This document describes a dependency parsing-based question answering system that uses RDF and SPARQL. It parses natural language facts and questions into typed dependencies, translates them into RDF and SPARQL, queries populated RDF data to answer questions, and incorporates WordNet and DBpedia background knowledge. The system handles negation, tenses, passive voice and provides examples of its question answering capabilities.
RDF is a framework for describing resources on the web using triples of the form (subject, predicate, object). It allows metadata to be added to resources, enabling applications to publish and link open data on the web. RDF descriptions can be seen as a graph with resources as nodes and properties as edges between them. RDF has XML and other syntaxes and provides primitives for building containers, collections, and structured values.
Combining Social Music and Semantic Web for music-related recommender systemsAlexandre Passant
This document discusses combining social music data and the semantic web for music recommendation systems. It outlines how social music data from services like Last.fm can be modeled and interconnected using ontologies like FOAF, SIOC and MOAT. This unified semantic social music data can then be used for music recommendations by exploring relationships between artists, genres, social connections and tagged content. Examples of recommendation approaches are provided that leverage different aspects of the semantic social music graph.
The document discusses RDF Shapes, which are used to describe and validate RDF data. It provides examples of using ShEx and SHACL to define shapes for RDF graphs and validate instance data against those shapes. Key points covered include the differences between ShEx and SHACL, such as ShEx focusing on defining structures while SHACL adds target declarations, and how both can be used to generate validation reports.
The document discusses ontologies and the RDF-S/OWL languages for defining ontologies. It defines ontologies as formal, explicit specifications of shared conceptualizations and describes some key parts of ontologies including concepts, relations, instances, and axioms. It provides an example ontology about artists and the works they create. RDF-S semantics are discussed for defining subclasses, subproperties, domains, and ranges within an ontology.
The document discusses the Semantic Web, including its languages (RDF, RDFS, OWL, Turtle), storage and querying (SPARQL), integration (RDFa, GRDDL), browsing and visualization (Fresnel lenses), and mashups (Yahoo Pipes). It also covers reasoning with OWL and potential applications like legal assessment. While the technologies exist, broader adoption on the web is still developing.
The document discusses representing data in the Resource Description Framework (RDF). It describes how relational data can be represented as RDF triples with rows becoming subjects, columns becoming properties, and values becoming objects. It also discusses using URIs instead of internal IDs and names to allow data integration. The document then covers serializing RDF data in different formats like RDF/XML, N-Triples, N3, and Turtle and describes syntax for representing literals, language tags, and abbreviating subject and predicate pairs.
SPARQL is a standard query language for RDF that has undergone two iterations (1.0 and 1.1) through the W3C process. SPARQL 1.1 includes updates to RDF stores, subqueries, aggregation, property paths, negation, and remote querying. It also defines separate specifications for querying, updating, protocols, graph store protocols, and federated querying. Apache Jena provides implementations of SPARQL 1.1 and tools like Fuseki for deploying SPARQL servers.
This document discusses RDF and SPARQL. It provides an introduction to RDF, including the basic RDF data model of subject-predicate-object triples. It then discusses SPARQL, the query language for retrieving and manipulating RDF data, including basic SPARQL syntax examples. It also briefly mentions the SPARQL protocol for accessing RDF data via HTTP endpoints.
This document discusses the need for named graphs in RDF to represent contextual information like provenance and source of RDF data. It proposes extensions to the RDF/XML syntax to associate RDF descriptions and statements with named graphs. This allows modeling things like different hypotheses, temporal aspects, points of view, and distributed storage in a way that is currently not possible without named graphs in the RDF model.
Data deduplication, or entity resolution, is a common problem for anyone working with data, especially public data sets. Many real world datasets do not contain unique IDs, instead, we often use a combination of fields to identify unique entities across records by linking and grouping. This talk will show how we can use active learning techniques to train learnable similarity functions that outperform standard similarity metrics (such as edit or cosine distance) for deduplicating data in a graph database. Further, we show how these techniques can be enhanced by inspecting the structure of the graph to inform the linking and grouping processes. We will demonstrate how to use open source tools to perform entity resolution on a dataset of campaign finance contributions loaded into the Neo4j graph database.
The forth lecture of the course I'm giving on "Interoperability and Semantic Technologies" at Politecnico di Milano in the academic year 2015-16. It presents an introduction to RDF. It starts presenting the data model. Then it presents the turtle serialization. It compares XML vs. RDF. Finally, it provides few informations about RDFa and Linked Data.
