I used this slideset to present our research paper at the 14th Int. Semantic Web Conference (ISWC 2015). Find a preprint of the paper here:
http://olafhartig.de/files/HartigPerez_ISWC2015_Preprint.pdf
Rethinking Online SPARQL Querying to Support Incremental Result VisualizationOlaf Hartig
These are the slides of my invited talk at the 5th Int. Workshop on Usage Analysis and the Web of Data (USEWOD 2015): http://usewod.org/usewod2015.html
The abstract of this talks is given as follows:
To reduce user-perceived response time many interactive Web applications visualize information in a dynamic, incremental manner. Such an incremental presentation can be particularly effective for cases in which the underlying data processing systems are not capable of completely answering the users' information needs instantaneously. An example of such systems are systems that support live querying of the Web of Data, in which case query execution times of several seconds, or even minutes, are an inherent consequence of these systems' ability to guarantee up-to-date results. However, support for an incremental result visualization has not received much attention in existing work on such systems. Therefore, the goal of this talk is to discuss approaches that enable query systems for the Web of Data to return query results incrementally.
What is the fuzz on triple stores? Will triple stores eventually replace relational databases? This talk looks at the big picture, explains the technology and tries to look at the road ahead.
Rethinking Online SPARQL Querying to Support Incremental Result VisualizationOlaf Hartig
These are the slides of my invited talk at the 5th Int. Workshop on Usage Analysis and the Web of Data (USEWOD 2015): http://usewod.org/usewod2015.html
The abstract of this talks is given as follows:
To reduce user-perceived response time many interactive Web applications visualize information in a dynamic, incremental manner. Such an incremental presentation can be particularly effective for cases in which the underlying data processing systems are not capable of completely answering the users' information needs instantaneously. An example of such systems are systems that support live querying of the Web of Data, in which case query execution times of several seconds, or even minutes, are an inherent consequence of these systems' ability to guarantee up-to-date results. However, support for an incremental result visualization has not received much attention in existing work on such systems. Therefore, the goal of this talk is to discuss approaches that enable query systems for the Web of Data to return query results incrementally.
What is the fuzz on triple stores? Will triple stores eventually replace relational databases? This talk looks at the big picture, explains the technology and tries to look at the road ahead.
Overview of the SPARQL-Generate language and latest developmentsMaxime Lefrançois
SPARQL-Generate is an extension of SPARQL 1.1 for querying not only RDF datasets but also documents in arbitrary formats. The solution bindings can then be used to output RDF (SPARQL-Generate) or text (SPARQL-Template)
Anyone familiar with SPARQL can easily learn SPARQL-Generate; Learning SPARQL-Generate helps you learning SPARQL.
The open-source implementation (Apache 2 license) is based on Apache Jena and can be used to execute transformations from a combination of RDF and any kind of documents in XML, JSON, CSV, HTML, GeoJSON, CBOR, streams of messages using WebSocket or MQTT... (easily extensible)
Recent extensions and improvement include:
- heavy refactoring to support parallelization
- more expressive iterators and functions
- simple generation of RDF lists
- support of aggregates
- generation of HDT (thanks Ana for the use case)
- partial implementation of STTL for the generation of Text (https://ns.inria.fr/sparql-template/)
- partial implementation of LDScript (http://ns.inria.fr/sparql-extension/)
- integration of all these types of rules to decouple or compose queries, e.g.:
- call a SPARQL-Generate query in the SPARQL FROM clause
- plug a SPARQL-Generate or a SPARQL-Template query to the output of a SPARQL-
Select function
- a Sublime Text package for local development
Knowledge graph embeddings are a mechanism that projects each entity in a knowledge graph to a point in a continuous vector space. It is commonly assumed that those approaches project two entities closely to each other if they are similar and/or related. In this talk, I give a closer look at the roles of similarity and relatedness with respect to knowledge graph embeddings, and discuss how the well-known embedding mechanism RDF2vec can be tailored towards focusing on similarity, relatedness, or both.
