through lightweight interfaces.
Semantic Relatedness of Web Resources by XESA - Philipp SchollCROKODIl consortium
This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.
5 Lessons Learned from Designing Neural Models for Information RetrievalBhaskar Mitra
Slides from my keynote talk at the Recherche d'Information SEmantique (RISE) workshop at CORIA-TALN 2018 conference in Rennes, France.
(Abstract)
Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. Unlike classical IR models, these machine learning (ML) based approaches are data-hungry, requiring large scale training data before they can be deployed. Traditional learning to rank models employ supervised ML techniques—including neural networks—over hand-crafted IR features. By contrast, more recently proposed neural models learn representations of language from raw text that can bridge the gap between the query and the document vocabulary.
Neural IR is an emerging field and research publications in the area has been increasing in recent years. While the community explores new architectures and training regimes, a new set of challenges, opportunities, and design principles are emerging in the context of these new IR models. In this talk, I will share five lessons learned from my personal research in the area of neural IR. I will present a framework for discussing different unsupervised approaches to learning latent representations of text. I will cover several challenges to learning effective text representations for IR and discuss how latent space models should be combined with observed feature spaces for better retrieval performance. Finally, I will conclude with a few case studies that demonstrates the application of neural approaches to IR that go beyond text matching.
Knuth's Definitions of Data and Information; Proposed Definition of KNOWLEDGE Putcha Narasimham
The words data and information are used without sufficient delineation of HOW, WHERE and WHEN to use them. They are at times used interchangeably and the dictionary meanings which seek to distinguish data and information end up with cyclic references. The use of these terms in computer science and information technology also follow the same colloquial trend with some pseudo-scientific attributions (raw facts are data and processed data is information) that do not pass simple tests of validity or rigor.
At times the word knowledge is used to explain the meanings of data and information, compounding the confusion and not having its own meaning. Donald E Knuth’s definitions of data and information are sufficiently precise and rigorous to be called scientific. Unfortunately Knuth's definitions of "data & information" do NOT turn up in the search results of Google, Yahoo, Bing, WolframAlpha. It seems perhaps the SINGLE AUTHENTIC source is vanishing. One definition of "data" which comes very close is from Dictionary of Military and Associated Terms.
They are discussed and used as foundation to define knowledge.
Knuth’s definition of “information” includes the word “meaning” which itself is a very complex and is wrongly defined in most places (according to me). I have a proposed definition for meaning also…too long). It is NOT essential to bring in the concept of "meaning" in the definition of "information". This is a new addition (09OCT13).
The available definitions of knowledge are examined and contrasted with Knuth’s definition of Data and Information. It is argued that “knowledge” refers to the “ability of a person or entity” “to provide data or information” “in response to a query”. This provides basis for knowledge representation, authoring and processing (separately described).
Neuro-cognitive and psychological linguistics present important area of multidisciplinary research.
In this paper we have described some possible applications of mathematical methods to neuro-cognitive linguistics. In neuro-cognitive study of language, neural architecture and neuropsychological mechanism of verbal cognition are basis of a vector–based modeling. A comparison of human mental space to a vector space is an effective way of analyzing of human semantic vocabulary, mental representations and rules of clustering and mapping in typologically different languages.
Euclidean and non-Euclidean spaces can be applied for a description of human semantic vocabulary and high order structures reflecting internal and external features of object and action (event). Vector analysis of word meaning and basic syntax structures offers new methodological opportunities to interpret effect of semantic and pragmatic forces at morphology and syntax levels.
Non-linear and metaphoric transformations present specific complex phenomenon to be described in 3D and other N-dimensional spaces in the framework of quantum semantics.
Keywords: Mental mapping, human mental lexicon, embodied and symbolic cognition, verbal cognition, semantic space, scalar, vector space, mental transformation, semantic gravity.
International Journal of Computational Engineering Research (IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Cooperating Techniques for Extracting Conceptual Taxonomies from TextFulvio Rotella
The current abundance of electronic documents requires automatic techniques that support the users in understanding their content and extracting useful information. To this aim, it is important to have conceptual taxonomies that express common sense and implicit relationships among concepts. This work proposes a mix of several tech niques that are brought to cooperation for learning them automatically. Although the work is at a preliminary stage, interesting initial results suggest to go on extending and improving the approach.
More details can be found here:
http://www.di.uniba.it/~loglisci/MCP2011/mce2011.pdf
Ontological knowledge integration for Bayesian network structure learningUniversity of Nantes
Intégration de connaissances ontologiques pour l'apprentissage des réseaux bayésiens
5èmes Journées thématiques "Apprentissage Artificiel & Fouille de Données", 28 juin 2012, Université Paris 13, France.
(title in French, but slides english)
This document discusses topic models, LDA, and related concepts. It begins with an overview of LDA and how it uses a graphical model approach for unsupervised learning. Various inference methods for LDA are discussed, including variational inference and Gibbs sampling. The document also covers extensions like correlated topic models and dynamic topic models, as well as applications and researchers in the field. Key concepts covered include posterior approximation, sampling, variational methods, and optimization.
Meetup 22/2/2018 - Artificiële Intelligentie & Human ResourcesDigipolis Antwerpen
The document discusses how artificial intelligence (AI) is impacting and will continue to impact human resources (HR) and the future of work. It describes how AI can automate certain cognitive processes like communicating, storing and retrieving information, sorting, filtering, pattern recognition, and decision making. The document also discusses using neural networks to generate inferential roles or predictions about what sentences are likely to follow from a given sentence to understand semantics. It provides an example of a model trained on a natural language inference dataset that is able to generate simple inferences. The document argues that understanding language involves drawing inferences, so inference should be at the core of models of semantic cognition.
