In ontology alignment, there is no single best performing matching algorithm for every matching problem. Thus, most modern matching systems combine several base matchers and aggregate their results into a final alignment. This combination is often based on simple voting or averaging, or uses existing matching problems for learning a combination policy in a supervised setting. In this paper, we present the COMMAND matching system, an unsupervised method for combining base matchers, which uses anomaly detection to produce an alignment from the results delivered by several base matchers. The basic idea of our approach is that in a large set of potential mapping candidates, the scarce actual mappings should be visible as anomalies against the majority of non-mappings. The approach is evaluated on different OAEI datasets and shows a competitive performance with state-of-the-art systems.
Detecting Incorrect Numerical Data in DBpediaHeiko Paulheim
DBpedia is a central hub of Linked Open Data (LOD). Being
based on crowd-sourced contents and heuristic extraction methods, it is not free of errors. In this paper, we study the application of unsupervised numerical outlier detection methods to DBpedia, using Interquantile Range (IQR), Kernel Density Estimation (KDE), and various dispersion estimators, combined with different semantic grouping methods. Our approach reaches 87% precision, and has lead to the identification of 11 systematic errors in the DBpedia extraction framework.
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.
Serving DBpedia with DOLCE - More Than Just Adding a Cherry on TopHeiko Paulheim
Large knowledge bases, such as DBpedia, are most often created heuristically due to scalability issues. In the building process, both random as well as systematic errors may occur. In this paper, we focus on finding systematic errors, or anti-patterns, in DBpedia. We show that by aligning the DBpedia ontology to the foundational ontology DOLCE-Zero, and by combining reasoning and clustering of the reasoning results, errors affecting millions of statements can be identified at a minimal workload for the knowledge base designer.
Identifying Wrong Links between Datasets by Multi-dimensional Outlier DetectionHeiko Paulheim
Links between datasets are an essential ingredient of Linked Open Data. Since the manual creation of links is expensive at large-scale, link sets are often created using heuristics, which may lead to errors. In this paper, we propose an unsupervised approach for finding erroneous links. We represent each link as a feature vector in a higher dimensional vector space, and find wrong links by means of different multi-dimensional outlier detection methods. We show how the approach can be implemented in the RapidMiner platform using only off-the-shelf components, and present a first evaluation with real-world datasets from the Linked Open Data cloud showing promising results, with an F-measure of up to 0.54, and an area under the ROC curve of up to 0.86.
Many students spend enormous amounts of their time engaged with their computers, accepting of course that mobile devices are simply computers of a different form factor. Engaged with the social networks, utilizing computer platforms to source and share content of various forms, their contributions of “data” into what is the cloud, and in many cases a void, is enormous. What community and career benefit might result from those students spending some of their time contributing chemistry related data to the world? What challenges lie in the way of their participation and how might participating have a positive, or negative impact on their future career. The Royal Society of Chemistry hosts a number of chemistry data platforms to which students can actively contribute and for which their participation can be measured. Moreover the RSC’s micropublishing platform allows chemists to learn how to write up their scientific work, obtain review from their peers and chemistry professors in a non-threatening environment and produce an online published work in less than day that is both citable and available as a shared resource for the community. This presentation will demonstrate how to participate and encourage engagement from students early in their education. There are no longer any technology barriers to the sharing of the majority of chemistry related data.
Universidad nacional de san agustín y su historia de vergüenzalerikrat
Esta institución, ha avalado conductas prepotentes y autoritarias; ha avalado y hasta homenajeado, a autoridades del claustro que han ejercido un trato discriminatorio en agravio de profesores, autoridades que han segregado de la carrera docente a algunos profesores, simplemente por apetitos personales, y violando toda norma moral.
Detecting Incorrect Numerical Data in DBpediaHeiko Paulheim
DBpedia is a central hub of Linked Open Data (LOD). Being
based on crowd-sourced contents and heuristic extraction methods, it is not free of errors. In this paper, we study the application of unsupervised numerical outlier detection methods to DBpedia, using Interquantile Range (IQR), Kernel Density Estimation (KDE), and various dispersion estimators, combined with different semantic grouping methods. Our approach reaches 87% precision, and has lead to the identification of 11 systematic errors in the DBpedia extraction framework.
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.