This document provides an introduction to XSPARQL, a language for transforming between RDF and XML. It discusses how transformations between RDF and XML can be challenging due to different syntaxes and serializations used to represent the same RDF graph. It notes that while SPARQL is good for querying RDF, it does not provide a way to produce arbitrary XML output. The document then introduces XSPARQL as a transformation language that combines XML, RDF, XQuery and SPARQL to allow lifting and lowering between XML and RDF formats in a single language.
Although RDF is a corner stone of semantic web and knowledge graphs, it has not been embraced by everyday programmers and software architects who need to safely create and access well-structured data. There is a lack of common tools and methodologies that are available in more conventional settings to improve data quality by defining schemas that can later be validated. Two technologies have recently been proposed for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). In the talk, we will review the history and motivation of both technologies. We will also and enumerate some challenges and future work with regards to RDF validation.
- SPARQL is a query language for retrieving and manipulating data stored in RDF format. It is similar to SQL but for RDF data.
- SPARQL queries contain prefix declarations, specify a dataset using FROM, and include a graph pattern in the WHERE clause to match triples.
- The main types of SPARQL queries are SELECT, ASK, DESCRIBE, and CONSTRUCT. SELECT returns variable bindings, ASK returns a boolean, DESCRIBE returns a description of a resource, and CONSTRUCT generates an RDF graph.
SPARQL 1.1 introduced several new features including:
- Updated versions of the SPARQL Query and Protocol specifications
- A SPARQL Update language for modifying RDF graphs
- A protocol for managing RDF graphs over HTTP
- Service descriptions for describing SPARQL endpoints
- Basic federated query capabilities
- Other minor features and extensions
This document provides an overview of describing web resources using the Resource Description Framework (RDF). It discusses the basic concepts of RDF including resources, properties, statements, and the XML syntax used to represent them. It also covers RDF Schema which adds vocabulary for describing properties and classes of RDF resources, and provides a critical view of some aspects of RDF such as its use of binary predicates and treatment of properties.
The document discusses the Semantic Web and Resource Description Framework (RDF). It defines the Semantic Web as making web data machine-understandable by describing web resources with metadata. RDF uses triples to describe resources, properties, and relationships. RDF data can be visualized as a graph and serialized in formats like RDF/XML. RDF Schema (RDFS) provides a basic vocabulary for defining classes, properties, and hierarchies to enable reasoning about RDF data.
Dependency parsing analyzes the syntactic structure of sentences by determining how each word depends on or is modified by other words through grammatical relationships. It parses sentences on a word-by-word basis rather than analyzing constituent phrases. Tools like WEKA can be used to generate feature files and build decision tree models to predict syntactic dependencies between words.
Qedia - Natural Language Queries on DBPedialucianb
This document describes an application that allows users to query the DBPedia knowledge base using natural language queries rather than SPARQL queries. It presents the architecture of the application, including how it parses natural language phrases into subject-predicate-object triplets and then generates the corresponding SPARQL queries to run on DBPedia. It also describes the types of natural language queries it can handle and discusses potential areas of improvement.
This document discusses managing and reasoning with completeness information for RDF data sources. It presents two tools for completeness management: CORNER, a generic completeness reasoner, and COOL-WD, a tool tailored for Wikidata. It also describes techniques for optimizing completeness reasoning, including data-aware reasoning, time-aware reasoning, constant-relevance, completeness templates, and partial matching. The goal is to develop flexible completeness reasoning methods that scale to real-world datasets.
NLIDB(Natural Language Interface to DataBases)Swetha Pallati
The document discusses natural language interfaces to databases (NLIDB). It describes the purpose of NLIDB as allowing users to compose queries in natural language rather than SQL. The architecture of an NLIDB system includes components like a lexicon, tokenizer, matcher, query generator, and equivalence checker. NLIDB systems have advantages like being more user-friendly than SQL, but also have disadvantages like not having perfect linguistic coverage.
The document discusses the use of deep neural networks and text mining. It provides an overview of key developments in deep learning for natural language processing, including word embeddings using Word2Vec, convolutional neural networks for modeling sentences and documents, and applications such as machine translation, relation classification and topic modeling. The document also discusses parameter tuning for deep learning models.
