SWiM – A Semantic Wiki for Mathematical Knowledge Management

•

1 like•208 views

SWiM is a semantic wiki for collaborative mathematical knowledge management. It uses IkeWiki combined with mathematical markup languages to allow users to formally edit content like proofs, formulas, and symbol dictionaries. The system extracts structured data from the wiki pages using an ontology and stores it as an RDF graph to power search and navigation services. SWiM aims to facilitate workflows like collaborative proof formalization and development of OpenMath content dictionaries. The approach is intended to be adaptable to other technical domains that use semantic markup.

Technology Education Business

Research Background and Application Context Key Technology The System Use Cases and Applications Conclusion

SWiM – A Semantic Wiki for Mathematical Knowledge
Management
5th European Semantic Web Conference 2008, Demo Track

Christoph Lange

Jacobs University, Bremen, Germany
KWARC – Knowledge Adaptation and Reasoning for Content

June 3, 2008

Ch. Lange (Jacobs University Bremen) SWiM – A Semantic Wiki for Mathematical Knowledge Management 2008
June 3, 1

Research Background and Application Context Key Technology The System Use Cases and Applications Conclusion

Mathematical Knowledge Management

Goal: support collaborative authoring of documents usable for
knowledge management
semantic markup common for documents in mathematics: MathML,
OpenMath, OMDoc
layers of knowledge: symbols, statements, theories, documents
has many applications, but how to acquire the knowledge?
⇒ services to support the authoring workﬂow

Example (a simple formula?)
<apply>
<csymbol definitionURL="http://openmath.org/cd/arith1#plus"/>
<cn type="integer">1</cn>
<ci>n</ci>
</apply>

Ch. Lange (Jacobs University Bremen) SWiM – A Semantic Wiki for Mathematical Knowledge Management 2008
June 3, 2

Research Background and Application Context Key Technology The System Use Cases and Applications Conclusion

Semantic Wiki and Ontologies
Semantic wikis found usable to support collaborative formalisation
Diﬀerence here is: deeply nested markup, lots of cross-references
Right granularity of pages: one page = one theory, one statement,
one formula?
⇒ extract knowledge relevant for search and navigation, build most
services on top of that
RDF graph in terms of an ontology that models the semantics of the
markup; direct and inferred relationships: dependency, containment

Example
A wiki page: Extracted RDF triples (= graph):
Proof proves Theorem
<omdoc> type type
<proof id="pyth-proof"
for="pythagoras"> pyth-proof proves pythagoras
...</proof> <pyth-proof, rdf:type, omdoc:Proof>
</omdoc> <pyth-proof, omdoc:proves, pythagoras>

Ch. Lange (Jacobs University Bremen) SWiM – A Semantic Wiki for Mathematical Knowledge Management 2008
June 3, 3

Research Background and Application Context Key Technology The System Use Cases and Applications Conclusion

SWiM: IkeWiki + Mathematical Markup

editing, presentation, navigation, discourse, semantic services
See http://swim.kwarc.info

Ch. Lange (Jacobs University Bremen) SWiM – A Semantic Wiki for Mathematical Knowledge Management 2008
June 3, 4

Research Background and Application Context Key Technology The System Use Cases and Applications Conclusion

Proof Formalisation and Dictionaries of Symbols

Flyspeck: Formalising a Proof of the Kepler conjecture:
hundreds of proof sketches, collaboratively transform them
into something machine-veriﬁable
formalising, annotating, discussing, project management
OpenMath 3: revision of the content dictionaries (collections of symbol
deﬁnitions)
user interface: editing formulae, metadata, symbol notations
“Let’s write multiplication as a × b instead of a · b!”

Ch. Lange (Jacobs University Bremen) SWiM – A Semantic Wiki for Mathematical Knowledge Management 2008
June 3, 5

Research Background and Application Context Key Technology The System Use Cases and Applications Conclusion

The SWiM Approach: Good for Math and other Domains

SWiM makes mathematical documents editable collaboratively and
facilitates common workﬂows by exploiting the knowledge they
contain.
Domain-speciﬁc semantic markup and ontology allows for advantages
over generic semantic wikis, and over non-semantic mathematical
wikis (more and easier knowledge management)
Approach considered transferable to other domains (e. g. chemistry):
decide on page granularity, capture semantics in ontology, extract
RDF, integrate suitable editors

Come and see the demo tonight!

Ch. Lange (Jacobs University Bremen) SWiM – A Semantic Wiki for Mathematical Knowledge Management 2008
June 3, 6

This document discusses using simulations to research emerging effects in learning networks. It outlines why simulations are useful given limitations of real learning networks. It also describes appropriate simulation frameworks, a methodology for designing simulations, and potential research focuses including exploring recommendation algorithms and measuring performance using learning theory and social network measures. Open questions are raised about how to observe and measure emergent effects, necessary statistical analysis, and integrating additional factors into simulations.