Serving DBpedia with DOLCE - More Than Just Adding a Cherry on TopHeiko Paulheim
Large knowledge bases, such as DBpedia, are most often created heuristically due to scalability issues. In the building process, both random as well as systematic errors may occur. In this paper, we focus on finding systematic errors, or anti-patterns, in DBpedia. We show that by aligning the DBpedia ontology to the foundational ontology DOLCE-Zero, and by combining reasoning and clustering of the reasoning results, errors affecting millions of statements can be identified at a minimal workload for the knowledge base designer.
Identifying Wrong Links between Datasets by Multi-dimensional Outlier DetectionHeiko Paulheim
Links between datasets are an essential ingredient of Linked Open Data. Since the manual creation of links is expensive at large-scale, link sets are often created using heuristics, which may lead to errors. In this paper, we propose an unsupervised approach for finding erroneous links. We represent each link as a feature vector in a higher dimensional vector space, and find wrong links by means of different multi-dimensional outlier detection methods. We show how the approach can be implemented in the RapidMiner platform using only off-the-shelf components, and present a first evaluation with real-world datasets from the Linked Open Data cloud showing promising results, with an F-measure of up to 0.54, and an area under the ROC curve of up to 0.86.
Many students spend enormous amounts of their time engaged with their computers, accepting of course that mobile devices are simply computers of a different form factor. Engaged with the social networks, utilizing computer platforms to source and share content of various forms, their contributions of “data” into what is the cloud, and in many cases a void, is enormous. What community and career benefit might result from those students spending some of their time contributing chemistry related data to the world? What challenges lie in the way of their participation and how might participating have a positive, or negative impact on their future career. The Royal Society of Chemistry hosts a number of chemistry data platforms to which students can actively contribute and for which their participation can be measured. Moreover the RSC’s micropublishing platform allows chemists to learn how to write up their scientific work, obtain review from their peers and chemistry professors in a non-threatening environment and produce an online published work in less than day that is both citable and available as a shared resource for the community. This presentation will demonstrate how to participate and encourage engagement from students early in their education. There are no longer any technology barriers to the sharing of the majority of chemistry related data.
Universidad nacional de san agustín y su historia de vergüenzalerikrat
Esta institución, ha avalado conductas prepotentes y autoritarias; ha avalado y hasta homenajeado, a autoridades del claustro que han ejercido un trato discriminatorio en agravio de profesores, autoridades que han segregado de la carrera docente a algunos profesores, simplemente por apetitos personales, y violando toda norma moral.
5 самых вкусных способов заработка в Youtube - Заработок в сети без вложений Лайфхак - Вебинары
Вы сможете не только работать, но и консультировать в этой теме, а так же получите лидерство в своей нише. Освоение методики быстрой раскрутки, для тех, кому результаты нужны еще вчера. Владение инструментами автоматизации, которые минимизируют личное участие и ежедневную рутину. Умение использовать продвинутые техники видеовирусы, платный посев видео, изучить в теории запрещенные методы продвижения.
Thanks everyone who attended this webcast. Special thanks to Launch Podcast, Launch Workplaces and the 280 Group for their support. We hope you enjoyed it.
You can view or listen to the discussion on demand using the links provided.
BoldPM Insights: "Why Smart, Connected Devices Are Transforming Businesses"
Thu, May 5, 1:00 - 2:00 p.m. EDT
Slideshare: http://bit.ly/bldpmi0505
If you like this webcast content, be sure to like it and share it with others. We hope you can use this information to implement changes within your organization.
Interested in actionable business intelligence? Sign up for upcoming BoldPM Insights webcasts here: http://bit.ly/boldpminsights.
Description
Smart, connected devices are completely changing the game.
The evolution of products into intelligent, connected devices, which are increasingly embedded in broader systems, is radically reshaping companies and competition. This is forcing companies to redefine their industries and rethink nearly everything they do, beginning with their strategies.
Companies will need new types of relationships with customers. They will need new product capabilities, infrastructure, and processes; entirely new structures, functions, and new forms of cross-functional collaboration.
In this webinar, Hector Del Castillo discusses how smart, connected products are impacting business strategies and transforming the entire value chain.
Esta es una pequeña presentación que habla acerca del impacto causado del "marketing" en los jóvenes, sus estrategias, formas de evitarlo, temas que aborda, entre otros aspectos desarrollados por el equipo.