Querying your database in natural language by Daniel Moisset PyData SV 2014PyData
Most end users can't write a database query, and yet, they often have the need to access information that keyword-based searches can't retrieve precisely. Lately, there's been an explosion of proprietary Natural Language Interfaces to knowledge databases, like Siri, Google Now and Wolfram Alpha. On the open side, huge knowledge bases like DBpedia and Freebase exists, but access to them is typically limited to using formal database query languages. We implemented Quepy as an approach to provide a solution for this problem. Quepy is an open source framework to transform Natural Language questions into semantic database queries that can be used with popular knowledge databases like, for example, DBPedia and Freebase. So instead of requiring end users to learn to write some query language, a Quepy Application can fills the gap, allowing end users to make their queries in "plain English". In this talk we would discuss the techniques used in Quepy, what additional work can be done, and its limitations.
A talk about the gap between theory and practice with W3C Semantic Web and Dublin Core standards, and how the DC Tools Community can help collectively reduce the cost of that gap.
Given as part of the DC Tools Community workshop at LIDA2009 in Zadar, Croatia.
Lecture at the advanced course on Data Science of the SIKS research school, May 20, 2016, Vught, The Netherlands.
Contents
-Why do we create Linked Open Data? Example questions from the Humanities and Social Sciences
-Introduction into Linked Open Data
-Lessons learned about the creation of Linked Open Data (link discovery, knowledge representation, evaluation).
-Accessing Linked Open Data
SPARQL is a query language for retrieving and manipulating data stored in RDF format. It allows users to write queries against remote SPARQL endpoints to query RDF triples stored in a database. SPARQL queries are composed of triple patterns, similar to RDF triples, that can include variables to retrieve variable bindings from the queried data. Query results are returned as solutions that assign values to the variables. Common queries include SELECT, ASK, CONSTRUCT, and DESCRIBE. SPARQL endpoints provide programmatic access to issue SPARQL queries against remote SPARQL-accessible stores.
The document discusses the principles and benefits of linked open data (LOD) in the culture sector. It describes several cultural heritage organizations that publish linked data, including Europeana, the Collections Trust, the Science Museum, and Semuse. It then lists 10 principles for implementing linked data in the culture sector, such as making data rich and connected, helping achieve efficient practice and public access, and becoming an embedded function of cultural organization software. Finally, it provides examples of linked data technologies like content negotiation, SPARQL querying, and RDF stores.
FHIR can be represented in RDF format. Resources are serialized as directed graphs using URIs, properties, and values. FHIR defines a metadata vocabulary for use in RDF, and a FHIR resource catalog provides the URIs for standard FHIR resources and properties. Shape expressions (ShEx) schemas validate FHIR RDF according to resource definitions. Together, these components allow FHIR data to be queried and manipulated using RDF techniques while maintaining compatibility with the JSON format. Tools exist for converting between FHIR JSON and RDF formats.
This is a regular query asking for the abstract of the Semantic Web resource from DBPedia. The query returns the English abstract which describes the Semantic Web as a collaborative movement led by the W3C that promotes common data formats on the World Wide Web in order to convert it from unstructured documents to a web of shared and reusable data across applications through the use of semantic annotations.
This document discusses adding semantic structure to real-time social data from Twitter through Twitter Annotations. It describes how Annotations can be mapped to existing Semantic Web vocabularies and linked to datasets to enable real-time semantic search over social and linked data. A system called TwitLogic is presented that captures Twitter data, converts it to RDF, and publishes it as linked streams to allow for continuous querying and integration with the live Semantic Web.
Knowledge Technologies: Opportunities and ChallengesFariz Darari
How to be one step ahead of leveraging knowledge technologies for your apps!
When: Dec 8, 2017
Where: Fl. 6, Multimedia Tower, Central Jakarta
Thanks to Ragil for the invitation!
This document outlines activities for a workshop on BIBFRAME, RDF, and SPARQL. Activity 1 involves writing RDF triples and creating an RDF graph using book examples. Activity 2 focuses on using BIBFRAME tools like the editor, transformation service, and comparison service. Activity 3 demonstrates using SPARQL to query the DBPedia dataset, including writing simple queries and queries for specific resources like Rome that filter by language.
Semantic Web: A web that is not the WebBruce Esrig
1. The document discusses the Semantic Web and semantics, describing semantics as constructing formal explanations for how things work and modeling concepts and relationships.
2. It provides examples of semantic modeling using FOAF (Friend of a Friend) to represent relationships between people.
3. Semantic technologies can help websites expose and organize data about content and enable new ways for users to access information based on concepts.
