A statistical and schema independent approach to determine equivalent properties between linked datasets. The approach utilizes interlinking between datasets and property extensions to understand the equivalence of properties.
Top-K Dominating Queries on Incomplete Data with Prioritiesijtsrd
Top-K dominating query returns the k objects that are dominated in a dataset. Finding dominated elements on incomplete dataset is more complicated than in case of complete dataset. In the real- time datasets the dataset can be incomplete due to various reasons such as data loss, privacy preservation or awareness problem etc. In this paper we aims to find top-k elements from an incomplete dataset by providing priority values to each dimension in the data object. Skyline based algorithm is applied for that purpose. Since the priority value is used while determining the dominance this method return the most suitable and efficient result than other previous methods. The output will be more preferable according to the users purpose. Dr. Prabha Shreeraj Nair | Prof. Dr. G. K. Awari"Top-K Dominating Queries on Incomplete Data with Priorities" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7056.pdf http://www.ijtsrd.com/computer-science/other/7056/top-k-dominating-queries-on-incomplete--data-with-priorities/dr-prabha-shreeraj-nair
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATAcsandit
This work presents a novel ranking scheme for structured data. We show how to apply the
notion of typicality analysis from cognitive science and how to use this notion to formulate the
problem of ranking data with categorical attributes. First, we formalize the typicality query
model for relational databases. We adopt Pearson correlation coefficient to quantify the extent
of the typicality of an object. The correlation coefficient estimates the extent of statistical
relationships between two variables based on the patterns of occurrences and absences of their
values. Second, we develop a top-k query processing method for efficient computation. TPFilter
prunes unpromising objects based on tight upper bounds and selectively joins tuples of highest
typicality score. Our methods efficiently prune unpromising objects based on upper bounds.
Experimental results show our approach is promising for real data.
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners ...Edureka!
This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial:
1. Variables
2. Data types
3. Operators
4. Conditional Statements
5. Loops
6. Strings
7. Functions
This teaches how to implement a data science project using R.
This video tutorial teaches how to implement a data science project (R version). Youtube video link https://goo.gl/GZmJpC PowerPoint Slides: https://goo.gl/EQPdZE. R Script codes & datasets: https://goo.gl/wRFohE
Implementing a data_science_project (Python Version)_part1Dr Sulaimon Afolabi
This teaches how to implement a data science project using Python.
You can watch the youtube video via this link https://goo.gl/Mi4aJH
Jupyter notebook: https://goo.gl/AxRMe3
Top-K Dominating Queries on Incomplete Data with Prioritiesijtsrd
Top-K dominating query returns the k objects that are dominated in a dataset. Finding dominated elements on incomplete dataset is more complicated than in case of complete dataset. In the real- time datasets the dataset can be incomplete due to various reasons such as data loss, privacy preservation or awareness problem etc. In this paper we aims to find top-k elements from an incomplete dataset by providing priority values to each dimension in the data object. Skyline based algorithm is applied for that purpose. Since the priority value is used while determining the dominance this method return the most suitable and efficient result than other previous methods. The output will be more preferable according to the users purpose. Dr. Prabha Shreeraj Nair | Prof. Dr. G. K. Awari"Top-K Dominating Queries on Incomplete Data with Priorities" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7056.pdf http://www.ijtsrd.com/computer-science/other/7056/top-k-dominating-queries-on-incomplete--data-with-priorities/dr-prabha-shreeraj-nair
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATAcsandit
This work presents a novel ranking scheme for structured data. We show how to apply the
notion of typicality analysis from cognitive science and how to use this notion to formulate the
problem of ranking data with categorical attributes. First, we formalize the typicality query
model for relational databases. We adopt Pearson correlation coefficient to quantify the extent
of the typicality of an object. The correlation coefficient estimates the extent of statistical
relationships between two variables based on the patterns of occurrences and absences of their
values. Second, we develop a top-k query processing method for efficient computation. TPFilter
prunes unpromising objects based on tight upper bounds and selectively joins tuples of highest
typicality score. Our methods efficiently prune unpromising objects based on upper bounds.
Experimental results show our approach is promising for real data.
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners ...Edureka!