Master defence 2020 - Serhii Brodiuk - Concept Embedding and Network Analysis...

Lviv Data Science Summer School

This document presents a study analyzing the emergence of scientific innovations through concept embedding and network analysis of scientific papers. The study constructed a network of scientific concepts based on their co-occurrence in papers from the arXiv repository. Two methods were used to analyze the network - concept embedding and network analysis. These methods quantified properties of concept groups and predicted new links between concepts, revealing fields where innovations are likely to emerge. The analysis found that concept embedding and network methods can quantify the structure and dynamics of connections between scientific concepts in papers. This enables the prediction of emerging new links between concepts, representing innovations in different fields of knowledge.

OpenMinted: It's Uses and Benefits for the Social Sciences

openminted_eu

PhD Research Topics in Cloud Computing Tutorials

PhD Services

This document outlines potential PhD research topics in cloud computing. It lists topics such as migration, load balancing, and resource management as innovative mechanisms in cloud computing projects. Modern topics discussed include cloud with fog architecture, blockchain technology in clouds, and hybrid cloud technology. Major algorithms relevant to cloud computing research include decision making, deep learning, heuristics algorithms, and reinforcement algorithms. The document provides contact information for research assistance on PhD and MS projects related to these cloud computing topics.

PhD Projects in Constant Bitrate Network Research Ideas

PhD Services

IMPULSE_TECHNOLOGY

The Breakdown: What is OpenMinTeD?

openminted_eu

OpenMinTeD is an EU infrastructure project that aims to establish an open and sustainable text mining infrastructure. It will bring together accessible content, discoverable text mining services, and efficient processing capabilities. This will allow researchers to collaboratively create, discover, share and reuse knowledge extracted from a wide range of scientific text sources. The project involves 16 partners from 6 countries and will run for 3 years, starting in June 2015.

Reconciling Event-Based Knowledge through RDF2VEC

Mehwish Alam

Multi core processing of xml twig patterns +91-9994232214,8144199666, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2015-2016 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:ieeeprojectchennai@gmail.com Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support

Text Mining: the next data frontier. Beyond Open Access

openminted_eu

1) The presentation discusses the need for text and data mining (TDM) tools to make sense of the vast amount of digital data and literature being produced. It notes there are over 1.8 billion websites and 3.46 billion internet users producing large amounts of data daily. 2) Similarly, the global research community produces around 2.5 million new scholarly articles per year, but much of this work is never read or cited. 3) The presentation proposes establishing an open TDM platform called "OpenMinted" that would allow researchers to discover, share, and reuse knowledge extracted from text-based sources through the use of shared TDM services and tools.

Spatial Analysis On Histological Images Using Spark

Jen Aman

This document describes using Spark for spatial analysis of histological images to characterize the tumor microenvironment. The goal is to provide actionable data on the location and density of immune cells and blood vessels. Over 100,000 objects are annotated in each whole slide image. Spark is used to efficiently calculate over 5 trillion pairwise distances between objects within a neighborhood window. This enables profiling of co-localization and spatial clustering of objects. Initial results show the runtime scales linearly with the number of objects. Future work includes integrating clinical and genomic data to characterize variation between tumor types and patients.

E-ARK: Open Data Mining for Government Archives

Danube University Krems, Centre for E-Governance

This document discusses challenges and solutions related to digital preservation of large datasets from government archives. It describes the E-ARK project, which aims to provide better access and integration of archival storage systems with big data technologies for national archives across the EU. The project involves 16 partners including 5 archives, 4 research institutions, and 3 SMEs. It will develop and pilot test a data management application integrated with scalable storage and computation to enable long-term preservation, access, and reuse of archival data. Key challenges addressed include managing heterogeneous and increasing volumes of data and ensuring access over long periods of time.

Towards an ecosystem of data and ontologies

Mathieu d'Aquin

1) Traditional ontology research involved developing individual ontologies and annotating data with them, but now thousands of ontologies and huge amounts of data are available online. 2) This allows new opportunities for fundamental and applied research in automatically aligning data and ontologies to make sense of both, and mapping the landscape of semantics on the web. 3) Empirical studies are needed to understand ontology engineering practices by analyzing interconnected online ontologies, and how data and ontologies interact on the web.