Semantic Integration with Apache Jena and StanbolAll Things Open
The document provides an overview of semantic integration using Apache Jena and Apache Stanbol. It discusses using semantic web technologies like RDF, ontologies, and vocabularies to integrate data from various sources and allow machines and people to better understand and use the integrated information. It also provides technical details on Apache Jena, which can store and query RDF data, and Apache Stanbol, a semantic processing engine that can enhance content with metadata.
2011 4IZ440 Semantic Web – RDF, SPARQL, and software APIsJosef Petrák
The document discusses the Semantic Web and RDF data formats. It provides an overview of RDF syntaxes like RDF/XML, N3, N-Triples, RDF/JSON, and RDFa. It also discusses software APIs for working with RDF data in languages like Java, PHP, and Ruby. The document outlines handling RDF data using statement-centric, resource-centric, and ontology-centric models, as well as named graphs. It provides examples of reading RDF data from files and querying RDF data using SPARQL.
Bernhard Haslhofer is a postdoc researcher at Cornell University studying linked data, user-contributed data, and data interoperability. He discusses Linked (Open) Data, which uses URIs and RDF to publish and link structured data on the web. The key principles are using URIs to identify things, providing useful information about those URIs when dereferenced, and including links to other URIs. Enabling technologies include URIs, RDF, RDFS/OWL for vocabularies, SPARQL for querying, and best practices for publishing vocabularies and data. Useful tools are also presented.
The document discusses using RDFS and OWL reasoning to integrate heterogeneous linked data by addressing issues like terminology and naming heterogeneity. It presents an approach using a subset of OWL 2 RL rules to reason over a billion triple corpus in a scalable way, handling the TBox separately from the ABox to avoid quadratic inferences. It also describes augmenting the reasoning with annotations to track trustworthiness and using this to filter inferences, detect inconsistencies and perform a light repair of the data. Consolidation is discussed as rewriting URIs to canonical identifiers based on owl:sameAs relations. Performance results show the different techniques taking between 1-20 hours to run over the corpus distributed across 9 machines.
FIWARE Global Summit - FIROS: Helping Robots to be Context AwareFIWARE
Presentation by Peter Detzner
Automation and Embedded Systems, Fraunhofer-Institut für Material Fluss und Logistik IML
FIWARE Global Summit
27-28 November 2018
Malaga, Spain
Producing, publishing and consuming linked data - CSHALS 2013François Belleau
This document discusses lessons learned from the Bio2RDF project for producing, publishing, and consuming linked data. It outlines three key lessons: 1) How to efficiently produce RDF using existing ETL tools like Talend to transform data formats into RDF triples; 2) How to publish linked data by designing URI patterns, offering SPARQL endpoints and associated tools, and registering data in public registries; 3) How to consume SPARQL endpoints by building semantic mashups using workflows to integrate data from multiple endpoints and then querying the mashup to answer questions.
Similar to Dependency Parsing-based QA System for RDF and SPARQL (20)
Data X Museum - Hari Museum Internasional 2022 - WMIDFariz Darari
This document discusses the importance of preserving cultural heritage through museums and digitizing cultural artifacts and traditions. It provides statistics on the diversity of Indonesian culture and examples of how structured data and APIs can be used to catalog and provide access to cultural works, including examples from Wikidata and the Metropolitan Museum of Art. The document encourages utilizing structured data to digitally preserve traditions like rendang and making museum data widely available to promote cultural heritage for all.
Kuis tryout 1 mata kuliah Dasar-Dasar Pemrograman 2 Fasilkom UI berisi soal pilihan ganda dan esai tentang konsep-konsep dasar Java seperti tipe data, pewarisan, package, class, objek, dan string builder. Soal-soal tersebut bertujuan mengetes pemahaman mahasiswa terhadap materi pemrograman dasar yang telah diajarkan.
Game theory is the study of strategic decision making between interdependent parties. It analyzes situations where players make decisions that will impact outcomes for themselves and others. The document provides examples of classic game theory scenarios like the prisoner's dilemma and discusses concepts like dominant strategies, Nash equilibriums, and mixed strategies. It also presents a two-player "two-finger Morra game" to illustrate game theory principles.
Neural Networks and Deep Learning: An IntroFariz Darari
This document provides an overview of neural networks and deep learning. It describes how artificial neurons are arranged in layers to form feedforward neural networks, with information fed from the input layer to subsequent hidden and output layers. Networks are trained using gradient descent to adjust weights between layers to minimize error. Convolutional neural networks are also discussed, which apply convolution and pooling operations to process visual inputs like images for tasks such as image classification. CNNs have achieved success in applications involving computer vision, natural language processing, and more.