A New Key Agreement Protocol Using BDP and CSP in Non Commutative GroupsEswar Publications
The available key agreement schemes using number theoretic, elliptic curves etc are common for cryptanalysts and associated security is vulnerable. This vulnerability further increases when we talk about modern efficient computers. So there is a need of providing new mechanism for key agreement with different properties so intruders get surprised and communication scenarios becomes stronger than before. In this paper, we propose a key agreement protocol which works in a non commutative group. We prove that our protocol meets the desired security attributes under the assumption that Conjugacy Search Problem and Decomposition Problem are hard in non commutative groups.
This document discusses linked data and its role in enabling the semantic web. It begins with an introduction to semantic technology and how it relates to web technology like the semantic web and web 2.0. It then describes the design and publication of linked data, including the nine steps toward linked open data. Finally, it provides examples of existing linked data sets and projects that have been created.
This document summarizes Andre Freitas' talk on AI beyond deep learning. It discusses representing meaning from text at scale using knowledge graphs and embeddings. It also covers using neuro-symbolic models like graph networks on top of knowledge graphs to enable few-shot learning, explainability, and transportability. The document advocates that AI engineers should focus on representation design and evaluating multi-component NLP systems.
The document provides an agenda for discussing network basics, research design, digging into social graph data, and doing a live demonstration of analyzing online social networks. It begins by introducing network theory and how relationships can be represented through graphs. Metrics like degree, betweenness, closeness, and eigenvector centrality are discussed for identifying key actors in a network. Visualization and computing these metrics in R are demonstrated on a drug user social network from Hartford, CT, with the goal of finding outliers and identifying important individuals.
Cmap Tools as an essential for teaching academic writingLawrie Hunter
IT tools are great, but they must take their place among other tools, some of them not recognized as technology, e.g. the paragraph is technology - didn't you knowtice?
Academic writing process: Cmaps as an essential tool (JALTCALL 2013, Matsumoto)Lawrie Hunter
The document describes a case study of using concept mapping (Cmaps) with English for Academic Purposes (EAP) students to improve their academic writing. It discusses how the students cycled between mapping texts and analyzing texts using Cmaps and text analysis tools. By mapping introductions to research papers and critiquing and revising the maps, the students were able to produce improved summaries. The case study suggests Cmaps are an effective tool for identifying rhetorical structure and aiding in academic writing.
This document discusses interlinking in linked data and the challenges of link discovery. It defines interlinking as the degree to which entities representing the same concept are linked to each other. It describes two categories of link discovery frameworks: ontology matching and instance matching. The key challenges of link discovery are computational complexity and selecting an appropriate link specification. Current approaches include domain-specific and universal frameworks, and active learning techniques can help guide selection of optimal link specifications.
Explanations in Dialogue Systems through Uncertain RDF Knowledge BasesDaniel Sonntag
We implemented a generic dialogue shell that can be configured for and applied to domain-specific dialogue applications. The dialogue system works robustly for a new domain when the application backend can automatically infer previously unknown knowledge (facts) and provide explanations for the inference steps involved. For this purpose, we employ URDF, a query engine for uncertain and potentially inconsistent RDF knowledge bases. URDF supports rule-based, first-order predicate logic as used in OWL-Lite and OWL-DL, with simple and effective top-down reasoning capabilities. This mechanism also generates explanation graphs. These graphs can then be displayed in the GUI of the dialogue shell and help the user understand the underlying reasoning processes. We believe that proper explanations are a main factor for increasing the level of user trust in end-to-end human-computer interaction systems.
Analysis of different similarity measures: SimrankAbhishek Mungoli
SimRank exploits object-to-object relationships very well and finds out the similarity between two objects.
We have used it in our project to find similar reasearch papers from DBLP dataset (DBLP Dataset provides a comprehensive list of research papers in computer science domain).
SimRank is a generic approach and its basic idea can also be applied to other domain of interests as well.
The document discusses the benefits of reflective teaching. It emphasizes that teachers should regularly evaluate their own practices through self-analysis and questioning in order to improve. Reflecting on lessons allows teachers to consider what was successful and how planning and instruction could be enhanced. Sharing reflections with other teachers provides opportunities for peer learning and improvement. Keeping notes, using structured reflection forms, and consulting additional resources can aid the reflective process. Reflective practice is an important part of continuous professional development for teachers.
Utilizing Open Data for interactive knowledge transferMonika Steinberg
The document proposes a qKAI application framework to provide interactive knowledge transfer through the use of open data, utilizing semantic web technologies and linked open data to build a hybrid data layer and incentivize user interaction through knowledge games that instantiate gaming components and sequences. The framework is intended to enhance the quality of open content through metadata analysis and user enrichment while refining interaction through lightweight interfaces.
Semantic Relatedness of Web Resources by XESA - Philipp SchollCROKODIl consortium
This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.
5 Lessons Learned from Designing Neural Models for Information RetrievalBhaskar Mitra
Slides from my keynote talk at the Recherche d'Information SEmantique (RISE) workshop at CORIA-TALN 2018 conference in Rennes, France.
(Abstract)
Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. Unlike classical IR models, these machine learning (ML) based approaches are data-hungry, requiring large scale training data before they can be deployed. Traditional learning to rank models employ supervised ML techniques—including neural networks—over hand-crafted IR features. By contrast, more recently proposed neural models learn representations of language from raw text that can bridge the gap between the query and the document vocabulary.