Gist od2-feb-2011

ianibbo

This document provides an overview of the semantic web including its conceptual model and major aspects. The semantic web allows for describing things in a graph model using URIs, RDF, and SPARQL. It is good for describing unforeseen classes but not as good for tabular data or frequent updates. The conceptual model uses subject-predicate-object triples to describe resources and their relationships where the description and thing described are separate. Major aspects include the distributed graph model, RDF for descriptions, and SPARQL for retrieval.

Brecht Verstichel

Brecht Verstichel is a computational physicist with 14 peer-reviewed publications. He has extensive experience developing efficient codes to solve problems in quantum many-body physics and quantum chemistry. Currently he is a post-doctoral researcher at Ghent University, where he optimizes Tensor Network States for strongly-correlated 2D systems. Previously he held positions at Princeton University and Ghent University, developing techniques involving density matrices, tensor networks, and quantum Monte Carlo methods.

Current Issue: December 2019, Volume 8, Number 6

kevig

Aisi2017 keynote speaker

Aboul Ella Hassanien

NeXO Web Poster for ISMB 2014 BioVis SIG

Keiichiro Ono

NeXO Web is an integrated ontology visualization application for modern web platforms that provides a new way to visualize ontology data sets like the Gene Ontology and NeXO using modern web technologies. It integrates multi-omics data into a HTML5-based single page application and is open for future expansion. NeXO Web allows users to visualize ontologies, search and enrich terms, view analysis windows, ontology trees, raw interactions, and term details.

PhD Projects in Network Engineering Research Topics

PhD Services

A First Step Towards Content Protecting Plagiarism Detection

Scientific Information Analytics Group, Prof. Gipp

Ontologies in Ubiquitous Computing

Jose R. Hilera

The document discusses the use of ontologies in ubiquitous computing. It defines what an ontology is and describes ontology languages. It then presents a taxonomy for classifying ontologies used in ubiquitous computing into two main categories: ontologies of the ubiquitous computing domain and ontologies as software artifacts. Examples are given for each category including generic and specific domain ontologies as well as ontology-driven, ontology-aware, and ontology use at development time applications. The conclusion states that many works propose using ontologies in ubiquitous computing and the presented taxonomy can help organize these works.

PhD Projects in Opnet Research Guidance

PhD Services

Using Open Research Data for Public Policy Making: Opportunities of Virtual R...

Danube University Krems, Centre for E-Governance

1) The document discusses using open research data and virtual research environments to support public policy making. It analyzes requirements for a virtual research environment based on a case study of the European Plate Observing System project. 2) Key requirements for a virtual research environment include quick storage of large, multidisciplinary datasets; access to computational analysis tools; data curation and cataloguing; and linking of multiple research infrastructures. 3) An example use case involves an Italian ministry using earth science data along with open government data on buildings and residents for policy decisions around earth displacements.

Technology_solution

Data Science Solutions by Materials Scientists: The Early Case Studies

Tony Fast

Improvements in algorithms, technology, and computation are directly impacting the landscape of information use in materials science. The 3 V’s of Big Data (volume, velocity, and variety) are becoming evermore apparent within all sectors of the field. Novel approaches will be required to confront the emerging data deluge and extract the richest knowledge from simulated and empirical information in complex evolving 3-D spaces. Microstructure Informatics (μInformatics) is an emerging suite of signal processing techniques, advanced statistical tools, and data science methods tailored specifically for this new frontier. μInformatics curates and transforms large collections of materials science information using efficient workflows to extract knowledge of bi-directional structure-property/processing connections for most material classes. In this talk, a few early case studies in data-driven methods to solve materials science problems will be explored. Emerging spatial statistics tools will be explored that enable an objective comparison of static and evolving 3-D material volumes from molecular dynamics simulation, micro-CT, and Scanning Electron Microscopy. Also, the statistics will provide a foundation to create improved bottom-up homogenization relationships in fuel cell materials. Lastly, applications of the Materials Knowledge System, a data-driven meta-model to create top-down localization relationships will be explored for phase field model and finite element model information.

Eira presentation

Pontus Thome

This document describes EIRA, a system for analyzing research impact that consists of four main components: an Analyzer that extracts annotations from research funding applications, a Finder that retrieves and organizes related data, a Ranker that ranks entities based on calculated values and fields, and a Viewer graphical user interface. The system is implemented using modular applications on IBM's Bluemix cloud platform, with the Analyzer deployed as a cognitive computing service to extract important information from research documents.

A Mathematical Approach to Ontology Authoring and Documentation

Christoph Lange

This document proposes using OMDoc, a framework for representing formal knowledge, to improve ontology authoring and documentation. It describes how OMDoc can: 1) Provide better support for modularity, documentation at different granularities, and linking documentation to formal representations compared to languages like OWL. 2) Model existing ontologies and translate between OMDoc and OWL/RDF formats to leverage existing tools. 3) Allow comprehensive, integrated documentation of ontologies through features like literate programming. The approach is evaluated by reimplementing the FOAF ontology in OMDoc.