Ringkasan dokumen tersebut adalah sebagai berikut:
1. Dokumen tersebut membahas tentang pengembangan talenta AI di perguruan tinggi dan hubungannya dengan industri, khususnya dalam memenuhi kebutuhan akan keterampilan AI.
2. Talenta AI di perguruan tinggi tidak hanya terfokus pada pendidikan AI saja, tetapi juga penelitian dan pengabdian masyarakat melalui teknologi AI.
3. Dibut
Basic Python Programming: Part 01 and Part 02Fariz Darari
This document discusses basic Python programming concepts including strings, functions, conditionals, loops, imports and recursion. It begins with examples of printing strings, taking user input, and calculating areas of shapes. It then covers variables and data types, operators, conditional statements, loops, functions, imports, strings, and recursion. Examples are provided throughout to demonstrate each concept.
This document discusses several topics related to properly implementing AI in education, including:
1) Ensuring AI teacher evaluation and models are not biased toward specific demographic groups or teaching styles.
2) The importance of data quality when training AI models, such as removing duplicates and standardizing formats.
3) The need for explainable AI models.
4) Examples of non-machine learning AI applications, such as an automated study topic scheduler.
5) A reminder that we have a choice in how AI is designed to have a positive impact.
Featuring pointers for: Single-layer neural networks and multi-layer neural networks, gradient descent, backpropagation. Slides are for introduction, for deep explanation on deep learning, please consult other slides.
Current situation: focus is limited to only implement Tridharma, that is, education, research, and community service, with little concern on openness aspect.
The openness of Tridharma can potentially be a breakthrough in mitigating the quality gap issue: opening Tridharma outputs for public would help to increase the citizen inclusion in accessing the quality content of Tridharma, hence narrowing the quality gap in higher education.
Defense Slides of Avicenna Wisesa - PROWDFariz Darari
This document presents ProWD, a tool for analyzing completeness in Wikidata. It introduces Wikidata and knowledge graphs, discusses issues like knowledge imbalance and inference errors due to lack of completeness awareness. It then presents a formal framework for completeness analysis using class, facet, and attribute profiles. This framework is implemented in ProWD, a proof of concept tool that allows analyzing Wikidata's completeness through single and compare views. ProWD is designed to be updated live and make completeness analysis accessible to laymen. Future work aims to expand the framework, improve scalability, and extend ProWD features.
This document provides an introduction to object-oriented programming concepts using Java. It begins by demonstrating how object-oriented thinking is natural through everyday examples of objects like cars and cats. It then defines key object-oriented programming terminology like class, object, attributes, and methods. The document walks through creating a sample Cube class to demonstrate these concepts in code. It shows how to define the class, instantiate objects, access attributes and call methods. The document also covers other OOP concepts like constructors, the toString() method, passing objects by reference, and the null value. Finally, it provides examples of real-world classes like String, LocalDate, Random and how to work with static variables and methods.
[ISWC 2013] Completeness statements about RDF data sources and their use for ...Fariz Darari
This was presented at ISWC 2013 in Sydney, Australia.
Abstract:
With thousands of RDF data sources available on the Web covering disparate and possibly overlapping knowledge domains, the problem of providing high-level descriptions (in the form of metadata) of their content becomes crucial. In this paper we introduce a theoretical framework for describing data sources in terms of their completeness. We show how existing data sources can be described with completeness statements expressed in RDF. We then focus on the problem of the completeness of query answering over plain and RDFS data sources augmented with completeness statements. Finally, we present an extension of the completeness framework for federated data sources.
Testing in Python: doctest and unittest (Updated)Fariz Darari
The document discusses testing in Python. It defines testing vs debugging, and explains why testing is important even for professional programmers. It provides examples of manually testing a square area function that initially had a bug, and how the bug was detected and fixed. It then introduces doctest and unittest as systematic ways to test in Python, providing examples of using each. Finally, it discusses test-driven development as a software development method where tests are defined before writing code.
Testing in Python: doctest and unittestFariz Darari
The document discusses testing in Python. It defines testing vs debugging, and explains why testing is important even for professional programmers. It introduces doctest and unittest as systematic ways to test Python code. Doctest allows embedding tests in docstrings, while unittest involves writing separate test files. The document also covers test-driven development, which involves writing tests before coding to define desired behavior.