Wi2015 - Clustering of Linked Open Data - the LODeX toolLaura Po
Presentation of the tool LODeX (http://www.dbgroup.unimore.it/lodex2/testCluster) at the 2015 IEEE/WIC/ACM International Conference on Web Intelligence, Singapore, December 6-8, 2015
Presentation done* at the 13th International Semantic Web Conference (ISWC) in which we approach a compressed format to represent RDF Data Streams. See the original article at: http://dataweb.infor.uva.es/wp-content/uploads/2014/07/iswc14.pdf
* Presented by Alejandro Llaves (http://www.slideshare.net/allaves)
Knowledge Discovery tools using Linked Data techniques - {resentation for the Linked Data 4 Knowledge Discovery Workshop at ECML/PKDD2015 conference - http://events.kmi.open.ac.uk/ld4kd2015/ -
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
Talk at 3th Keystone Training School - Keyword Search in Big Linked Data - Institute for Software Technology and Interactive Systems, TU Wien, Austria, 2017
CIDOC Congress, Dresden, Germany
2014-09-05: International Terminology Working Group: full version (http://vladimiralexiev.github.io/pres/20140905-CIDOC-GVP/index.html)
2014-09-09: Getty special session: short version (http://VladimirAlexiev.github.io/pres/20140905-CIDOC-GVP/GVP-LOD-CIDOC-short.pdf)
VALA Tech Camp 2017: Intro to Wikidata & SPARQLJane Frazier
A hands-on introduction to interrogation of Wikidata content using SPARQL, the query language used to query data represented in RDF, SKOS, OWL, and other Semantic Web standards.
Presented by myself and Peter Neish, Research Data Specialist @ University of Melbourne.
n overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
Full version of http://www.slideshare.net/valexiev1/gvp-lodcidocshort. Same is available on http://vladimiralexiev.github.io/pres/20140905-CIDOC-GVP/index.html
CIDOC Congress, Dresden, Germany
2014-09-05: International Terminology Working Group: full version.
2014-09-09: Getty special session: short version
Gathering Alternative Surface Forms for DBpedia EntitiesHeiko Paulheim
Wikipedia is often used a source of surface forms, or alternative reference strings for an entity, required for entity linking, disambiguation or coreference resolution tasks. Surface forms have been extracted in a number of works from Wikipedia labels, redirects, disambiguations and anchor texts of internal Wikipedia links, which we complement with anchor texts of external Wikipedia links from the Common Crawl web corpus. We tackle the problem of quality of Wikipedia-based surface forms, which has not been raised before. We create the gold standard for the dataset quality evaluation, which reveales the surprisingly low precision of the Wikipedia-based surface forms. We propose filtering approaches that allowed boosting the precision from 75% to 85% for a random entity subset, and from 45% to more than 65% for the subset of popular entities. The filtered surface form dataset as well the gold standard are made publicly available.
Overview of the SPARQL-Generate language and latest developmentsMaxime Lefrançois
SPARQL-Generate is an extension of SPARQL 1.1 for querying not only RDF datasets but also documents in arbitrary formats. The solution bindings can then be used to output RDF (SPARQL-Generate) or text (SPARQL-Template)
Anyone familiar with SPARQL can easily learn SPARQL-Generate; Learning SPARQL-Generate helps you learning SPARQL.
The open-source implementation (Apache 2 license) is based on Apache Jena and can be used to execute transformations from a combination of RDF and any kind of documents in XML, JSON, CSV, HTML, GeoJSON, CBOR, streams of messages using WebSocket or MQTT... (easily extensible)
Recent extensions and improvement include:
- heavy refactoring to support parallelization
- more expressive iterators and functions
- simple generation of RDF lists
- support of aggregates
- generation of HDT (thanks Ana for the use case)
- partial implementation of STTL for the generation of Text (https://ns.inria.fr/sparql-template/)
- partial implementation of LDScript (http://ns.inria.fr/sparql-extension/)
- integration of all these types of rules to decouple or compose queries, e.g.:
- call a SPARQL-Generate query in the SPARQL FROM clause
- plug a SPARQL-Generate or a SPARQL-Template query to the output of a SPARQL-
Select function
- a Sublime Text package for local development
Knowledge graph embeddings are a mechanism that projects each entity in a knowledge graph to a point in a continuous vector space. It is commonly assumed that those approaches project two entities closely to each other if they are similar and/or related. In this talk, I give a closer look at the roles of similarity and relatedness with respect to knowledge graph embeddings, and discuss how the well-known embedding mechanism RDF2vec can be tailored towards focusing on similarity, relatedness, or both.