Neural IR is an emerging field and research publications in the area has been increasing in recent years. While the community explores new architectures and training regimes, a new set of challenges, opportunities, and design principles are emerging in the context of these new IR models. In this talk, I will share five lessons learned from my personal research in the area of neural IR. I will present a framework for discussing different unsupervised approaches to learning latent representations of text. I will cover several challenges to learning effective text representations for IR and discuss how latent space models should be combined with observed feature spaces for better retrieval performance. Finally, I will conclude with a few case studies that demonstrates the application of neural approaches to IR that go beyond text matching.
Effective Semantics for Engineering NLP SystemsAndre Freitas
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
Here are some science-related events from EventKG that took place in Lyon:
- 1921: "À Lyon, fusion de la Société de médecine et de la Société des sciences médicales" (In Lyon, merger of the Medical Society and the Society of Medical Sciences)
- 1987: "The International Astronomical Union organizes its 24th General Assembly in Lyon"
- 1988: "The International Astronomical Union organizes its 25th General Assembly in Lyon"
- 2009: "The International Astronomical Union organizes its 26th General Assembly in Lyon"
- 2015: "The International Astronomical Union organizes its 29th General Assembly in Lyon"
-
This document discusses theories of latent semantics and social interaction. It outlines latent semantic analysis (LSA) and social network analysis (SNA) as methods to analyze meaning and interactions. It proposes meaningful interaction analysis (MIA) as a technique that combines LSA and SNA to study associative closeness structures and social relations in latent semantic spaces. Examples of applying MIA to analyze forum postings, virtual meeting attendance, and blog subscriptions are provided.
Schema Engineering for Enterprise Knowledge GraphsVera G. Meister
Knowledge graphs for the support of knowledge-intensive tasks and processes, or as a backbone of an organizational digitalization strategy are intensively discussed in recent scientific literature. What is largely missing, are aspects of the organizational management and even the major change of intermediary roles between business domain experts and IT specialists. Based on a literature review this paper introduces a comprehensive definition together with an abstract model of a knowledge graph in companies, a so-called Enterprise Knowledge Graph (EKG). Considering one of its core elements, the development of an EKG schema, i.e. the formal knowledge structure of the business domains to model, was analyzed in detail and reflected in the course of a case study. The new role of a knowledge engineer and its responsibilities as well as necessary competencies were outlined and discussed from real world perspective. The findings of the paper constitute an initial building block of a comprehensive framework for the implementation and the assessment of the maturity of knowledge graphs in organizations.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
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.
Ontology based semantics and graphical notation as directed graphsJohann Höchtl
The document discusses ontology-based semantics and visualization of ontologies as directed graphs. It provides an overview of ontology visualization tools including IsaViz, Protégé, Jambalaya, and Graphviz. It also discusses notions of ontology similarity and approaches used by tools like COMA++, BayesOWL, and SOQA-SimPack to measure structural and semantic similarity between ontologies.
Different Semantic Perspectives for Question Answering SystemsAndre Freitas
Question Answering systems define one of the most complex tasks in computational semantics. The intrinsic complexity of the QA task allows researchers of QA systems to investigate and explore different perspectives of semantics. However, this complexity also induces a bias towards a systems perspective, where researchers are alienated from a deeper reasoning on the semantic principles that are in place within the different components of the system. In this talk we will explore the semantic challenges, principles and perspectives behind the components of QA systems, aiming at providing a principled map and overview on the contribution of each component within the QA semantic interpretation goal.
Similar to Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration (20)
Faire Datenökonomie für Wirtschaft, Wissenschaft und Gesellschaft: Was brauch...Christoph Lange
In Wirtschaft und Wissenschaft entstehen zunehmend Infrastrukturen für Datenaustausch. Der Wirtschaft ist Vertrauen unter Geschäftspartnern wichtig und Souveränität darüber, was Andere mit meinen Daten machen – die Wissenschaft betont freie Zugänglichkeit und Nachnutzbarkeit. FAIR Data Spaces verbinden beides auf Grundlage gemeinsamer Prinzipien.
Was muss getan werden, damit Datenaustausch nicht mehr bedeutet, E-Mail-Anhänge zu verschicken oder Geheimnisse zentralen Plattformen feindlicher Mächte anzuvertrauen? Wirtschaft, Wissenschaft und öffentliche Verwaltung suchen zunehmend nach Lösungen, um den Datenaustausch sicher und effizient zu gestalten und damit neues Innovationspotenzial zu heben. Was gibt es schon, was ist geplant, und wie können vorhandene Initiativen zusammenwachsen, um Daten über die Grenzen dieser Welten hinaus gemeinsam zu nutzen?
Initiativen der Wirtschaft wie Gaia-X und International Data Spaces priorisieren den Aufbau von Vertrauen unter Geschäftspartner:innen ohne Papier-Verträge sowie die Souveränität darüber, was Andere mit den eigenen wertvollen Daten machen. In der Wissenschaft, zum Beispiel bei der Nationalen Forschungsdateninfrastruktur NFDI, geht es um freie Zugänglichkeit und Nachnutzbarkeit im Einklang mit ethischen Prinzipien. Der öffentlichen Hand ist neben dem freien Zugang etwa zu Open-Data-Portalen die digitale Daseinsvorsorge wichtig. Große Herausforderungen unserer Zeit erfordern Datenaustausch nicht nur innerhalb dieser Welten, sondern über ihre Grenzen hinaus:
zum Beispiel zwischen Forschungsinstituten und kleinen Technologie-Unternehmen, die nicht alle Daten selbst sammeln können,
oder zwischen großen Unternehmen mit reichen Datenschätzen und wirtschaftlichen Interessen und einer Nutzung dieser Daten für das Gemeinwohl.