In this talk, I compare and contrast scientific method for hypothesis testing commonly used in distributed systems, cloud computing, software engineering and control theory. For each method I highlight the principle, pros, cons and some good usage examples.
(Talk given at Research Day, Umeå University on 2016-05-24.)
An experimental comparison of globally-optimal data de-identification algorithmsarx-deidentifier
Collaboration and data sharing have become core elements of biomedical research. At the same time, there is a growing understanding of privacy threats related to data sharing, especially when sensitive data from distributed sources become available for linkage. Statistical disclosure control comprises well-known data anonymization techniques that allow the protection of data by introducing fuzziness. To protect datasets from different types of threats, different privacy criteria are commonly implemented. Data anonymization is an important measure, but it is computationally complex, and it can significantly reduce the expressiveness of data. To attenuate these problems, a number of algorithms has been proposed, which aim at increasing data quality or improving efficiency. Previous evaluations of such algorithms lack a systematic approach, as they focus on specific algorithms, specific privacy criteria, and specific runtime environments. Therefore, it is difficult for decision makers to decide which algorithm is best suited for their requirements. As a first step towards a comprehensive and systematic evaluation of anonymity algorithms, we report on our ongoing efforts for providing an open source benchmark. In this contribution, we focus on optimal algorithms utilizing global recoding with full-domain generalization. We present a systematic evaluation of domain-specific algorithms and generic search methods for a broad set of privacy criteria, including k-anonymity, l-diversity, t-closeness and d-presence, and their use in multiple real-world datasets. Our results show that there is no single solution fitting all needs, and that generic search methods can outperform highly specialized algorithms.
Worked examples of sampling uncertainty evaluationGH Yeoh
ISO/IEC 17025:2017 laboratory accreditation standard has expanded its requirement for measurement uncertainty to include both sampling and analytical uncertainties.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
5 самых вкусных способов заработка в Youtube - Заработок в сети без вложений Лайфхак - Вебинары
Вы сможете не только работать, но и консультировать в этой теме, а так же получите лидерство в своей нише. Освоение методики быстрой раскрутки, для тех, кому результаты нужны еще вчера. Владение инструментами автоматизации, которые минимизируют личное участие и ежедневную рутину. Умение использовать продвинутые техники видеовирусы, платный посев видео, изучить в теории запрещенные методы продвижения.
Thanks everyone who attended this webcast. Special thanks to Launch Podcast, Launch Workplaces and the 280 Group for their support. We hope you enjoyed it.
You can view or listen to the discussion on demand using the links provided.
BoldPM Insights: "Why Smart, Connected Devices Are Transforming Businesses"
Thu, May 5, 1:00 - 2:00 p.m. EDT
Slideshare: http://bit.ly/bldpmi0505
If you like this webcast content, be sure to like it and share it with others. We hope you can use this information to implement changes within your organization.
Interested in actionable business intelligence? Sign up for upcoming BoldPM Insights webcasts here: http://bit.ly/boldpminsights.
Description
Smart, connected devices are completely changing the game.
The evolution of products into intelligent, connected devices, which are increasingly embedded in broader systems, is radically reshaping companies and competition. This is forcing companies to redefine their industries and rethink nearly everything they do, beginning with their strategies.
Companies will need new types of relationships with customers. They will need new product capabilities, infrastructure, and processes; entirely new structures, functions, and new forms of cross-functional collaboration.
In this webinar, Hector Del Castillo discusses how smart, connected products are impacting business strategies and transforming the entire value chain.
Esta es una pequeña presentación que habla acerca del impacto causado del "marketing" en los jóvenes, sus estrategias, formas de evitarlo, temas que aborda, entre otros aspectos desarrollados por el equipo.
In this talk, I compare and contrast scientific method for hypothesis testing commonly used in distributed systems, cloud computing, software engineering and control theory. For each method I highlight the principle, pros, cons and some good usage examples.
(Talk given at Research Day, Umeå University on 2016-05-24.)