TNTBase – a Versioned Database for XML (Mathematical) Documents

Christoph Lange

The Distributed Ontology Language (DOL): Use Cases, Syntax, and Extensibility

Christoph 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.

What's hot

Data compression with Python: application of different algorithms with the us...

Alex Camargo

Multi core processing of xml twig patterns

ieeepondy

Text Mining: the next data frontier. Beyond Open Access

openminted_eu

Spatial Analysis On Histological Images Using Spark

Jen Aman

E-ARK: Open Data Mining for Government Archives

Danube University Krems, Centre for E-Governance

Towards an ecosystem of data and ontologies

Mathieu d'Aquin

Gist od2-feb-2011

ianibbo

Brecht Verstichel

Current Issue: December 2019, Volume 8, Number 6

kevig

Aisi2017 keynote speaker

Aboul Ella Hassanien

NeXO Web Poster for ISMB 2014 BioVis SIG

Keiichiro Ono

PhD Projects in Network Engineering Research Topics

PhD Services

A First Step Towards Content Protecting Plagiarism Detection

Scientific Information Analytics Group, Prof. Gipp

Ontologies in Ubiquitous Computing

Jose R. Hilera

PhD Projects in Opnet Research Guidance

PhD Services

Using Open Research Data for Public Policy Making: Opportunities of Virtual R...

Danube University Krems, Centre for E-Governance

Technology_solution

Data Science Solutions by Materials Scientists: The Early Case Studies

Tony Fast

Eira presentation

Pontus Thome

What's hot (19)

Data compression with Python: application of different algorithms with the us...

Multi core processing of xml twig patterns

Text Mining: the next data frontier. Beyond Open Access

Spatial Analysis On Histological Images Using Spark

E-ARK: Open Data Mining for Government Archives

Towards an ecosystem of data and ontologies

Gist od2-feb-2011

Current Issue: December 2019, Volume 8, Number 6

Aisi2017 keynote speaker

NeXO Web Poster for ISMB 2014 BioVis SIG

PhD Projects in Network Engineering Research Topics

A First Step Towards Content Protecting Plagiarism Detection

Ontologies in Ubiquitous Computing

PhD Projects in Opnet Research Guidance

Using Open Research Data for Public Policy Making: Opportunities of Virtual R...

Data Science Solutions by Materials Scientists: The Early Case Studies

Eira presentation

Viewers also liked

A Mathematical Approach to Ontology Authoring and Documentation

Christoph Lange

TNTBase – a Versioned Database for XML (Mathematical) Documents

Christoph Lange

The Distributed Ontology Language (DOL): Use Cases, Syntax, and Extensibility

Christoph Lange

Linked Open (Geo)Data and the Distributed Ontology Language – a perfect match

Christoph 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).

WDAqua ITN – Answering Questions using Web Data

Christoph Lange

OSCOSS: Opening Scholarly Communication in Social Sciences

Christoph Lange

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.

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

Viewers also liked (8)

A Mathematical Approach to Ontology Authoring and Documentation

TNTBase – a Versioned Database for XML (Mathematical) Documents

The Distributed Ontology Language (DOL): Use Cases, Syntax, and Extensibility

Linked Open (Geo)Data and the Distributed Ontology Language – a perfect match

WDAqua ITN – Answering Questions using Web Data

OSCOSS: Opening Scholarly Communication in Social Sciences

Ontology Integration and Interoperability (OntoIOp) – Part 1: The Distributed...

Interlinking Data and Knowledge in Enterprises, Research and Society with Lin...

Similar to SWiM – A Semantic Wiki for Mathematical Knowledge Management

SWiM – A wiki for collaborating on mathematical ontologies

Christoph Lange

SWiM is a semantic wiki for collaborating on mathematical ontologies. It combines a wiki interface with semantic markup languages like MathML and OMDoc to support collaborative authoring of mathematical knowledge. Key features include dedicated editors for mathematical formulas and structures, an argumentation ontology for discussion, and extraction of semantic relationships into an RDF graph using a mathematical ontology. The system is being used for projects like revising the OpenMath content dictionaries and formalizing a large mathematical proof.