Das Projekt FAIR Data Spaces schafft Bausteine für übergreifende Datenräume als Keimzellen einer fairen Datenökonomie nach gemeinsamen Prinzipien. Wir möchten diskutieren, wie weit die aus dem Forschungsdatenmanagement stammenden FAIR-Data-Prinzipien tragen, wonach Daten findable (auffindbar), accessible (zugänglich), interoperabel und reusable (nachnutzbar) sein sollen. Das Projekt verfolgt den Plan, vorhandene Initiativen organisatorisch, rechtlich, technisch und praktisch zu einer gemeinsamen Community zusammenzuführen, und lebt dabei von einer breiten Mitwirkung. Werdet mit dem Fraunhofer IUK-Verbund Teil dieser Community und bleibt dabei innovativ und kritisch!
Machine Support for Interacting with Scientific Publications Improving Inform...Christoph Lange
1) The document discusses using semantic web and linked data technologies to help assess the quality of scientific output by answering questions about workshops, conferences, publications, and data.
2) It proposes connecting bibliographic metadata, citations, full text, social networks and research data using initiatives like schema.org to provide machine support for quality assessment.
3) The goal is to provide complementary metrics to human peer review and impact factors by enabling multidimensional, context-sensitive analysis of trends, topics, citations and more.
Interlinking Data and Knowledge in Enterprises, Research and Society with Lin...Christoph Lange
The Linked Data paradigm has emerged as a powerful enabler for data and knowledge interlinking and exchange using standardised Web technologies.
In this article, we discuss our vision how the Linked Data paradigm can be employed to evolve the intranets of large organisations -- be it enterprises, research organisations or governmental and public administrations -- into networks of internal data and knowledge.
In particular for large enterprises data integration is still a key challenge. The Linked Data paradigm seems a promising approach for integrating enterprise data. Like the Web of Data, which now complements the original document-centred Web, data intranets may help to enhance and flexibilise the intranets and service-oriented architectures that exist in large organisations. Furthermore, using Linked Data gives enterprises access to 50+ billion facts from the growing Linked Open Data (LOD) cloud. As a result, a data intranet can help to bridge the gap between structured data management (in ERP, CRM or SCM systems) and semi-structured or unstructured information in documents, wikis or web portals, and make all of these sources searchable in a coherent way.
Keynote at Baltic DB&IS 2014, 9 June 2014, Tallinn, Estonia
Linked Open (Geo)Data and the Distributed Ontology Language – a perfect matchChristoph Lange
The Distributed Ontology Language is a meta-language for integrating
ontologies written in different languages. Our notion of “distributed”
comprises logical heterogeneity within ontologies, modularity and reuse,
and links across ontologies in different places of the Web. Not only
can ontologies be distributed across the Web, but DOL's supply of
supported ontology languages can also be extended in a decentral way.
For this functionality, DOL builds on the Linked Open Data (LOD)
principles. But DOL also contributes to LOD use cases. Many current
LOD applications are limited by the weak expressivity of the RDF and
RDFS languages commonly used to express data and vocabularies.
Completely switching to a more expressive language would impair
scalability to big datasets. DOL addresses the scalability and
expressivity requirements by allowing to represent each aspect of a
dataset in the most suitable language and keeping these different
representations connected. This is particularly useful in geographic
information systems, where big datasets (e.g. Linked Geo Data, the LOD
version of OpenStreetMap) need to be integrated with formalisations of
complex spatial notions (e.g. in the first-order language Common Logic).
Linking Big Data to Rich Process DescriptionsChristoph Lange
Linked (Open) Data is one key to coping with Big Data: it enables decentralised, collaborative management of big datasets, low-overhead information retrieval, and scalable reasoning. Big Data are created or consumed by technical processes or business processes. Their formal description, e.g. for software verification or compliance checking, requires logics whose complexity far exceeds that of the data. Restricting LOD to the RDF logic does not allow for integrating rich process descriptions with the data that these processes create, and therefore does not enable knowledge management, information retrieval and reasoning to take full advantage of rich background knowledge. In this talk I demonstrate different frontiers at which I have worked towards achieving an integration of process descriptions and data.
The Distributed Ontology Language (DOL): Use Cases, Syntax, and ExtensibilityChristoph Lange
The document discusses the Distributed Ontology Language (DOL) which aims to support semantic integration and interoperability across heterogeneous ontologies. DOL allows for logically heterogeneous ontologies, modular ontologies, and formal and informal links between ontologies. It has a formal semantics and can be serialized in XML, RDF, and text. Examples of applications that could benefit from DOL include an ontology repository engine and a multilingual map user interface driven by aligned ontologies.
Bringing Mathematics To the Web of Data: the Case of the Mathematics Subject ...Christoph Lange
This document discusses redesigning the Mathematics Subject Classification (MSC) scheme as a linked dataset using SKOS. Key points include: representing the MSC hierarchy using SKOS concepts and properties; adding multilingual labels and mathematical markup; linking related concepts within and across schemes; and deploying the dataset on the web with a SPARQL endpoint for access. The redesign aims to facilitate maintenance and reuse while preserving all existing MSC information.
Semantic Web Technology: The Key to Making Scientific Information Systems SocialChristoph Lange
This document discusses how semantic web technologies can make scientific information systems more social. It provides examples of how schema.org defines structured data for annotating web pages with information like movies, reviews, and social relationships between people. It also briefly mentions Facebook's Open Graph protocol. The key points are that semantic web annotations allow machines to understand web data in order to assist users, initiatives like schema.org are making these annotations mainstream, and structured semantic data enables social features for information sharing and collaboration.