Dissertation Defense - Managing and Consuming Completeness Information for RD...Fariz Darari
The ever increasing amount of Semantic Web data gives rise to the question: How complete is the data? Though generally data on the Semantic Web is incomplete, many parts of data are indeed complete, such as the children of Barack Obama and the crew of Apollo 11. This thesis aims to study how to manage and consume completeness information about Semantic Web data. In particular, we first discuss how completeness information can guarantee the completeness of query answering. Next, we propose optimization techniques of completeness reasoning and conduct experimental evaluations to show the feasibility of our approaches. We also provide a technique to check the soundness of queries with negation via reduction to query completeness checking. We further enrich completeness information with timestamps, enabling query answers to be checked up to when they are complete. We then introduce two demonstrators, i.e., CORNER and COOL-WD, to show how our completeness framework can be realized. Finally, we investigate an automated method to generate completeness statements from text on the Web via relation cardinality extraction.
The document provides information about research writing. It discusses that everyone can be considered a researcher through everyday activities like using social media or traveling. Research is defined as a careful, diligent search to establish new facts or reach conclusions. The constituents of research are outlined as defining problems, formulating hypotheses, collecting and analyzing data, and validating conclusions. The document emphasizes that research writing is important and discusses choosing the right research topic and venue for publication. It provides tips for writing different sections of a research paper and following the common three-phase model of initial workshop or conference papers leading to a journal publication.
KOI - Knowledge Of Incidents - SemEval 2018Fariz Darari
We present KOI (Knowledge Of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events.
KOI-KG can then be used to efficiently answer questions such as "How many killing incidents happened in 2017 that involve Sean?" The required steps in building the KG include:
(i) document preprocessing involving word sense disambiguation, named-entity recognition, temporal expression recognition and normalization, and semantic role labeling;
(ii) incidental event extraction and coreference resolution via document clustering; and (iii) KG construction and population.
Slides made and presented by Paramita.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
In this follow-up session on knowledge and prompt engineering, we will explore structured prompting, chain of thought prompting, iterative prompting, prompt optimization, emotional language prompts, and the inclusion of user signals and industry-specific data to enhance LLM performance.
Join EIS Founder & CEO Seth Earley and special guest Nick Usborne, Copywriter, Trainer, and Speaker, as they delve into these methodologies to improve AI-driven knowledge processes for employees and customers alike.
INDIAN AIR FORCE FIGHTER PLANES LIST.pdfjackson110191
These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
MYIR Product Brochure - A Global Provider of Embedded SOMs & SolutionsLinda Zhang
This brochure gives introduction of MYIR Electronics company and MYIR's products and services.
MYIR Electronics Limited (MYIR for short), established in 2011, is a global provider of embedded System-On-Modules (SOMs) and
comprehensive solutions based on various architectures such as ARM, FPGA, RISC-V, and AI. We cater to customers' needs for large-scale production, offering customized design, industry-specific application solutions, and one-stop OEM services.
MYIR, recognized as a national high-tech enterprise, is also listed among the "Specialized
and Special new" Enterprises in Shenzhen, China. Our core belief is that "Our success stems from our customers' success" and embraces the philosophy
of "Make Your Idea Real, then My Idea Realizing!"
How Social Media Hackers Help You to See Your Wife's Message.pdfHackersList
In the modern digital era, social media platforms have become integral to our daily lives. These platforms, including Facebook, Instagram, WhatsApp, and Snapchat, offer countless ways to connect, share, and communicate.
Blockchain technology is transforming industries and reshaping the way we conduct business, manage data, and secure transactions. Whether you're new to blockchain or looking to deepen your knowledge, our guidebook, "Blockchain for Dummies", is your ultimate resource.
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
Quality Patents: Patents That Stand the Test of TimeAurora Consulting
Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality.
Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality.
Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality.
Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank?
** Episode Overview **
In this first episode of our quality series, Kristen Hansen and the panel discuss:
⦿ What do we mean when we say patent quality?
⦿ Why is patent quality important?
⦿ How to balance quality and budget
⦿ The importance of searching, continuations, and draftsperson domain expertise
⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications
https://www.aurorapatents.com/patently-strategic-podcast.html
Sustainability requires ingenuity and stewardship. Did you know Pigging Solutions pigging systems help you achieve your sustainable manufacturing goals AND provide rapid return on investment.
How? Our systems recover over 99% of product in transfer piping. Recovering trapped product from transfer lines that would otherwise become flush-waste, means you can increase batch yields and eliminate flush waste. From raw materials to finished product, if you can pump it, we can pig it.