Mathematical Semantic Markup in a Wiki: the Roles of Symbols and Notations

Christoph Lange

Towards Scientific Collaboration in a Semantic Wiki

Christoph Lange

This document discusses developing a semantic wiki for scientific collaboration called SWiM. It aims to bridge the gap between traditional wikis and the semantic web by incorporating formal knowledge representation and enabling enhanced services. The document outlines SWiM's use of semantic markup languages to represent scientific knowledge, extraction of an ontology from this representation, and potential services like learning assistance and dependency management that could be built on top. It concludes by discussing implementing and evaluating prototypes of these services in scientific and educational case studies.

Semantic Technologies for Big Sciences including Astrophysics

Artificial Intelligence Institute at UofSC

Amit Sheth with TK Prasad, "Semantic Technologies for Big Science and Astrophysics", Invited Plenary Presentation, at Earthcube Solar-Terrestrial End-User Workshop, NJIT, Newark, NJ, August 13, 2014. Like many other fields of Big Science, Astrophysics and Solar Physics deal with the challenges of Big Data, including Volume, Variety, Velocity, and Veracity. There is already significant work on handling volume related challenges, including the use of high performance computing. In this talk, we will mainly focus on other challenges from the perspective of collaborative sharing and reuse of broad variety of data created by multiple stakeholders, large and small, along with tools that offer semantic variants of search, browsing, integration and discovery capabilities. We will borrow examples of tools and capabilities from state of the art work in supporting physicists (including astrophysicists) [1], life sciences [2], material sciences [3], and describe the role of semantics and semantic technologies that make these capabilities possible or easier to realize. This applied and practice oriented talk will complement more vision oriented counterparts [4]. [1] Science Web-based Interactive Semantic Environment: http://sciencewise.info/ [2] NCBO Bioportal: http://bioportal.bioontology.org/ , Kno.e.sis’s work on Semantic Web for Healthcare and Life Sciences: http://knoesis.org/amit/hcls [3] MaterialWays (a Materials Genome Initiative related project): http://wiki.knoesis.org/index.php/MaterialWays [4] From Big Data to Smart Data: http://wiki.knoesis.org/index.php/Smart_Data

Towards a Semantic Wiki for Science

Christoph Lange

Knowledge Representation on the Web

Rinke Hoekstra

This document outlines a course on Knowledge Representation (KR) on the Web. The course aims to expose students to challenges of applying traditional KR techniques to the scale and heterogeneity of data on the Web. Students will learn about representing Web data through formal knowledge graphs and ontologies, integrating and reasoning over distributed datasets, and how characteristics such as volume, variety and veracity impact KR approaches. The course involves lectures, literature reviews, and milestone projects where students publish papers on building semantic systems, modeling Web data, ontology matching, and reasoning over large knowledge graphs.

Semantic Text Processing Powered by Wikipedia

Maxim Grinev

The document discusses using Wikipedia as a resource for semantic text processing and natural language processing techniques. It describes using Wikipedia's comprehensive coverage of terms, rich structure of links and categories, and ability to be continuously updated to power text analysis algorithms. These include word sense disambiguation, keyword extraction, topic inference, ontology management, semantic search, and improved recommendations. The techniques analyze Wikipedia's link structure and build semantic graphs of documents to discover related concepts and group keywords.

Semantic Web, Ontology, and Ontology Learning: Introduction

Kent State University

(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.

Project Proposal Topics Modeling (Ir)

Svitlana volkova

This document proposes using Wikipedia concepts to improve topic modeling. It discusses using n-grams like bigrams and phrases from Wikipedia categories rather than only individual words. The goal is to develop a topic model that associates words and Wikipedia concepts with mixtures of topics. Important steps include collecting a dataset, preprocessing it, performing topic modeling using an LDA model that incorporates Wikipedia concepts, and evaluating the results against models using only unigrams or n-grams. Key benefits noted are the ability to represent topics with more representative concepts and reduce ambiguity compared to models using only individual words.

Flyspeck in a Semantic Wiki – Collaborating on a Large Scale Formalization of...

Christoph Lange

1) The document discusses using a semantic wiki to support the Flyspeck project, which aims to formally verify Thomas Hales' proof of the Kepler Conjecture. 2) Two semantic wikis, Semantic MediaWiki and SWiM, were evaluated for their ability to represent the formalized mathematics, annotate concepts, and enable collaboration on the large proof project. 3) While both showed promise, SWiM was found to better support the formal ontology needed and enable more powerful querying of the mathematical concepts and lemmas. However, more work is needed to fully realize SWiM's capabilities for the Flyspeck project.