An experimental comparison of globally-optimal data de-identification algorithmsarx-deidentifier
Collaboration and data sharing have become core elements of biomedical research. At the same time, there is a growing understanding of privacy threats related to data sharing, especially when sensitive data from distributed sources become available for linkage. Statistical disclosure control comprises well-known data anonymization techniques that allow the protection of data by introducing fuzziness. To protect datasets from different types of threats, different privacy criteria are commonly implemented. Data anonymization is an important measure, but it is computationally complex, and it can significantly reduce the expressiveness of data. To attenuate these problems, a number of algorithms has been proposed, which aim at increasing data quality or improving efficiency. Previous evaluations of such algorithms lack a systematic approach, as they focus on specific algorithms, specific privacy criteria, and specific runtime environments. Therefore, it is difficult for decision makers to decide which algorithm is best suited for their requirements. As a first step towards a comprehensive and systematic evaluation of anonymity algorithms, we report on our ongoing efforts for providing an open source benchmark. In this contribution, we focus on optimal algorithms utilizing global recoding with full-domain generalization. We present a systematic evaluation of domain-specific algorithms and generic search methods for a broad set of privacy criteria, including k-anonymity, l-diversity, t-closeness and d-presence, and their use in multiple real-world datasets. Our results show that there is no single solution fitting all needs, and that generic search methods can outperform highly specialized algorithms.
Worked examples of sampling uncertainty evaluationGH Yeoh
ISO/IEC 17025:2017 laboratory accreditation standard has expanded its requirement for measurement uncertainty to include both sampling and analytical uncertainties.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Dowhy: An end-to-end library for causal inferenceAmit Sharma
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step.
A talk at ESSA@Work, TUHH (Technical University of Hamburg), 24th Nov 2017.
Abstract: Simulation models can only be justified with respect to the models purpose or aim. The talk looks at six common purposes for modelling: prediction, explanation, analogy, theoretical exposition, description, and illustration. Each of these is briefly described, with an example and an brief analysis of the risks to achieving these, and hence how they should be demonstrated. The importance of being explicitly clear about the model purpose is repeatedly emphasised.
Knowledge Graph Generation from Wikipedia in the Age of ChatGPT: Knowledge ...Heiko Paulheim
In the past years, sophisticated methods for extracting knowledge graphs from Wikipedia, like DBpedia,YAGO, and CaLiGraph, have been developed. In this talk, I revisit some of these methods and examine if and how they can be replaced by prompting a large language model like ChatGPT.
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.
RDF2vec is a method for creating embeddings vectors for entities in knowledge graphs. In this talk, I introduce the basic idea of RDF2vec, as well as the latest extensions developments, like the use of different walk strategies, the flavour of order-aware RDF2vec, RDF2vec for dynamic knowledge graphs, and more.
Using knowledge graphs in data mining typically requires a propositional, i.e., vector-shaped representation of entities. RDF2vec is an example for generating such vectors from knowledge graphs, relying on random walks for extracting pseudo-sentences from a graph, and utilizing word2vec for creating embedding vectors from those pseudo-sentences. In this talk, I will give insights into the idea of RDF2vec, possible application areas, and recently developed variants incorporating different walk strategies and training variations.
Knowledge Matters! The Role of Knowledge Graphs in Modern AI SystemsHeiko Paulheim
AI is not just about machine learning, it also requires knowledge about the world. In this talk, I give an introduction on knowledge graphs, how they are built at scale, and how they are used in modern AI systems.
This presentation shows approaches for knowledge graph construction from Wikipedia and other Wikis that go beyond the "one entity per page" paradigm. We see CaLiGraph, which extracts entities from categories and listings, as well as DBkWik, which extracts and integrates information from thousands of Wikis.
Using Knowledge Graphs in Data Science - From Symbolic to Latent Representati...Heiko Paulheim
Knowledge Graphs are often used as a symbolic representation mechanism for representing knowledge in data intensive applications, both for integrating corporate knowledge as well as for providing general, cross-domain knowledge in public knowledge graphs such as Wikidata. As such, they have been identified as a useful way of injecting background knowledge in data analysis processes. To fully harness the potential of knowledge graphs, latent representations of entities in the graphs, so called knowledge graph embeddings, show superior performance, but sacrifice one central advantage of knowledge graphs, i.e., the explicit symbolic knowledge representations. In this talk, I will shed some light on the usage of knowledge graphs and embeddings in data analysis, and give an outlook on research directions which aim at combining the best of both worlds.