Making Heterogeneous Ontologies Interoperable Through StandardisationChristoph Lange
The document discusses making heterogeneous ontologies interoperable through standardization, presenting a scenario of an assisted living environment where different devices like a wheelchair and freezer need to communicate but use different ontologies. It argues for developing a standardized meta ontology language to facilitate integration and interoperability between these diverse ontologies used by different devices with varying knowledge needs.
Previewing OWL Changes and Refactorings Using a Flexible XML DatabaseChristoph Lange
The document discusses using a flexible XML database called TNTBase to preview changes and refactorings to ontologies. TNTBase allows editing ontologies through "virtual documents" that define editable XML views of ontology content. This enables refactoring ontologies by previewing the effects of changes like extracting subclasses into a new module before making the changes live. The document provides examples of refactoring an ontology in this way and describes the underlying library functions that power the refactoring previews.
The document proposes an architecture called JOBAD that allows mathematical documents to interactively access web services. JOBAD uses JavaScript to integrate definition lookup, unit conversion, and other services directly into OMDoc-based documents. This allows readers to interactively adapt document appearance and access remote explanations and computations without leaving the document interface. Future plans include more interactive customization and linking documents to external search and information resources.
The document describes a project to publish mathematics lecture notes as linked data. Key points:
1) Lecture notes containing 2,000 slides and 1,000 homework problems were semantically annotated and converted to RDF to create structured data.
2) The RDF is stored in a triplestore and can be queried with an OMDoc-aware SPARQL endpoint or full-text search.
3) Annotations in the human-readable XHTML documents link to services for interactivity. The goal is to scale this to 300,000 annotated publications and link to external datasets.
sTeX+ – a System for Flexible Formalization of Linked DataChristoph Lange
The document describes S EX+, an extension of S EX that allows formalizing and annotating technical documents with semantic metadata. S EX+ enables defining ad hoc vocabularies to describe project-specific concepts and annotate documents accordingly. It produces output in PDF, OMDoc+RDFa, and XHTML+MathML+RDFa to enable interactive services. S EX+ aims to balance formalization with flexibility for existing authoring practices.
Krextor – An Extensible Framework for Contributing Content Math to the Web of...Christoph Lange
Moseley (DBTune) (DBTune) RAMEAU
Folk NTU SH lobid
GTAA Plymouth Resource
Krextor is an extensible framework for contributing mathematical content from OpenMath CDs to the Web of Data. It converts OpenMath CDs, which are document-oriented, to RDF, which follows the graph-based RDF data model used by the Web of Data. As an example, it can link a mathematical property in an OpenMath CD to its identifier by grouping the property and giving it an ID, without modifying the original CD. This allows bootstrapping mathematics onto the Web of Data in
The document discusses the mathematical semantics of statistical data. It presents examples of derived statistical values for populations and unemployment rates for two locations. It raises questions about how to validate derived values and compute them for new data points. It proposes representing mathematical expressions as ordered n-ary trees in RDF to integrate math into the semantic web of data.
Ontology Integration and Interoperability (OntoIOp) – Part 1: The Distributed...Christoph Lange
The document introduces the Distributed Ontology Language (DOL), which is part of the Ontology Integration and Interoperability (OntoIOP) standard. DOL aims to enable logical and modular heterogeneity across ontologies to improve semantic integration and interoperability. It will serve as a logic-agnostic meta-language for structuring ontologies, ontology modules, and formal and informal links between ontologies. DOL is intended to have well-defined semantics and serializations to XML, RDF, and text to facilitate reuse of existing ontologies and reasoning over heterogeneous ontological representations.
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
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UiPath Test Automation using UiPath Test Suite series, part 5
Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration
1. Overview Service Integration Knowledge Representation Conclusion & Future
Enabling Collaboration
on Semiformal Mathematical Knowledge
by Semantic Web Integration
Christoph Lange
Jacobs University, Bremen, Germany
KWARC – Knowledge Adaptation and Reasoning for Content
2011-03-11
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 1
2. Overview Service Integration Knowledge Representation Conclusion & Future
Why Mathematics?
Mathematics
ubiquitous foundation of science, technology, and engineering
these have in common:
rigorous style of argumentation
symbolic formula language
similar process of understanding results
Mathematical Knowledge
complex structures
. . . that have been well studied and understood
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 2
3. Overviewtime. Note factoring a quartic into two real quadratics is different than
sign each Service Integration Knowledge Representation Conclusion & Future
trying to find four complex roots.
Definition: A function f is analytic on an open subset R ⊂ C if f is complex
Semiformal Mathematical Knowledge
differentiable everywhere on R; f is entire if it is analytic on all of C.
2 Proof of the Fundamental Theorem via Liouville
Theorem 2.1 (Liouville). If f (z) is analytic and bounded in the complex plane,
then f (z) is constant.
Informal
We now prove
Theorem 2.2 (Fundamental Theorem of Algebra). Let p(z) be a polynomial
Formalized = Computerized
with complex coefficients of degree n. Then p(z) has n roots.
Proof. It is sufficient to show any p(z) has one root, for by division we can then
write p(z) = (z − z0 )g(z), with g of lower degree.
Note that if
p(z) = an z n + an−1 z n−1 + · · · + a0 , (2)
then as |z| → ∞, |p(z)| → ∞. This follows as
an−1 a0
p(z) = z n · an + + ··· + n . (3)
z z
1
Assume p(z) is non-zero everywhere. Then p(z) is bounded when |z| ≥ R.
1 1
Also, p(z) = 0, so p(z) is bounded for |z| ≤ R by continuity. Thus, p(z) is
a bounded, entire function, which must be constant. Thus, p(z) is constant, a
contradiction which implies p(z) must have a zero (our assumption).