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Performance Budgets for the Real World by Tammy EvertsScyllaDB
Performance budgets have been around for more than ten years. Over those years, we’ve learned a lot about what works, what doesn’t, and what we need to improve. In this session, Tammy revisits old assumptions about performance budgets and offers some new best practices. Topics include:
• Understanding performance budgets vs. performance goals
• Aligning budgets with user experience
• Pros and cons of Core Web Vitals
• How to stay on top of your budgets to fight regressions
6. QA in General
• Finding an answer to natural language
questions based on documents/facts
• Instead of documents, give answers
• Factoid questions:
– Who can dance Tango?
– What did I eat this morning?
– When Mahatma Gandhi was born?
6
9. WordNet
• Large lexical database of English words
• Words are grouped into synsets
• Relations among synsets: Synonymy,
antonymy, hyponymy, meronymy, troponymy
9
12. NL Text Dependency
(Facts) Parser
RDFizer
RDF SPARQL
OWL SPARQLizer
Ontology
Dependency NL Text
Parser (Questions)
13. Facts Population
1. We parse the natural language facts using the
Stanford dependency parser. The result will be
typed dependencies.
2. The typed dependencies are then translated into
RDF format using RDFizer. The RDFizer is built
using Java with Apache Jena as the Semantic
Web library.
3. The resulting RDF will be consulted with an OWL
ontology that contains some WordNet and
DBpedia axioms to infer some new facts.
13
14. Query Execution
1. We parse the natural language questions
using the Stanford dependency parser. The
result will be typed dependencies.
2. We then translate the typed dependencies
into SPARQL query format.
3. The SPARQL query is then executed over
populated RDF data from the result of Facts
Population.
14
24. A Detailed Example (4)
KB has:
We have the following triple in the knowledge base
already:
:bought owl:sameAs :purchased .
and also the following rules:
PREFIX : <http://example.org/sentence/>
CONSTRUCT {:vehicle ?y ?z} WHERE {:car ?y ?z}
PREFIX : <http://example.org/sentence/>
CONSTRUCT {?x ?y :vehicle} WHERE {?x ?y :car}
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25. A Detailed Example (5)
Inferred facts:
<http://example.org/sentence/purchased>
<http://example.org/sentence/dobj>
<http://example.org/sentence/vehicle> ;
<http://example.org/sentence/nsubj>
<http://example.org/sentence/aliana> .
25
26. A Detailed Example (6)
Typed dependencies of question:
[nsubj(purchased-2, Who-1), root(ROOT-0,
purchased-2), det(vehicle-4, a-3), dobj(purchased-
2, vehicle-4)]
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27. A Detailed Example (7)
SPARQL form of question:
SELECT ?x WHERE {
:vehicle :det :a .
:purchased :nsubj ?x .
:purchased :dobj :vehicle .
:root :root :purchased }
Answer: “aliana”
27
28. DBpedia Integration
• By adding some background knowledge from
DBpedia, one can ask more questions.
• Example of Italy data:
:italy owl:sameAs dbpedia:Italy .
dbpedia:Italy dbpprop:capital "Rome" .
dbpedia:Enzo_Ferrari dbpedia-
owl:nationality dbpedia:Italy ;
dbpprop:deathPlace dbpedia:Maranello .
dbpedia:Enzo_Ferrari dbpedia-owl:child
dbpedia:Piero_Ferrari ,
dbpedia:Alfredo_Ferrari .
28
29. Example Case
• Fact = “Fariz loves Italy.”
• Question = “Does Fariz love a country, whose
capital is Rome, which was the nationality of a
person who passed away in Maranello and
whose sons are Piero Ferrari and Alfredo
Ferrari?”
• Thus the answer will be: YES, eventhough we
only have a fact, Fariz loves Italy.
29
30. Example Case (cont.)
• Note that, the previous example, the fact is
translated automatically by the system but the
question is translated manually to be the
following SPARQL query:
ASK WHERE { :love :nsubj :fariz . :root
:root :love .
:love :dobj ?x .
?x dbpprop:capital "Rome" .
?y dbpedia-owl:nationality ?x ;
dbpprop:deathPlacedbpedia:Maranello .
?y dbpedia-owl:childdbpedia:Piero_Ferrari
, dbpedia:Alfredo_Ferrari }
30
31. How to handle negation? (1)
• Fact: I did not buy it.