Wi2015 - Clustering of Linked Open Data - the LODeX toolLaura Po
Presentation of the tool LODeX (http://www.dbgroup.unimore.it/lodex2/testCluster) at the 2015 IEEE/WIC/ACM International Conference on Web Intelligence, Singapore, December 6-8, 2015
Presentation done* at the 13th International Semantic Web Conference (ISWC) in which we approach a compressed format to represent RDF Data Streams. See the original article at: http://dataweb.infor.uva.es/wp-content/uploads/2014/07/iswc14.pdf
* Presented by Alejandro Llaves (http://www.slideshare.net/allaves)
Knowledge Discovery tools using Linked Data techniques - {resentation for the Linked Data 4 Knowledge Discovery Workshop at ECML/PKDD2015 conference - http://events.kmi.open.ac.uk/ld4kd2015/ -
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
Talk at 3th Keystone Training School - Keyword Search in Big Linked Data - Institute for Software Technology and Interactive Systems, TU Wien, Austria, 2017
CIDOC Congress, Dresden, Germany
2014-09-05: International Terminology Working Group: full version (http://vladimiralexiev.github.io/pres/20140905-CIDOC-GVP/index.html)
2014-09-09: Getty special session: short version (http://VladimirAlexiev.github.io/pres/20140905-CIDOC-GVP/GVP-LOD-CIDOC-short.pdf)
VALA Tech Camp 2017: Intro to Wikidata & SPARQLJane Frazier
A hands-on introduction to interrogation of Wikidata content using SPARQL, the query language used to query data represented in RDF, SKOS, OWL, and other Semantic Web standards.
Presented by myself and Peter Neish, Research Data Specialist @ University of Melbourne.
n overview of existing solutions for link discovery and looked into some of the state-of-art algorithms for the rapid execution of link discovery tasks focusing on algorithms which guarantee result completeness.
(HOBBIT project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688227.)
Full version of http://www.slideshare.net/valexiev1/gvp-lodcidocshort. Same is available on http://vladimiralexiev.github.io/pres/20140905-CIDOC-GVP/index.html
CIDOC Congress, Dresden, Germany
2014-09-05: International Terminology Working Group: full version.
2014-09-09: Getty special session: short version
Gathering Alternative Surface Forms for DBpedia EntitiesHeiko Paulheim
Wikipedia is often used a source of surface forms, or alternative reference strings for an entity, required for entity linking, disambiguation or coreference resolution tasks. Surface forms have been extracted in a number of works from Wikipedia labels, redirects, disambiguations and anchor texts of internal Wikipedia links, which we complement with anchor texts of external Wikipedia links from the Common Crawl web corpus. We tackle the problem of quality of Wikipedia-based surface forms, which has not been raised before. We create the gold standard for the dataset quality evaluation, which reveales the surprisingly low precision of the Wikipedia-based surface forms. We propose filtering approaches that allowed boosting the precision from 75% to 85% for a random entity subset, and from 45% to more than 65% for the subset of popular entities. The filtered surface form dataset as well the gold standard are made publicly available.
Introduction to the Data Web, DBpedia and the Life-cycle of Linked DataSören Auer
Over the past 4 years, the Semantic Web activity has gained momentum with the widespread publishing of structured data as RDF. The Linked Data paradigm has therefore evolved from a practical research idea into
a very promising candidate for addressing one of the biggest challenges
of computer science: the exploitation of the Web as a platform for data
and information integration. To translate this initial success into a
world-scale reality, a number of research challenges need to be
addressed: the performance gap between relational and RDF data
management has to be closed, coherence and quality of data published on
the Web have to be improved, provenance and trust on the Linked Data Web
must be established and generally the entrance barrier for data
publishers and users has to be lowered. This tutorial will discuss
approaches for tackling these challenges. As an example of a successful
Linked Data project we will present DBpedia, which leverages Wikipedia
by extracting structured information and by making this information
freely accessible on the Web. The tutorial will also outline some recent advances in DBpedia, such as the mappings Wiki, DBpedia Live as well as
the recently launched DBpedia benchmark.