Beyond DBpedia and YAGO – The New Kids on the Knowledge Graph BlockHeiko Paulheim
Starting with Cyc in the 1980s, the collection of general
knowledge in machine interpretable form has been considered a valuable ingredient in intelligent and knowledge intensive applications. Notable contributions in the field include the Wikipedia-based datasets DBpedia and YAGO, as well as the collaborative knowledge base Wikidata. Since Google has coined the term in 2012, they are most often referred to as knowledge graphs. Besides such open knowledge graphs, many companies have started using corporate knowledge graphs as a means of information representation.
In this talk, I will look at two ongoing projects related to the extraction of knowledge graphs from Wikipedia and other Wikis. The first new dataset, CaLiGraph, aims at the generation of explicit formal definitions from categories, and the extraction of new instances from list pages. In its current release, CaLiGraph contains 200k axioms defining classes,
and more than 7M typed instances. In the second part, I will look at the transfer of the DBpedia approach to a multitude of arbitrary Wikis. The first such prototype, DBkWik, extracts data from Fandom, a Wiki farm hosting more than 400k different Wikis on various topics. Unlike DBpedia, which relies on a larger user base for crowdsourcing an explicit schema and extraction rules, and the "one-page-per-entity" assumption, DBkWik has to address various challenges in the fields of schema learning and data integration. In its current release, DBkWik contains more than 11M entities, and has been found to be highly complementary to DBpedia.
From Wikipedia to Thousands of Wikis – The DBkWik Knowledge GraphHeiko Paulheim
From a bird eye's view, the DBpedia Extraction Framework takes a MediaWiki dump as input, and turns it into a knowledge graph. In this talk, I discuss the creation of the DBkWik knowledge graph by applying the DBpedia Extraction Framework to thousands of Wikis.
The original Semantic Web vision foresees to describe entities in a way that the meaning can be interpreted both by machines and humans. Following that idea, large-scale knowledge graphs capturing a significant portion of knowledge have been developed. In the recent past, vector space embeddings of semantic web knowledge graphs - i.e., projections of a knowledge graph into a lower-dimensional, numerical feature
space (a.k.a. latent feature space) - have been shown to yield superior performance in many tasks, including relation prediction, recommender systems, or the enrichment of predictive data mining tasks. At the same time, those projections describe an entity as a numerical vector, without
any semantics attached to the dimensions. Thus, embeddings are as far from the original Semantic Web vision as can be. As a consequence, the results achieved with embeddings - as impressive as they are in terms of quantitative performance - are most often not interpretable, and it is hard to obtain a justification for a prediction, e.g., an explanation why an item has been suggested by a recommender system. In this paper, we make a claim for semantic embeddings and discuss possible ideas towards their construction.
Knowledge graphs are used in various applications and have
been widely analyzed. A question that is not very well researched is: what is the price of their production? In this paper, we propose ways to estimate the cost of those knowledge graphs. We show that the cost of manually curating a triple is between $2 and $6, and that the cost for automatically created knowledge graphs is by a factor of 15 to 150 cheaper (i.e., 1c to 15c per statement). Furthermore, we advocate for taking cost into account as an evaluation metric, showing the correspondence between cost per triple and semantic validity as an example.
Machine Learning with and for Semantic Web Knowledge GraphsHeiko Paulheim
Large-scale cross-domain knowledge graphs, such as DBpedia or Wikidata, are some of the most popular and widely used datasets of the Semantic Web. In this paper, we introduce some of the most popular knowledge graphs on the Semantic Web. We discuss how machine learning is used to improve those knowledge graphs, and how they can be exploited as background knowledge in popular machine learning tasks, such as recommender systems.
Weakly Supervised Learning for Fake News Detection on TwitterHeiko Paulheim
The problem of automatic detection of fake news insocial media, e.g., on Twitter, has recently drawn some attention. Although, from a technical perspective, it can be regarded
as a straight-forward, binary classification problem, the major
challenge is the collection of large enough training corpora, since manual annotation of tweets as fake or non-fake news is an expensive and tedious endeavor. In this paper, we discuss a weakly supervised approach, which automatically collects a large-scale, but very noisy training dataset comprising hundreds of thousands of tweets. During collection, we automatically label tweets by their source, i.e., trustworthy or untrustworthy source, and train a classifier on this dataset. We then use that classifier for a different classification target, i.e., the classification of fake and non-fake tweets. Although the labels are not accurate according to the new classification target (not all tweets by an untrustworthy source need to be fake news, and vice versa), we show that despite this unclean inaccurate dataset, it is possible to detect fake news with an F1 score of up to 0.9.