• RDF:
<http://example.org/sentence/root>
<http://example.org/sentence/root>
<http://example.org/sentence/bought> .
<http://example.org/sentence/bought>
<http://example.org/sentence/dobj>
<http://example.org/sentence/it> ;
<http://example.org/sentence/neg>
<http://example.org/sentence/not> ;
<http://example.org/sentence/nsubj>
<http://example.org/sentence/i> .
31
32. How to handle negation? (2)
• Question: Who bought it?
• SPARQL:
SELECT ?x WHERE {:bought :nsubj ?x . :bought
:dobj :it . :root :root :bought . FILTER NOT
EXISTS { [] :neg ?z . } }
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33. How to handle negation? (3)
• Who did not buy it? I.
QUERY: SELECT ?x WHERE {:bought :dobj :it .
:bought :neg :not . :bought :nsubj ?x . :root
:root :bought }
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34. How to handle tenses? (1)
• Fact (I will buy it):
<http://example.org/sentence/buy>
<http://example.org/sentence/aux>
<http://example.org/sentence/will> ;
<http://example.org/sentence/dobj>
<http://example.org/sentence/it> ;
<http://example.org/sentence/nsubj>
<http://example.org/sentence/i> .
34
35. How to handle tenses? (2)
• Who buys it?
• SELECT ?x WHERE {:root :root :buys . :buys
:nsubj ?x . :buys :dobj :it . FILTER NOT EXISTS {
[] :aux :will . } }
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36. How to handle passive sentences?
Fact: Juliet was killed by Romeo.
<http://example.org/sentence/root>
<http://example.org/sentence/root>
<http://example.org/sentence/killed> .
<http://example.org/sentence/killed>
<http://example.org/sentence/agent>
<http://example.org/sentence/romeo> ;
<http://example.org/sentence/nsubjpass>
<http://example.org/sentence/juliet> .
36
37. How to handle passive sentences?
• Ontology:
:nsubjpass owl:equivalentProperty :dobj .
:agent owl:equivalentProperty :nsubj .
37
38. How to handle passive sentences?
• Who killed Juliet?
SELECT ?x WHERE {:killed :nsubj ?x . :killed :dobj
:juliet . :root :root :killed . FILTER NOT EXISTS {
[] :neg ?z . }}
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39. DEMO - A Story about Antonio
Antonio is a famous and cool doctor. Antonio
has been working for 10 years. Antonio is in
Italy. Antonio can dance Salsa well. Antonio
loves Maria and Karina. Antonio is also loved
by Karina. Antonio never cooks. But Maria
always cooks. Antonio just bought a car.
Antonio must fly to Indonesia tomorrow.
39
40. Conclusions
• Dependency parsing-based QA system with
RDF and SPARQL
• The system is also aware of negations, tenses
and passive sentences
• Improvements: More advanced parsing
method, more efficient inference system,
richer background knowledge
40
50. Working Examples
• "John, that is the founder of Microsoft and the
initiator of Greenpeace movement, is genius,
honest and cool."
• "Who is honest?"
50
51. Working Examples
• "Farid wants to go to Rome.“
• "Who wants to go to Rome?"
• "Who wants to go?"
• "Who wants?"
51
52. Working Examples
• "Jorge ate 10 delicious apples."
• "Who ate 10 delicious apples?"
• "Who ate 10 apples?"
• "Who ate apples?"
52
Siri image: apple.comWatson: gizmodo.comExample questions (Querying Contacts):What's Michael's address?What is Susan Park's phone number?When is my wife's birthday?Show Jennifer's home email address
Siri image: apple.comWatson: gizmodo.comAnswer - Question systemfor example: 4 JulyWhen is the celebration of the independence day of the USA?
The Stanford dependencies provide a representation of grammatical relations between words in a sentence. They have been designed to be easily understood and effectively used by people who want to extract textual relations. Stanford dependencies (SD) are triplets: name of the relation, governor and dependent.The dependencies are produced using hand-written tregex patterns over phrase-structure trees as described in:Marie-Catherine de Marneffe, Bill MacCartney and Christopher D. Manning. 2006. Generating Typed Dependency Parses from Phrase Structure Parses. In LREC 2006.
SW: an extension of the Web with machine-interpretable content
Synsets: sets of cognitive synonyms, each expressing a distinct conceptAn example of synonymy: buy = purchaseantonymy: bad = goodhyponymy: hyponymy = relationmeronymy: tires = cartroponymy: run = move