Open Education Challenge 2014: exploiting Linked Data in Educational Applicat...Stefan Dietze
Presentation from mentoring event of Open Education Europa Challenge (http://www.openeducationchallenge.eu/) about using Linked Data in educational applications.
Evaluating Named Entity Recognition and Disambiguation in News and TweetsMarieke van Erp
Named entity recognition and disambiguation are important for information extraction and populating knowledge bases. Detecting and classifying named entities has traditionally been taken on by the natural language processing community, whilst linking of entities to external resources, such as DBpedia and GeoNames, has been the domain of the Semantic Web community. As these tasks are treated in different communities, it is difficult to assess the performance of these tasks combined.
We present results on an evaluation of the NERD-ML approach on newswire and tweets for both Named Entity Recognition and Named Entity Disambiguation.
Presented at CLIN 24: http://clin24.inl.nl/
http://nerd.eurecom.fr
https://github.com/giusepperizzo/nerdml
Introduction to DBpedia, the most popular and interconnected source of Linked Open Data. Part of EXPLORING WIKIDATA AND THE SEMANTIC WEB FOR LIBRARIES at METRO http://metro.org/events/598/
Fast Approximate A-box Consistency Checking using Machine LearningHeiko Paulheim
Ontology reasoning is typically a computationally intensive operation. While soundness and completeness of results is required in some use cases, for many others, a sensible trade-off between computation efforts and correctness of results makes more sense. In this paper, we show that it is possible to approximate a central task in reasoning, i.e., A-box consistency checking, by training a machine learning model which approximates the behavior of that reasoner for a specific ontology. On four different datasets, we show that such learned models constantly achieve an accuracy above 95% at less than 2% of the runtime of a reasoner, using a decision tree with no more than 20 inner nodes. For example, this allows for validating 293M Microdata documents against the schema.org ontology in less than 90 minutes, compared to 18 days required by a state of the art ontology reasoner.
Data Mining with Background Knowledge from the Web - Introducing the RapidMin...Heiko Paulheim
Many data mining problems can be solved better if more background knowledge is added: predictive models can become more accurate, and descriptive models can reveal more interesting findings. However, collecting and integrating background knowledge is tedious manual work. In this paper, we introduce the RapidMiner Linked Open Data Extension, which can extend a dataset at hand with additional attributes drawn from the Linked Open Data (LOD) cloud, a large collection of publicly available datasets on various topics. The extension contains operators for linking local data to open data in the LOD cloud, and for augmenting it with additional attributes. In a case study, we show that the prediction error of car fuel consumption can be reduced by 50% by adding additional attributes, e.g., describing the automobile layout and the car body configuration, from Linked Open Data.
Unsupervised Extraction of Attributes and Their Values from Product DescriptionRakuten Group, Inc.
Keiji Shinzato and Satoshi Sekine
17th Oct. 2013
The 6th International Joint Conference on Natural Language Processing
This slide shows an unsupervised method for extracting product attributes and their values from an e-commerce product page. Previously, distant supervision has been applied for this task, but it is not applicable in domains where no reliable knowledge base (KB) is available. Instead, the proposed method automatically creates a KB from tables and itemizations embedded in the product’s pages. This KB is applied to annotate the pages automatically and the annotated corpus is used to train a model for the extraction. Because of the incompleteness of the KB, the annotated corpus is not as accurate as a manually annotated one. Our method tries to filter out sentences that are likely to include problematic annotations based on statistical measures and morpheme patterns induced from the entries in the KB. The experimental results show that the performance of our method achieves an average F score of approximately 58.2 points and that filters can improve the performance.
Health care and life sciences research heavily relies on the ability to search, discover, formulate and correlate data from distinct sources. Over the last decade the deluge of health care life science data and the standardisation of linked data technologies resulted in publishing datasets of great importance. This emerged as an opportunity to explore new ways of bio-medical discovery through standardised interfaces.