Model of semantic textual document clustering

SK Ahammad Fahad

Now the age of information technology, textual document is spontaneously increasing over the internet, e-mail, b pages, offline and online reports, journals, articles and they stored in the electronic database format. Millions of new text file created in a day, but for the proper classification, people miss vast information those are useful to several challenges in daily life. To maintain and access those documents are very difficult without adequate rating and when there has classification without any information provide call clustering. To overcome such difficulties K-means and others old clustering algorithms are unfit to impart as may be expected on Natural languages. Because of high-dimensional about texts, the presence of logical structure clues within the texts and novel segmentation techniques have taken advantage of advances in generative topic modeling algorithms, specifically designed to spot questions at intervals text to cipher word–topic distributions. By considering those challenges there, in the current thesis proposed a semantic document clustering framework and the framework be developed by using Python platform and tested each of steps. In this context there have preprocessing steps like tag elimination, removed stop words according to Oxford dictionary, applying lemmatization process after getting the help of WordNet semantic information available and synsets for each word individually from raw text. So considering the limitation of K-Means algorithm and other old algorithms, COBB conceptual clustering algorithm applied to the preprocessed data in this context. Clusters quality and accuracy is one of the most significant contributions to this research. For ensuring the accuracy of clusters, the f-measure accuracy measuring methods selected for evaluate the clusters and feedback the accuracy of clusters. F-Measure returns the accuracy of clusters and also ensuring the purity of clustering process. Framework tests on 20 samples of 20 different articles and minimum accuracy considered as the accuracy of the clusters and the developed system return 71.42% accurate. There are several challenges, such as synonym, high dimensionality, extracting core semantics from texts, and assigning appropriate description for the generated clusters need to experiment further. This research to work to find an accurate way to cluster text documents based on semantic meaning by the help of WordNet database.

Ck32985989

IJMER

This document presents an approach for extracting ontologies from heterogeneous documents. It discusses how ontologies play an important role in the semantic web for knowledge management and interoperability. The authors describe a clustering algorithm that identifies concepts and relationships by processing sentences from input documents. Key steps include marking the first word of each sentence as a parent concept and subsequent words as child concepts. They also describe a harmonization process to integrate extracted ontologies with existing knowledge bases by matching and merging corresponding concepts and relations. The authors applied their approach to documents in text, document and PDF formats, and were able to extract concept hierarchies and relationships from the input files.

towards interoperable archives: the Universal Preprint Service initiative

Herbert Van de Sompel

Semantic Web: Technolgies and Applications for Real-World

Amit Sheth

IMPACT Final Event 26-06-2012 - Franciska de Jong - Indexing and searching of...

IMPACT Centre of Competence

This document discusses indexing and searching noisy data. Part I provides examples of noisy data from spelling corrections, speech recognition, machine translation and optical character recognition where information is lost. Part II discusses emerging scenarios for scholarly use of noisy metadata including discovery and exploration of large digital collections. Part III argues that metadata mining can enhance accessibility and understanding by integrating different annotation types from manual, automatic and community sources.

Knowledge/Argument/Dialogue Mapping and Social Software

Simon Buckingham Shum

The document discusses knowledge mapping and social software tools that can be used to support sensemaking, knowledge sharing, and collective dialogue. It provides examples of tools such as Compendium that allow users to create and link different knowledge elements, and how such tools have been applied in contexts like capturing scientific collaborations and emergency response planning. The document concludes by suggesting potential applications of knowledge mapping tools and resources for learning more.

The Revolution Of Cloud Computing

Carmen Sanborn

Here are the key points about using content-based filtering techniques: - Content-based filtering relies on analyzing the content or description of items to recommend items similar to what the user has liked in the past. It looks for patterns and regularities in item attributes/descriptions to distinguish highly rated items. - The item content/descriptions are analyzed automatically by extracting information from sources like web pages, or entered manually from product databases. - It focuses on objective attributes about items that can be extracted algorithmically, like text analysis of documents. - However, personal preferences and what makes an item appealing are often subjective qualities not easily extracted algorithmically, like writing style or taste. - So while content-based filtering can

Discovery Engines for Big Data: Accelerating Discovery in Basic Energy Sciences

Ian Foster

Argonne’s Discovery Engines for Big Data project is working to enable new research modalities based on the integration of advanced computing with experiments at facilities such as the Advanced Photon Source (APS). I review science drivers and initial results in diffuse scattering, high energy diffraction microscopy, tomography, and pythography. I also describe the computational methods and infrastructure that we leverage to support such applications, which include the Petrel online data store, ALCF supercomputers, Globus research data management services, and Swift parallel scripting. This work points to a future in which tight integration of DOE’s experimental and computational facilities enables both new science and more efficient and rapid discovery.

A SEMANTIC METADATA ENRICHMENT SOFTWARE ECOSYSTEM BASED ON TOPIC METADATA ENR...