This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial:
1. Variables
2. Data types
3. Operators
4. Conditional Statements
5. Loops
6. Strings
7. Functions
This teaches how to implement a data science project using R.
This video tutorial teaches how to implement a data science project (R version). Youtube video link https://goo.gl/GZmJpC PowerPoint Slides: https://goo.gl/EQPdZE. R Script codes & datasets: https://goo.gl/wRFohE
Implementing a data_science_project (Python Version)_part1Dr Sulaimon Afolabi
This teaches how to implement a data science project using Python.
You can watch the youtube video via this link https://goo.gl/Mi4aJH
Jupyter notebook: https://goo.gl/AxRMe3
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Analysis of different similarity measures: SimrankAbhishek Mungoli
SimRank exploits object-to-object relationships very well and finds out the similarity between two objects.
We have used it in our project to find similar reasearch papers from DBLP dataset (DBLP Dataset provides a comprehensive list of research papers in computer science domain).
SimRank is a generic approach and its basic idea can also be applied to other domain of interests as well.
Distribution Similarity based Data Partition and Nearest Neighbor Search on U...Editor IJMTER
Databases are build with the fixed number of fields and records. Uncertain database contains a
different number of fields and records. Clustering techniques are used to group up the relevant records
based on the similarity values. The similarity measures are designed to estimate the relationship between
the transactions with fixed attributes. The uncertain data similarity is estimated using similarity
measures with some modifications.
Clustering on uncertain data is one of the essential tasks in mining uncertain data. The existing
methods extend traditional partitioning clustering methods like k-means and density-based clustering
methods like DBSCAN to uncertain data. Such methods cannot handle uncertain objects. Probability
distributions are essential characteristics of uncertain objects have not been considered in measuring
similarity between uncertain objects.
The customer purchase transaction data is analyzed using uncertain data clustering scheme. The
density based clustering mechanism is used for the uncertain data clustering process. This model
produces results with minimum accuracy levels. The clustering technique is improved with distribution
based similarity model for uncertain data. The nearest neighbor search technique is applied on the
distribution based data environment. The system is designed using java as a front end and oracle as a
back end.
Document ranking using qprp with concept of multi dimensional subspacePrakash Dubey
qPRP is the new model for IR. Existing qPRP approach considers term present in different section of document equally. Our belief is that representing the document as multidimensional subspace will give better result. Because each section of document as its own importance.
Tech Jobs Interviews Preparation - GeekGap Webinar #1
Part 1 - Algorithms & Data Structures
What is an algorithm?
What is a data structure (DS)?
Why study algorithms & DS?
How to assess good algorithms?
Algorithm & DS interviews structure
Case study: Binary Search
2 Binary Search variants
Part 2 - System Design
What is system design?
Why study system design?
System design interviews structure
Case study: ERD with Lucidchart
Demo Time: SQLAlchemy
Githu Repo: http://bit.ly/gg-io-webinar-1-github
by www.geekgap.io
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Rethinking Data-Intensive Science Using Scalable Analytics Systems fnothaft
Presentation from SIGMOD 2015. With Matt Massie, Timothy Danford, Zhao Zhang, Uri Laserson, Carl Yeksigian, Jey Kottalam, Arun Ahuja, Jeff Hammerbacher, Michael Linderman, Michael J. Franklin, Anthony D. Joseph, David A. Patterson. Paper at http://dl.acm.org/citation.cfm?id=2742787.
Automated building of taxonomies for search enginesBoris Galitsky
We build a taxonomy of entities which is intended to improve the relevance of search engine in a vertical domain. The taxonomy construction process starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (their generalization) is applied to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration.
Taxonomy and paragraph-level syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. Proposed algorithm is implemented as a part of Apache OpenNLP.Similarity project.
Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Henson, and Amit Sheth, 'Trust Networks', In: 5th Indian International Conference on Artificial Intelligence (IICAI-11), December 14-16, 2011 (invited tutorial).
Amit Sheth, "Semantic Interoperability and Information Brokering in Global Information Systems," Keynote given at IEEE Meta-Data, Bathesda, MD, April 6 1999.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Analysis of different similarity measures: SimrankAbhishek Mungoli
SimRank exploits object-to-object relationships very well and finds out the similarity between two objects.