Although the Semantic Web and Linked Data technologies help in dealing with data integration problem there remains a barrier adopting these for non-technical research audiences. In this paper we present FedViz, a visual interface for SPARQL query formulation and execution. FedViz is explicitly designed to increase intuitive data interaction from distributed sources and facilitates federated as well as non-federated SPARQL queries formulation. FedViz uses FedX for query execution and results retrieval. We also evaluate the usability of our system by using the standard system usability scale as well as a custom questionnaire, particularly designed to test the usability of the FedViz interface. Our overall usability score of 74.16 suggests that FedViz interface is easy to learn, consistent, and adequate for frequent use.
Coral & Transport UDFs: Building Blocks of a Postmodern Data WarehouseWalaa Eldin Moustafa
These slides describe LinkedIn efforts in building a view virtualization layer that enables compute engines to access Hive views, reason about them semantically, and optionally rewrite them before presenting to various compute engines such as Spark, Hive, and Presto. Namely, we describe two frameworks: Coral for reasoning about views using their logical plans, and Transport UDFs for reasoning about UDFs.
Materials science experiments involve complex data that are often very heterogeneous and challenging to reproduce. Challenges with materials science data were observed, for example, in a previous study on harnessing lightweight design potentials via the Materials Data Space for which the data from materials sciences engineering experiments were generated using linked open data principles, e.g., Resource Description Framework (RDF) as the standard model for data interchange on the Web. However, detailed knowledge of formulating questions in the query language SPARQL is necessary to query the data. It was noticed that domain experts in Materials Science lack knowledge of querying the data using SPARQL queries. With this work, we aim to develop NaturalMSEQueries an approach for the material science domain expert where instead of SPARQL queries, the user can develop expressions in natural language, e.g., English, to query the data. This will significantly improve the usability of Semantic Web approaches in materials science and lower the adoption threshold of the methods for the domain experts. We plan to evaluate our approach, with varying amounts of data, from different sources. Furthermore, we want to compare with synthetic data to assess the quality of the implementation of our approach.
Semantics and optimisation of the SPARQL1.1 federation extensionOscar Corcho
Presentation done at ESWC2011 for the paper "Semantics and optimisation of the SPARQL1.1 federation extension". Buil-Aranda C, Arenas M, Corcho O. ESWC2011, May 2011, Hersonissos, Greece
Slides for the following paper: NLP Data Cleansing Based on Linguistic Ontology Constraints
Abstract: Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is – compared to other domains, such as biology – a late Linked Data adopter. However, it has seen a
steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the lemon and NIF vocabularies in 277 test cases and point out common quality issues.
8th TUC Meeting - Peter Boncz (CWI). Query Language Task Force statusLDBC council
Peter Boncz, Research Scientist at the Centrum Wiskunde & Informatica in the Netherlands, talked about the updates on the Graph Query Language Task Force after being alive for a year. This Task Force was created to answer an issue detected during the benchmark meetings, all the workload is created in English text because there is no common graph query language.
a system called natural language interface which transforms user's natural language question into SPARQL query
find related papers here https://sites.google.com/site/fadhlinams81/publication
This presentation was given at the International Workshop on Interacting with Linked Data (ILD 2012) co-located with the 9th Extended Semantic Web Conference 2012, Heraklion, and is related the publication of the same title.
Much research has been done to combine the fields of Data-bases and Natural Language Processing. While many works focus on the problem of deriving a structured query for a given natural language question, the problem of query verbalization -- translating a structured query into natural language -- is less explored. In this work we describe our approach to verbalizing SPARQL queries in order to create natural language expressions that are readable and understandable by the human day-to-day user. These expressions are helpful when having search engines that generate SPARQL queries for user-provided natural language questions or keywords. Displaying verbalizations of generated queries to a user enables the user to check whether the right question has been understood. While our approach enables verbalization of only a subset of SPARQL 1.1, this subset applies to 90% of the 209 queries in our training set. These observations are based on a corpus of SPARQL queries consisting of datasets from the QALD-1 challenge and the ILD2012 challenge.