Knowledge Graphs, such as DBpedia, YAGO, or Wikidata, are valuable resources for building intelligent applications like data analytics tools or recommender systems. Understanding what is in those knowledge graphs is a crucial prerequisite for selecing a Knowledge Graph for a task at hand. Hence, Knowledge Graph profiling - i.e., quantifying the structure and contents of knowledge graphs, as well as their differences - is essential for fully utilizing the power of Knowledge Graphs. In this paper, I will discuss methods for Knowledge Graph profiling, depict crucial differences of the big, well-known Knowledge Graphs, like DBpedia, YAGO, and Wikidata, and throw a glance at current developments of new, complementary Knowledge Graphs such as DBkWik and WebIsALOD.
How are Knowledge Graphs created?
What is inside public Knowledge Graphs?
Addressing typical problems in Knowledge Graphs (errors, incompleteness)
New Knowledge Graphs: WebIsALOD, DBkWik
Data-driven Joint Debugging of the DBpedia Mappings and OntologyHeiko Paulheim
DBpedia is a large-scale, cross-domain knowledge graph extracted from Wikipedia. For the extraction, crowd-sourced mappings from Wikipedia infoboxes to the DBpedia ontology are utilized. In this process, different problems may arise: users may create wrong and/or inconsistent mappings, use the ontology in an unforeseen way, or change the ontology without considering all possible consequences. In this paper, we present a data-driven approach to discover problems in mappings as well as in the ontology and its usage in a joint, data-driven process. We show both quantitative and qualitative results about the problems identified, and derive proposals for altering mappings and refactoring the DBpedia ontology.
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
2. 10/13/15 Alexander C. Müller, Heiko Paulheim 2
Motivation
• Most high-performing matching systems use multiple matchers
• How to combine multiple matchers into a single result?
• Common approaches (selection of)
– average, maximum, minimum matching score
– voting
– expert modeled weights (0.4m1 + 0.3m2 + 0.3m3)
– supervised learning
• Proposal:
– use anomaly detection as an unsupervised aggregation method
3. 10/13/15 Alexander C. Müller, Heiko Paulheim 3
Idea
• Common definitions anomaly/outlier detection:
– Outlier or anomaly detection methods are used to “that appear to
deviate markedly from other members of the same sample", i.e.
– “that appear to be inconsistent with the remainder of the data"
• Rationale:
– for two ontologies with n and m concepts, there are nxm candidates
– the majority are non-matches
– the actual matches are a minority (that differ markedly from the rest)
– so, we should be able to identify them as outliers
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Outlier Detection in a Nutshell
• Given a set of instances as feature vectors
– outlier detection assigns an outlier score to each instance
– higher outlier scores ↔ higher degree of outlierness
• Common approaches
– distance based
– density based
– clustering based
– model based
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Aggregating Matchers via Anomaly Detection
• We run a set of base matchers
• Each base matcher score becomes a numerical feature
• Thus, out feature vectors consist of individual matching scores
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Aggregating Matchers via Anomaly Detection
• Example from the conference dataset
– note: reduced to two dimensions!
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COMMAND: Full Pipeline
• Run set of element-based matchers
– find non-correlated subset
• Run set of structure-based matchers on that subset
• Collect all results into feature vectors
• Perform dimensionality reduction
– removing correlated matchers
– Principal Component Analysis
• Run outlier detection
• Perform optional repair step
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COMMAND: Full Pipeline
• Run set of element-based matchers (28 different ones)
– find non-correlated subset
• Run set of structure-based matchers (five different ones)
on that subset
– Collect all results into feature vectors
• Perform dimensionality reduction
– removing correlated matchers
– Principal Component Analysis
• Run outlier detection
• Normalize outlier scores
• Select mapping candidates
• Perform optional repair setp
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COMMAND: Results
• Good results on biblio benchmark dataset
– up to 67% F-measure
• Median results on conference
– up to 68% F-measure
• Difficulties on anatomy dataset
– only a subset of matchers could be run for scalability reasons
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Discussion and Conclusion
• Proof of Concept
– Anomaly detection is suitable
for matcher aggregation
– non-trivial combination of
matcher scores (PCA, outlier score)
– automatic selection of a suitable
subset of matchers
• Future work
– address scalability issues
– try more anomaly detection
approaches