[Lev]
Semiformal – a pragmatic and practical compromise
2
anything informal that is intended to or could in principle be
formalized
combinations of informal and formal for both human and
machine audience
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 3
4. Overview Service Integration Knowledge Representation Conclusion & Future
Collaboration in Mathematics
History of collaboration
in the small: Hardy/Littlewood
in the large: hundreds of
mathematicians classifying the finite
simple groups
“industrialization” of research
Utilizing the Social Web
research blogs: Baez, Gowers, Tao
Polymath: collaborative proofs
Collaboration = creation,
formalization, organization,
understanding, reuse, application Polymath wiki/blog: P ≠ NP proof
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 4
5. Overview Service Integration Knowledge Representation Conclusion & Future
An Integrated View on a Collaboration Workflow
The author(s): The reader(s): The reviewer(s):
0 original idea (in one’s “What does that 1 read paper (← )
mind) mean?”: missing 2 verify claims
background,
1 formalize into
used to different 3 point out problems
structured document
notation with the paper and
2 search existing its formal concepts
“How does that
knowledge to build
work?”
on
“What is that good
3 validate formal
for?”
structure
look up background
4 present in a
information in cited
comprehensible way
publications
5 submit for review
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 5
6. Overview Service Integration Knowledge Representation Conclusion & Future
Looking up Background Knowledge
“What does that mean?”
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 6
7. Overview Service Integration Knowledge Representation Conclusion & Future
Adapting the Presentation to Familiar
Terminology
“What does that mean?” – here: unfamiliar unit system (imperial vs.
metric)
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 7
8. Overview Service Integration Knowledge Representation Conclusion & Future
Looking up Related Information
“What can I reuse — what is that good for — where/how is it applied?”
Sussex St.
Reading Andrews NDL
Audio- Lists Resource subjects t4gm
MySpace scrobbler Lists
Moseley (DBTune) (DBTune) RAMEAU
Folk NTU SH lobid
GTAA Plymouth Resource
Lists
Organi-
Reading
Lists
sations
Music The Open ECS
Magna- Brainz Music
DB tune Library LCSH South-
(Data Brainz LIBRIS ampton
Tropes lobid Ulm
Incubator) (zitgist) Man- EPrints
Resources
chester
Surge Reading
biz. Music RISKS
Radio Lists The Open ECS
data. John Brainz
Discogs Library PSH Gem. UB South-
gov.uk Peel (DBTune)
FanHubz (Data In- (Talis) Norm- Mann- ampton
(DB cubator) Jamendo datei heim RESEX
Tune)
Popula- Poké- DEPLOY
Last.fm
tion (En- pédia
Artists Last.FM Linked RDF
AKTing) research EUTC (DBTune) (rdfize) LCCN VIAF Book Wiki
data.gov Produc- Pisa Eurécom
P20 Mashup semantic
NHS .uk tions classical web.org
(EnAKTing) Pokedex
(DB
Mortality Tune) PBAC ECS
(En-
AKTing)
BBC MARC (RKB Budapest
Program Codes Explorer)
Energy education OpenEI BBC List Semantic Lotico Revyu OAI
(En- CO2 data.gov mes Music Crunch SW
AKTing) (En- .uk Chronic- Linked Dog
NSZL Base
AKTing) ling Event- MDB RDF Food IRIT
America Media Catalog
ohloh
BBC DBLP ACM IBM
Good- BibBase
Ord- Wildlife (RKB
Openly Recht- win
nance Finder Explorer)
Local spraak. Family DBLP
legislation Survey Tele- New VIVO UF
.gov.uk nl graphis York flickr (L3S) New-
VIVO castle
Times URI wrappr OpenCal Indiana RAE2001
UK Post- Burner ais DBLP
codes statistics (FU
VIVO CiteSeer Roma
data.gov LOIUS Taxon iServe Berlin) IEEE
.uk Cornell
Concept Geo
World data
ESD Fact- OS dcs
Names book dotAC
stan- reference Project
Linked Data NASA (FUB) Freebase
dards data.gov Guten-
.uk
for Intervals (Data GESIS Course-
transport DBpedia berg STW ePrints CORDIS
Incu- ware
data.gov bator) (FUB)
Fishes ERA UN/
.uk
of Texas Geo LOCODE
Uberblic
Euro- Species
The stat dbpedia TCM SIDER Pub KISTI
(FUB) lite Gene STITCH Chem JISC
London Geo KEGG
DIT LAAS
Gazette TWC LOGD Linked Daily OBO Drug
Eurostat Data UMBEL lingvoj Med
(es) Disea-
YAGO Medi some
Care ChEBI KEGG NSF
Linked KEGG KEGG
Linked Drug Cpd
GovTrack rdfabout Glycan
Sensor Data CT Bank Pathway
US SEC Open Reactome
(Kno.e.sis) riese Uni
Cyc Lexvo Path-
totl.net way Pfam PDB
Semantic HGNC
XBRL
WordNet KEGG KEGG
(VUA) Linked Taxo- CAS Reaction
rdfabout Twarql UniProt Enzyme
EUNIS Open nomy
US Census Numbers PRO- ProDom
SITE Chem2
UniRef Bio2RDF
Climbing WordNet SGD Homolo
Linked (W3C) Affy- Gene
Cornetto
GeoData metrix PubMed Gene
UniParc
Ontology
GeneID
Airports
Product
DB UniSTS MGI
Gen
Bank OMIM InterPro
As of September 2010
e-science data – with opaque mathematical models
statistical datasets – without mathematical derivation rules
publication databases – without mathematical content
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 8
9. Overview Service Integration Knowledge Representation Conclusion & Future
Pointing out and Discussing Problems
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 9
10. Overview Service Integration Knowledge Representation Conclusion & Future
Collaboration Still has to be Enabled!