The publication is available at http://www.aifb.kit.edu/images/b/b7/VerbalizingSparqlQueries.pdf
PhD thesis defense.
This manuscript describes a methodology designed and implemented to realise the recommendation of vocabularies based on the content of a given website. The goal of the proposed approach is to generate vocabularies by reusing existing schemas. The automatic recommendation helps to leverage websites to self-described web entities in the Web of Data; understandable by both humans and machines. In this direction, the implemented approach is wrapped within a broader methodology of turning a website in a machine understandable node by using technologies that have been developed in the scope of the Semantic Web vision. Transforming a website to a machine understandable entity is the first step required by the websites side in order to narrow the gap with web agents and enable the structured content consumption without the need of implementing an Application Programming Interface (API) that would provide read-write functionality. The motivation of the thesis stems from the fact that the data provided via an API is already presented on the corresponding website in most of the cases.
SPARQL Query Containment with ShEx ConstraintsAbdullah Abbas
ShEx (Shape Expressions) is a language for expressing constraints on RDF graphs. We consider the problem of SPARQL query containment in the presence of ShEx constraints. We first propose a sound and complete procedure for the problem of containment with ShEx, considering several SPARQL fragments. Particularly our procedure considers OPTIONAL query patterns, that turns out to be an important fragment to be studied with schemas. We then show the complexity bounds of our problem with respect to the fragments considered. To the best of our knowledge, this is the first work addressing SPARQL query containment in the presence of ShEx constraints.
Redundancy analysis on linked data #cold2014 #ISWC2014honghan2013
Wu, Honghan, Boris Villazon-Terrazas, Jeff Z. Pan, and Jose Manuel Gomez-Perez. “How Redundant Is It? – An Empirical Analysis on Linked Datasets.” In ISWC COLD Workshop. 2014.
http://ceur-ws.org/Vol-1264/cold2014_WuVPG.pdf
JURIX talk on representing and reasoning on the deontic aspects of normative rules relying only on standard Semantic Web languages.
The corresponding paper is at https://hal.inria.fr/hal-01643769v1
An Overview on PROV-AQ: Provenance Access and QueryOlaf Hartig
The slides which I used at the Dagstuhl seminar on Principles of Provenance (Feb.2012) for presenting the main contributions and open issues of the PROV-AQ document created by the W3C provenance working group.
Querying Trust in RDF Data with tSPARQLOlaf Hartig
With these slides I presented my paper on "Querying Trust in RDF Data with tSPARQL" at the European Semantic Web Conference 2009 (ESWC) in Heraklion, Crete. Actually, this slideset is an extended version of the slides I used for the talk (more examples and evaluation).
I used these slides for an introductory lecture (90min) to a seminar on SPARQL. This slideset introduces the semantics of the RDF query language SPARQL.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
LDQL: A Query Language for the Web of Linked Data
1. LDQL: A Query Language
for the Web of Linked Data
Olaf Hartig¹ and Jorge Pérez²
¹Hasso Plattner Institute, University of Potsdam, Germany
²Department of Computer Science, Universidad de Chile
2. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 2
Linked Data Query Processing
3. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 3
Research Question
What language
can we use to express queries
(in a declarative manner)
?
4. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 4
Existing Proposals 1/2
● Basic Graph Patterns
– Bounded method [Bouquet, Ghidini, Serafini 2009]
– Navigational method [Bouquet, Ghidini, Serafini 2009]
– Direct access method [Bouquet, Ghidini, Serafini 2009]
– Matching-based query semantics [Hartig 2011]
– Family of authoritativeness-based query semantics
[Harth and Speiser 2012]
– Different inference-based query semantics
[Umbrich et al. 2012]
5. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 5
Existing Proposals 2/2
● SPARQL 1.0
– Full-Web query semantics [Hartig 2012]
– Family of reachability-based semantics [Hartig 2012]
● cALL-semantics, cNONE-semantics, cMATCH-semantics, …
● SPARQL 1.1 Property Paths Patterns
– Context-based query semantics [Hartig and Pirrò 2015]
● NautiLOD [Fionda, Gutierrez, and Pirrò 2012]
● LDPath (no formal semantics) [Schaffert et al. 2012]
6. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 6
Limitation of Existing Proposals
● No separation of the following two tasks:
– Select query-relevant regions of the Web of Linked Data
– Evaluate pattern over the data in the selected regions
● For instance, impossible to express queries such as:
– Take all Linked Data reached by following
two foaf:knows links starting from URI u,
and evaluate a given SPARQL pattern
over the union of this data.
7. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 7
General Idea of LDQL
Query
Specification of
query-relevant
region
( N , Q )
8. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 8
Our Contributions
● Formal syntax and semantics of LDQL
– Including some rewrite rules (i.e., equivalences)
● Study Web safeness of LDQL queries
– Show a decidable syntactic property of LDQL queries
for which a complete execution is feasible in practice
● Compare LDQL with existing proposals
– Show that LDQL is strictly more expressive
? ?
?
? ?
??
?
9. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 9
Existing Proposals 2/2
● SPARQL 1.0
– Full-Web query semantics [Hartig 2012]
– Family of reachability-based semantics [Hartig 2012]
● cALL-semantics, cNONE-semantics, cMATCH-semantics, …
● SPARQL 1.1 Property Paths Patterns
– Context-based query semantics [Hartig and Pirrò 2015]
● NautiLOD [Fionda, Gutierrez, and Pirrò 2012]
● LDPath (no formal semantics) [Schaffert et al. 2012]
√
√
√ √ √
10. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 10
Our Contributions
● Formal syntax and semantics of LDQL
– Including some rewrite rules (i.e., equivalences)
● Study Web safeness of LDQL queries
– Show a decidable syntactic property of LDQL queries
for which a complete execution is feasible in practice
● Compare LDQL with existing proposals
– Show that LDQL is strictly more expressive
? ?
?
? ?
??
?
Let's have a brief look!
11. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 11
General Idea of LDQL
Query
( N , Q )
Specification of
query-relevant
region
12. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 12
General Idea of LDQL
SPARQL 1.1
graph pattern
Link Path Expression
(Nested regular
expressions over
an alphabet of so
called link patterns)
Query
( N , Q )
Specification of
query-relevant
region
13. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 13
Link Patterns
● Each βi is either:
– a URI, or
– a wildcard denoted by _, or
– a placeholder for the context URI denoted by +
● β3 may also be a literal
● Example:
( β1, β2, β3 )
( +, foaf:knows, _ )
14. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 14
Link Graph
( ua, foaf:knows, ub )
( uc, foaf:knows, ua )
doc(ua)
…
doc(ub)
...
doc(uc)
( +, foaf:knows, _ )
15. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 15
Link Graph
( ua, foaf:knows, ub )
( uc, foaf:knows, ua )
doc(ua)
…
doc(ub)
...
doc(uc)
( +, foaf:knows, _ )
( ua, foaf:knows, ub ), ub ( uc, foaf:knows, ua ), uc( ua, foaf:knows, ub )
( uc, foaf:knows, ua )
16. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 16
Link Pattern Matching
( ua, foaf:knows, ub )
( uc, foaf:knows, ua )
doc(ua)
…
doc(ub)
...
doc(uc)
( +, foaf:knows, _ )
( ua, foaf:knows, ub ), ub ( uc, foaf:knows, ua ), uc
A link graph edge labeled with RDF triple (x1, x2, x3) and URI u
matches a link pattern (β1, β2, β3) in the context of URI uctx if:
• i ∈ {1,2,3} such that βi = _ and xi = u, and
• i ∈ {1,2,3} (i) βi = xi , (ii) βi = _ , or (iii) βi = + and xi = uctx
17. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 17
Link Pattern Matching
( ua, foaf:knows, ub )
( uc, foaf:knows, ua )
doc(ua)
…
doc(ub)
...