We have used it in our project to find similar reasearch papers from DBLP dataset (DBLP Dataset provides a comprehensive list of research papers in computer science domain).
SimRank is a generic approach and its basic idea can also be applied to other domain of interests as well.
Distribution Similarity based Data Partition and Nearest Neighbor Search on U...Editor IJMTER
Databases are build with the fixed number of fields and records. Uncertain database contains a
different number of fields and records. Clustering techniques are used to group up the relevant records
based on the similarity values. The similarity measures are designed to estimate the relationship between
the transactions with fixed attributes. The uncertain data similarity is estimated using similarity
measures with some modifications.
Clustering on uncertain data is one of the essential tasks in mining uncertain data. The existing
methods extend traditional partitioning clustering methods like k-means and density-based clustering
methods like DBSCAN to uncertain data. Such methods cannot handle uncertain objects. Probability
distributions are essential characteristics of uncertain objects have not been considered in measuring
similarity between uncertain objects.
The customer purchase transaction data is analyzed using uncertain data clustering scheme. The
density based clustering mechanism is used for the uncertain data clustering process. This model
produces results with minimum accuracy levels. The clustering technique is improved with distribution
based similarity model for uncertain data. The nearest neighbor search technique is applied on the
distribution based data environment. The system is designed using java as a front end and oracle as a
back end.
Document ranking using qprp with concept of multi dimensional subspacePrakash Dubey
qPRP is the new model for IR. Existing qPRP approach considers term present in different section of document equally. Our belief is that representing the document as multidimensional subspace will give better result. Because each section of document as its own importance.
Tech Jobs Interviews Preparation - GeekGap Webinar #1
Part 1 - Algorithms & Data Structures
What is an algorithm?
What is a data structure (DS)?
Why study algorithms & DS?
How to assess good algorithms?
Algorithm & DS interviews structure
Case study: Binary Search
2 Binary Search variants
Part 2 - System Design
What is system design?
Why study system design?
System design interviews structure
Case study: ERD with Lucidchart
Demo Time: SQLAlchemy
Githu Repo: http://bit.ly/gg-io-webinar-1-github
by www.geekgap.io
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Rethinking Data-Intensive Science Using Scalable Analytics Systems fnothaft
Presentation from SIGMOD 2015. With Matt Massie, Timothy Danford, Zhao Zhang, Uri Laserson, Carl Yeksigian, Jey Kottalam, Arun Ahuja, Jeff Hammerbacher, Michael Linderman, Michael J. Franklin, Anthony D. Joseph, David A. Patterson. Paper at http://dl.acm.org/citation.cfm?id=2742787.
Automated building of taxonomies for search enginesBoris Galitsky
We build a taxonomy of entities which is intended to improve the relevance of search engine in a vertical domain. The taxonomy construction process starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (their generalization) is applied to the search results for existing entities to form commonalities between them. These commonality expressions then form parameters of existing entities, and are turned into new entities at the next learning iteration.
Taxonomy and paragraph-level syntactic generalization are applied to relevance improvement in search and text similarity assessment. We conduct an evaluation of the search relevance improvement in vertical and horizontal domains and observe significant contribution of the learned taxonomy in the former, and a noticeable contribution of a hybrid system in the latter domain. We also perform industrial evaluation of taxonomy and syntactic generalization-based text relevance assessment and conclude that proposed algorithm for automated taxonomy learning is suitable for integration into industrial systems. Proposed algorithm is implemented as a part of Apache OpenNLP.Similarity project.
Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Henson, and Amit Sheth, 'Trust Networks', In: 5th Indian International Conference on Artificial Intelligence (IICAI-11), December 14-16, 2011 (invited tutorial).
Amit Sheth, "Semantic Interoperability and Information Brokering in Global Information Systems," Keynote given at IEEE Meta-Data, Bathesda, MD, April 6 1999.
Amit Sheth, 'Semantic Computing in Real-World: Vertical and Horizontal application, within Enterprise and on the Web, ' Panel Presentation at International Conference on Semantic Computing (ICSC2011), Palo Alto, CA, September 20, 2011.
Talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
Amit Sheth, Pramod Anantharam, Krishnaprasad Thirunarayan, "kHealth: Proactive Personalized Actionable Information for Better Healthcare", Workshop on Personal Data Analytics in the Internet of Things at VLDB2014, Hangzhou, China, September 5, 2014.
Accompanying Video: http://youtu.be/pqcbwGYHPuc
Paper: http://www.knoesis.org/library/resource.php?id=2008
Krishnaprasad Thirunarayan and Amit Sheth: Semantics-empowered Approaches to Big Data Processing for Physical-Cyber-Social Applications, In: Proceedings of AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013.
With the rapid proliferation of mobile phones, social media, and sensors, it is critical to collect and convert big data so generated into actionable information that is relevant for decision making. In this session, we explore challenges and approaches for synthesizing relevant background knowledge and inferences that can enable smart healthcare and ultimately benefit community at large.
Paper: http://www.knoesis.org/library/resource.php?id=1903
Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is
increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.
Presentation given by Chris Welty (IBM Research) at Knoesis. We get the permission to upload this presentation from Chris Welty. Event details are at: http://j.mp/Welty-at-Knoesis and the associate video is at: https://www.youtube.com/watch?v=grDKpicM5y0
Harshal Patni, "Real Time Semantic Analysis of Streaming Sensor Data," MS Thesis Defense, Kno.e.sis Center, Wright State University, Dayton OH, March 21, 2001.
More at: http://wiki.knoesis.org/index.php/SSW
Dissertation Advisor: Prof. Amit Sheth
Cursing is not uncommon during conversations in the physical world: 0.5% to 0.7% of all the words we speak are curse words, given that 1% of all the words are first-person plural pronouns (e.g., we, us, our). On social media, people can instantly chat with friends without face-to-face interaction, usually in a more public fashion and broadly disseminated through highly connected social network. Will these distinctive features of social media lead to a change in people’s curs- ing behavior? In this paper, we examine the characteristics of cursing activity on a popular social media platform – Twitter, involving the analysis of about 51 million tweets and about 14 million users. In particular, we explore a set of questions that have been recognized as crucial for understanding curs- ing in offline communications by prior studies, including the ubiquity, utility, and contextual dependencies of cursing.
Original paper: http://knoesis.org/library/resource.php?id=1937
Pavan Kapanipathi, Prateek Jain, Chitra Venkataramani, Amit Sheth, User Interests Identification on Twitter Using a Hierarchical Knowledge Base, ESWC 2014, May 2014.
Paper at: http://j.mp/user-ig
More at: http://wiki.knoesis.org/index.php/Hierarchical_Interest_Graph
Invited talk presented by Hemant Purohit (http://knoesis.org/researchers/hemant) at the NCSU workshop on IT for sustainable tourism development. The talk presents application of technology developed for crisis coordination into more general marketplace coordination via social media for helping suppliers (micro-entrepreneurs) and demanders (tourists).
The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" – i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 70 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud – as we will illustrate – are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.
This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.
The ultimate goal of a recommender system is to suggest interesting and not obvious items (e.g., products to buy, people to connect with, movies to watch, etc.) to users, based on their preferences.
The advent of the Linked Open Data (LOD) initiative in the Semantic Web gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited.
Here I present several approaches to recommender systems that leverage Linked Data knowledge bases such as DBpedia. In particular, content-based and hybrid recommendation algorithms will be discussed.
For full details about the presented approaches please refer to the full papers mentioned in this presentation.
While Hadoop is the dominant "Big Data" tool suite today, it's a first-generation technology. I discuss its strengths and weaknesses, then look at how we "should" be doing Big Data and currently-available alternative tools.
OPTIMIZATION IN ENGINE DESIGN VIA FORMAL CONCEPT ANALYSIS USING NEGATIVE ATTR...csandit
There is an exhaustive study around the area of engine design that covers different methods that try to reduce costs of production and to optimize the performance of these engines.
Mathematical methods based in statistics, self-organized maps and neural networks reach the best results in these designs but there exists the problem that configuration of these methods is
not an easy work due the high number of parameters that have to be measured.