Many collaboration tasks not currently well supported by machines
For other tasks there is (limited) support
creating and formalizing documents – semiformal!?
search existing knowledge to build on – semiformal!?
computation (recall unit conversion) – but not inside documents
publishing in textbook style – could it be more comprehensible?
adapting notation (e.g. ⋅ ×, n k Cn ) – not quite on demand
k
Existing machine services only focus on primitive tasks
Can’t simply be put together, as they . . .
. . . speak different languages
. . . take different perspectives on knowledge
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 10
11. Overview Service Integration Knowledge Representation Conclusion & Future
Document Perspective: XML Markup
XHTML+MathML(+OpenMath)
... is <math>
<mn>9144</mn>
<mo>⁢</mo>
<mo>m</mo>
</math> from city ...
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 11
12. Overview Service Integration Knowledge Representation Conclusion & Future
Network Perspective: RDF Graphs
Look up Related Information: Point out and Discuss Problems:
Sussex St.
hasDiscussion `
forum1 definition
Reading Andrews NDL
Audio- Lists Resource subjects
(IkeWiki ontology)
t4gm
MySpace scrobbler Lists
Moseley (DBTune) (DBTune) RAMEAU
Folk NTU SH lobid
GTAA Plymouth Resource
Lists
Organi-
Reading
sations
exemplifies
Lists
Music The Open ECS
Magna- Brainz Music
DB Library LCSH South-
post1: Issue
tune (Data Brainz LIBRIS
lobid ampton Ulm
Tropes Incubator) (zitgist) Man- Resources EPrints
chester
Surge Reading
(UnclearWh.Useful)
biz. Music RISKS
Radio Lists The Open ECS
data. Brainz
example
John Discogs Library PSH Gem. UB South-
gov.uk Peel (DBTune)
FanHubz (Data In- (Talis) Norm- Mann- ampton
(DB
has_reply
cubator) Jamendo datei heim RESEX
elaborates_on
Tune)
Popula- Poké- DEPLOY
Last.fm
tion (En- pédia
Artists Last.FM Linked RDF
AKTing) research EUTC (DBTune) (rdfize) LCCN VIAF Book Wiki
data.gov Produc- Pisa Eurécom
P20 Mashup semantic
NHS
post2: Elaboration
.uk tions classical
Pokedex web.org
(EnAKTing) (DB
Mortality Tune) PBAC ECS
(En-
AKTing)
BBC MARC (RKB Budapest
Codes
has_container
Program Explorer)
OpenEI BBC
agrees_with
Energy education Semantic Lotico Revyu OAI
List
(En- CO2 data.gov mes Music Crunch SW
AKTing) (En- .uk Chronic- Linked Dog
NSZL Base
AKTing) ling Event- MDB RDF Food IRIT
Catalog
resolvesInto
America Media ohloh
BBC DBLP ACM
post3: Position
Good- BibBase IBM
Ord- Wildlife (RKB
Openly Recht- win
nance Finder Explorer)
Local spraak. Family DBLP
legislation Survey Tele- New
proposes_
VIVO UF
.gov.uk nl graphis York flickr (L3S) New-
VIVO castle
Times URI wrappr OpenCal Indiana RAE2001
UK Post- Burner ais DBLP
codes statistics
data.gov
.uk
LOIUS Taxon
Concept Geo
World
iServe
VIVO
Cornell
(FU
Berlin)
data
IEEE
CiteSeer Roma
solution_for knowledge
post4: Idea items
ESD Fact- OS dcs
Names book dotAC
stan- reference Project
Linked Data NASA (FUB) Freebase
dards data.gov Guten-
.uk
for Intervals (Data GESIS Course-
(ProvideExample)
STW CORDIS
(OMDoc ontology)
transport Incu- DBpedia berg ePrints
(FUB) ware
data.gov bator) Fishes ERA UN/
.uk
of Texas Geo LOCODE
Uberblic
on wiki pages
Euro- Species
supports
The stat dbpedia TCM SIDER Pub KISTI
(FUB) lite Gene STITCH Chem JISC
decides
London Geo KEGG
DIT LAAS
Gazette TWC LOGD Linked Daily OBO Drug
Eurostat Data UMBEL lingvoj Med
Disea-
post5: Evaluation
(es)
YAGO Medi some
Care ChEBI KEGG NSF
Linked KEGG KEGG
Linked Drug Cpd
GovTrack rdfabout Glycan
Sensor Data CT Bank Pathway
Open
agrees_with
US SEC riese Reactome
(Kno.e.sis) Cyc
Uni
Lexvo Path-
totl.net way Pfam PDB
Semantic HGNC
XBRL
KEGG
post6: Position
WordNet KEGG
(VUA) Linked Taxo- CAS Reaction
rdfabout Twarql UniProt Enzyme
EUNIS Open nomy
US Census Numbers PRO- ProDom
SITE Chem2
UniRef Bio2RDF
Climbing WordNet SGD Homolo
Linked (W3C) Affy- Gene
Cornetto
GeoData metrix PubMed Gene
post7: Decision
UniParc
Ontology
GeneID
Airports
Product
DB UniSTS
Gen
MGI supported_by
Bank OMIM InterPro
argumentative
As of September 2010
physical structure structure
(SIOC Core) discussion page (SIOC Arg.)
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 12
13. Overview Service Integration Knowledge Representation Conclusion & Future
How to Enable Collaboration?