IJDKP

As existing computer search engines struggle to understand the meaning of natural language, semantically enriched metadata may improve interest-based search engine capabilities and user satisfaction. This paper presents an enhanced version of the ecosystem focusing on semantic topic metadata detection and enrichments. It is based on a previous paper, a semantic metadata enrichment software ecosystem (SMESE). Through text analysis approaches for topic detection and metadata enrichments this paper propose an algorithm to enhance search engines capabilities and consequently help users finding content according to their interests. It presents the design, implementation and evaluation of SATD (Scalable Annotation-based Topic Detection) model and algorithm using metadata from the web, linked open data, concordance rules, and bibliographic record authorities. It includes a prototype of a semantic engine using keyword extraction, classification and concept extraction that allows generating semantic topics by text, and multimedia document analysis using the proposed SATD model and algorithm. The performance of the proposed ecosystem is evaluated using a number of prototype simulations by comparing them to existing enriched metadata techniques (e.g., AlchemyAPI, DBpedia, Wikimeta, Bitext, AIDA, TextRazor). It was noted that SATD algorithm supports more attributes than other algorithms. The results show that the enhanced platform and its algorithm enable greater understanding of documents related to user interests.

Supervised Approach to Extract Sentiments from Unstructured Text

International Journal of Engineering Inventions www.ijeijournal.com

Similar to SWiM – A Semantic Wiki for Mathematical Knowledge Management (20)

SWiM – A wiki for collaborating on mathematical ontologies

Mathematical Semantic Markup in a Wiki: the Roles of Symbols and Notations

Towards Scientific Collaboration in a Semantic Wiki

Semantic Technologies for Big Sciences including Astrophysics

Towards a Semantic Wiki for Science

Knowledge Representation on the Web

Semantic Text Processing Powered by Wikipedia

Semantic Web, Ontology, and Ontology Learning: Introduction

Project Proposal Topics Modeling (Ir)

Flyspeck in a Semantic Wiki – Collaborating on a Large Scale Formalization of...

Model of semantic textual document clustering

Ck32985989

towards interoperable archives: the Universal Preprint Service initiative

Semantic Web: Technolgies and Applications for Real-World

IMPACT Final Event 26-06-2012 - Franciska de Jong - Indexing and searching of...

Knowledge/Argument/Dialogue Mapping and Social Software

The Revolution Of Cloud Computing

Discovery Engines for Big Data: Accelerating Discovery in Basic Energy Sciences

A SEMANTIC METADATA ENRICHMENT SOFTWARE ECOSYSTEM BASED ON TOPIC METADATA ENR...

Supervised Approach to Extract Sentiments from Unstructured Text

More from Christoph Lange

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!

Research Careers in Applied Computer Science

Christoph Lange

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.

Linking Big Data to Rich Process Descriptions

Christoph 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.

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 Social

Christoph 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.

TCP – zuverlässiger Ende-zu-Ende-DatenstromChristoph Lange

Making Heterogeneous Ontologies Interoperable Through Standardisation

Christoph 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 Database

Christoph 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.

JOBAD – Interactive Mathematical Documents

Christoph Lange

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.

Publishing Math Lecture Notes as Linked Data

Christoph Lange

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 Data

Christoph 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

Mathematical Semantics of Statistical Data

Christoph Lange

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.

Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web I...

Christoph Lange

Ontology Integration and Interoperability (OntoIOp) – Part 1: The Distributed...

Christoph Lange

The document discusses the Distributed Ontology Language (DOL), a proposed standard being developed by ISO for expressing heterogeneous ontologies and links between ontologies. DOL aims to achieve semantic integration and interoperability across knowledge representations. It will have a formal semantics and support multiple serialization formats. The standard is being developed to facilitate communication and reduce complexity for applications involving multiple ontologies.

Processing and Publishing Content Math with JOMDoc and JOBAD

Christoph Lange

JOMDoc and JOBAD are libraries for processing and publishing content math. JOMDoc provides Java tools for working with OMDoc documents, including validation, rendering, and restructuring. JOBAD is a JavaScript API that makes OMDoc documents interactive by enabling features like customization, information lookup, rewriting, and discussion forums. Together JOMDoc and JOBAD allow integration of services like notation definitions, rendering, and external data into interactive math documents.

wiki.openmath.org – how it works, how you can participate

Christoph Lange

This document discusses the OpenMath wiki located at wiki.openmath.org. It describes how the wiki works and how users can participate. Specifically, it outlines how the wiki is used to author, review, and discuss OpenMath content dictionaries (CDs). It also evaluates how users are interacting with the wiki and considers future directions. The document provides examples of how minor edits, discussions around revisions, and editing symbol notations can be carried out on the wiki.