doc(uc)
( +, foaf:knows, _ )
( ua, foaf:knows, ub ), ub ( uc, foaf:knows, ua ), uc
A link graph edge labeled with RDF triple (x1, x2, x3) and URI u
matches a link pattern (β1, β2, β3) in the context of URI uctx if:
• i ∈ {1,2,3} such that βi = _ and xi = u, and
• i ∈ {1,2,3} (i) βi = xi , (ii) βi = _ , or (iii) βi = + and xi = uctx
√ X
18. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 18
(Algebraic) Syntax of LDQL
q := (lpe, P) | (lpe an LPE, P a SPARQL pattern)
(q AND q) |
(q UNION q) |
πV q | (V a set of variables)
(SEED U q) | (U a set of URIs)
(SEED ?v q) (?v a variable)
lpe := ε | lp | lpe/lpe | lpe|lpe | lpe* | [lpe] | (?v, q)
19. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 19
(Algebraic) Syntax of LDQL
q := (lpe, P) | (lpe an LPE, P a SPARQL pattern)
(q AND q) |
(q UNION q) |
πV q | (V a set of variables)
(SEED U q) | (U a set of URIs)
(SEED ?v q) (?v a variable)
lpe := ε | lp | lpe/lpe | lpe|lpe | lpe* | [lpe] | (?v, q)
Example: Take all Linked Data reached by following two
foaf:knows links starting from URI u, and evaluate
SPARQL pattern P over the union of this data.
( (+,foaf:knows, _) /(+,foaf:knows, _) , P )
with seeds S = {u }
20. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 20
(Algebraic) Syntax of LDQL
q := (lpe, P) | (lpe an LPE, P a SPARQL pattern)
(q AND q) |
(q UNION q) |
πV q | (V a set of variables)
(SEED U q) | (U a set of URIs)
(SEED ?v q) (?v a variable)
lpe := ε | lp | lpe/lpe | lpe|lpe | lpe* | [lpe] | (?v, q)
Example: Take all Linked Data reached by following two
foaf:knows links starting from URI u, and evaluate
SPARQL pattern P over the union of this data.
( (+,foaf:knows, _) /(+,foaf:knows, _) , P )
with seeds S = {u }
21. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 21
(Algebraic) Syntax of LDQL
q := (lpe, P) | (lpe an LPE, P a SPARQL pattern)
(q AND q) |
(q UNION q) |
πV q | (V a set of variables)
(SEED U q) | (U a set of URIs)
(SEED ?v q) (?v a variable)
lpe := ε | lp | lpe/lpe | lpe|lpe | lpe* | [lpe] | (?v, q)
Example: Take all Linked Data reached by following two
foaf:knows links starting from URI u, and evaluate
SPARQL pattern P over the union of this data.
( (+,foaf:knows, _) /(+,foaf:knows, _) , P )
with seeds S = {u }
22. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 22
(Algebraic) Syntax of LDQL
q := (lpe, P) | (lpe an LPE, P a SPARQL pattern)
(q AND q) |
(q UNION q) |
πV q | (V a set of variables)
(SEED U q) | (U a set of URIs)
(SEED ?v q) (?v a variable)
lpe := ε | lp | lpe/lpe | lpe|lpe | lpe* | [lpe] | (?v, q)
For every LDQL query q',
there exists a semantically equivalent LDQL query q''
such that
every LPE in q'' consists only of ε and lpe* and (?v, q).
23. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 23
(Algebraic) Syntax of LDQL
q := (lpe, P) | (lpe an LPE, P a SPARQL pattern)
(q AND q) |
(q UNION q) |
πV q | (V a set of variables)
(SEED U q) | (U a set of URIs)
(SEED ?v q) (?v a variable)
Example: ( (lpe1 , P1) AND (SEED ?x q2) )
where P1 is: ((?x,p,?y) FILTER (?y > 3))
lpe := ε | lp | lpe/lpe | lpe|lpe | lpe* | [lpe] | (?v, q)
24. LDQL: A Query Language for the Web of Linked Data – Olaf Hartig and Jorge Peréz – ISWC 2015 24
Summary
● Query language for the Web of Linked Data
● Main feature: separation of ...
… navigational components to select
regions of the Web of Linked Data, and
… query patterns to be matched in the
data of the selected regions
● In the paper we study LDQL
– Web-safeness property
– LDQL is strictly more expressive
than the major existing proposals