Integrate a wide range of different services
As they currently speak different languages, . . .
first create a unified interoperability layer for knowledge
representations (document vs. network perspective)
then translate between different representations
Tool: semantic web technology
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 13
14. Overview Service Integration Knowledge Representation Conclusion & Future
Contribution
Building a collaboration environment is not trivial
Collection of foundational, enabling technologies
OMDoc+RDF(a), a unified interoperability layer for representing
semiformal mathematical knowledge (document and network
perspective)
Design patterns for integrating services
interactive assistance in published documents
translations inside knowledge bases
Evaluation of how effectively an integrated environment built
that way (a semantic wiki for mathematics) supports practical
workflows
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 14
15. Overview Service Integration Knowledge Representation Conclusion & Future
SWiM, an Integrated Collaboration Environment
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 15
16. Overview Service Integration Knowledge Representation Conclusion & Future
SWiM, an Integrated Collaboration Environment
Semantic wiki, combining knowledge production and consumption
Editor for documents, Graph-based Localized discussion
formulæ, metadata navigation forums
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 16
17. Overview Service Integration Knowledge Representation Conclusion & Future
Usability Evaluation of the SWiM Prototype
Integration is feasible, but is the result usable?
learnable?
effective?
useful?
satisfying to use?
Can we effectively support maintenance workflows (on the
OpenMath CDs)?
Quick local fixing of minor errors
(in text, formalization, or presentation)
Peer review, and preparing major revisions by discussion
In general: What particular challenges to usability does the
integration of heterogenenous services entail?
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 17
18. Overview Service Integration Knowledge Representation Conclusion & Future
Feedback Statements from Test Users
positive
statement
successful 93
action
95 understood
concept
36
18 not understood concept
18 unexpected bug
negative 61
statement 43
dissatisfaction
52 44
51
confusion/uncertainty not understood
expectation what to do
not met
Understanding only seems marginal, but had a high impact on
successfully accomplishing tasks!
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 18
19. Overview Service Integration Knowledge Representation Conclusion & Future
Interpretation and Consequences
Usability hypotheses largely hold, but:
Users with previous knowledge of related knowledge models or
UIs had advantages
Less experienced users frequently taken in by misconceptions;
requested better explanations
Users expected a more coherent integration
User interfaces need Semantic Transparency (for learnability):
self-explaining user interfaces
familiar and consistent terminology (despite XML/RDF
heterogeneity under the hood!)
The SWiM user interface is not yet self-explaining
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 19
20. Overview Service Integration Knowledge Representation Conclusion & Future
Self-explaining Publications
and Assistive Services
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 20
21. Overview Service Integration Knowledge Representation Conclusion & Future
Structures of Mathematical Knowledge (MK)
Goal: design unified interoperability layer for all relevant aspects of MK
Different degrees of formality: informal, formalized, semiformal
Classification of structural dimensions:
logical/functional: symbols, objects, statements, theories
rhetorical/document: from chapters down to phrases
presentation: e.g. notation of symbols
metadata: general administrative ones;
applications/projects/people
discussions about MK (e.g. about problems)
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 21
23. Overview Service Integration Knowledge Representation Conclusion & Future
OMDoc+RDF(a) as an Interoperability Layer for
Exchanging and Reusing MK
1 Translate OMDoc to RDF
formalize conceptual model as an ontology
reused existing ontologies for rhetorics, metadata, etc.
specified an XML→RDF translation for identifiers and structures
2 Embed RDFa into OMDoc
extend OMDoc beyond mathematics
embed arbitrary metadata into mathematical documents
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 23
24. Overview Service Integration Knowledge Representation Conclusion & Future
Creating an RDF Resource from an XML Node
<theory name="group"> <http://ma.th/group>
<symbol name="op"> rdf:type omdoc:Theory ; Theory symbol
<type> omdoc:homeTheoryOf
M×M→M <http://ma.th/group#symbol> . rdf:type
</type> <http://ma.th/group#symbol> rdf:type homeTheoryOf
</symbol> rdf:type omdoc:Symbol ; . . . /group . . . /group#op
</theory> omdoc:declaredType ... .
Algorithm:
Require: b, p, u, T , P ∈ U, n is an XML node,
T is the URI of an ontology class or empty, P is the URI of an ontology property or empty
Ensure: R ∈ U × U × (U ∪ L) is an RDF graph
R←
if u = ε then {if no explicit URI is defined by the rule, . . . }
u ← mint(b, n) {. . . try to mint one, using built-in or custom minting functions (configurable per extraction module)}
end if
if u ≠ ε then {if we got a URI, . . . }
if T ≠ ε then
R ← R ∪ { u, rdf type, T } {make this resource an instance of the given class}
end if
if P ≠ ε then
R ← R ∪ add_uri_property( , p, P, u) {create a link (e.g. of a type like hasPart) from the parent subject to this resource}
end if
for all c ∈ π NS ($n ∗ $n @∗) do {from each element and attribute child node (determined using an XPath evaluation function
returning a nodeset) . . . }
R ← R ∪ extract(b, c, u) {. . . recursively extract RDF, using the newly created resource as a parent subject}
end for{i.e. the recursion terminates for nodes without children}
end if
return R
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 24
25. Overview Service Integration Knowledge Representation Conclusion & Future
The OMDoc Ontology (simplified)
dependsOn,
MathKnowledgeItem hasPart, subClassOf
verbalizes
Type
other
properties
Theory Statement
homeTheory
imports
From
e
imports,
NonConstitutive
p
hasTy
Import metaTheory
Statement
Notation
Constitutive Definition
Statement Proof
Example Assertion
proves
Symbol hasDefinition
Axiom exemplifies
Definition bol
Sym
ers
rend
Christoph Lange Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web Integration 2011-03-11 25