Web-2.0-Forschung der KWARC-Gruppe

Christoph Lange

Aspects of Mathematical Knowledge on the Semantic Web

Christoph Lange

Christoph Lange is a PhD student studying mathematical knowledge management using semantic web technologies. He developed a semantic wiki called SWiM for collaboratively structuring mathematical knowledge. His work included developing ontologies for modeling the structure of mathematical documents, creating user interfaces for annotating and browsing knowledge, and implementing domain-specific argumentation in wikis. He hopes his research on applying these techniques to the complex domain of mathematics can help advance semantic web technologies and knowledge management.

More from Christoph Lange (20)

Faire Datenökonomie für Wirtschaft, Wissenschaft und Gesellschaft: Was brauch...

Research Careers in Applied Computer Science

Machine Support for Interacting with Scientific Publications Improving Inform...

Linking Big Data to Rich Process Descriptions

Bringing Mathematics To the Web of Data: the Case of the Mathematics Subject ...

Semantic Web Technology: The Key to Making Scientific Information Systems Social

TCP – zuverlässiger Ende-zu-Ende-Datenstrom

Making Heterogeneous Ontologies Interoperable Through Standardisation

Previewing OWL Changes and Refactorings Using a Flexible XML Database

JOBAD – Interactive Mathematical Documents

Publishing Math Lecture Notes as Linked Data

sTeX+ – a System for Flexible Formalization of Linked Data

Krextor – An Extensible Framework for Contributing Content Math to the Web of...

Mathematical Semantics of Statistical Data

Enabling Collaboration on Semiformal Mathematical Knowledge by Semantic Web I...

Ontology Integration and Interoperability (OntoIOp) – Part 1: The Distributed...

Processing and Publishing Content Math with JOMDoc and JOBAD

wiki.openmath.org – how it works, how you can participate

Web-2.0-Forschung der KWARC-Gruppe

Aspects of Mathematical Knowledge on the Semantic Web

Recently uploaded

The Microsoft 365 Migration Tutorial For Beginner.pptx

operationspcvita

Building Production Ready Search Pipelines with Spark and Milvus

Zilliz

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

DianaGray10

Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more. The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications. We’ll discuss and demo the benefits of UiPath Apps and connectors including: Creating a compelling user experience for any software, without the limitations of APIs. Accelerating the app creation process, saving time and effort Enjoying high-performance CRUD (create, read, update, delete) operations, for seamless data management. Speakers: Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP Charlie Greenberg, host

Main news related to the CCS TSI 2023 (2023/1695)

Jakub Marek

An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers. The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 . The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .

Choosing The Best AWS Service For Your Website + API.pptx

Brandon Minnick, MBA

Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API? Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose? Which one is cheapest? Which one is fastest? Which one will scale to meet our needs? Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!

Presentation of the OECD Artificial Intelligence Review of Germany

innovationoecd

Dandelion Hashtable: beyond billion requests per second on a commodity server

Antonios Katsarakis

This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

Neo4j

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

Jason Yip

The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

Alex Pruden

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security. Paper Link: https://eprint.iacr.org/2024/257

Your One-Stop Shop for Python Success: Top 10 US Python Development Providers

akankshawande

Driving Business Innovation: Latest Generative AI Advancements & Success Story

Safe Software

Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency. During the hour, we’ll take you through: Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board. Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes. Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI. We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI. This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/ Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit. The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers. Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.

5th LF Energy Power Grid Model Meet-up Slides

DanBrown980551

5th Power Grid Model Meet-up It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology. Power Grid Model The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services. Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability. Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization. What to expect For the upcoming meetup we are organizing, we have an exciting lineup of activities planned: -Insightful presentations covering two practical applications of the Power Grid Model. -An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024. -An interactive brainstorming session to discuss and propose new feature requests. -An opportunity to connect with fellow Power Grid Model enthusiasts and users.

GraphRAG for Life Science to increase LLM accuracy

Tomaz Bratanic

Y-Combinator seed pitch deck template PP

c5vrf27qcz

Fueling AI with Great Data with Airbyte Webinar

Zilliz

Astute Business Solutions | Oracle Cloud Partner |

AstuteBusiness

Taking AI to the Next Level in Manufacturing.pdf

ssuserfac0301

Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as: 1. How quickly AI is being implemented in manufacturing. 2. Which barriers stand in the way of AI adoption. 3. How data quality and governance form the backbone of AI. 4. Organizational processes and structures that may inhibit effective AI adoption. 6. Ideas and approaches to help build your organization's AI strategy.

What is an RPA CoE? Session 1 – CoE Vision

DianaGray10

Recently uploaded (20)