OPTIMIZATION IN ENGINE DESIGN VIA FORMAL CONCEPT ANALYSIS USING NEGATIVE ATTR...cscpconf
There is an exhaustive study around the area of engine design that covers different methods that try to reduce costs of production and to optimize the performance of these engines. Mathematical methods based in statistics, self-organized maps and neural networks reach the best results in these designs but there exists the problem that configuration of these methods is not an easy work due the high number of parameters that have to be measured. In this work we extend an algorithm for computing implications between attributes with positive and negative values for obtaining the mixed concepts lattice and also we propose a theoretical method based in these results for engine simulators adjusting specific and different elements for obtaining optimal engine configurations.
Detecting Gender-bias from Energy Modeling Jobscapeyungahhh
Despite of many strides made by women in the workplace, workplace inequality persists, whether they come from geographical cultures or a result of workplace attrition. Unconscious (or implicit) bias can creep
into job listings and can actively or passively discourage certain applicants’ pool. The goal of this study is to understand the role of gender bias in the energy modeling job market.
Job listings and their word choices can significantly affect the application pool. This study investigates the word choices from job postings specifically for the energy modeling sector within the United States building construction industry, using web-scraped data of an online job site. The study explores the potential usage of gender-bias terminology through frequency analysis and Natural Language Processing Kit. The study also employs two machine learning methods (one supervise and one unsupervised) to test the application in this
context and evaluate its utilization.
MR – RANDOM FOREST ALGORITHM FOR DISTRIBUTED ACTION RULES DISCOVERYIJDKP
Action rules, which are the modified versions of classification rules, are one of the modern approaches for
discovering knowledge in databases. Action rules allow us to discover actionable knowledge from large
datasets. Classification rules are tailored to predict the object’s class. Whereas action rules extracted from
an information system produce knowledge in the form of suggestions of how an object can change from one
class to another more desirable class. Over the years, computer storage has become larger and also the
internet has become faster. Hence the digital data is widely spread around the world and even it is growing
in size such a way that it requires more time and space to collect and analyze them than a single computer
can handle. To produce action rules from a distributed massive data requires a distributed action rules
processing algorithm which can process the datasets in all systems in one or more clusters simultaneously
and combine them efficiently to induce single set of action rules. There has been little research on action
rules discovery in the distributed environment, which presents a challenge. In this paper, we propose a new
algorithm called MR – Random Forest Algorithm to extract the action rules in a distributed processing
environment.
MR – RANDOM FOREST ALGORITHM FOR DISTRIBUTED ACTION RULES DISCOVERYIJDKP
Action rules, which are the modified versions of classification rules, are one of the modern approaches for
discovering knowledge in databases. Action rules allow us to discover actionable knowledge from large
datasets. Classification rules are tailored to predict the object’s class. Whereas action rules extracted from
an information system produce knowledge in the form of suggestions of how an object can change from one
class to another more desirable class. Over the years, computer storage has become larger and also the
internet has become faster. Hence the digital data is widely spread around the world and even it is growing
in size such a way that it requires more time and space to collect and analyze them than a single computer
can handle. To produce action rules from a distributed massive data requires a distributed action rules
processing algorithm which can process the datasets in all systems in one or more clusters simultaneously
and combine them efficiently to induce single set of action rules. There has been little research on action
rules discovery in the distributed environment, which presents a challenge. In this paper, we propose a new
algorithm called MR – Random Forest Algorithm to extract the action rules in a distributed processing
environment.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
MARUTI SUZUKI- A Successful Joint Venture in India.pptx
Property Alignment on Linked Open Data
1. A Statistical and Schema Independent
Approach to Identify Equivalent
Properties on Linked Data
†Kno.e.sis Center
Wright State University
Dayton OH, USA
‡IBM T J Watson Research Center
Yorktown Heights
New York NY, USA
Kalpa Gunaratna†, Krishnaprasad Thirunarayan†, Prateek Jain‡, Amit Sheth†, and Sanjaya Wijeratne†
{kalpa,tkprasad,amit,sanjaya}@knoesis.org, jainpr@us.ibm.com
iSemantics 2013, Graz, Austria
5. 09/06/2013 3
Same information in different names.
Therefore, data integration for better presentation is required.
iSemantics 2013
6. Background
Statistical Equivalence of properties
Evaluation
Discussion, interesting facts, and future directions
Conclusion
09/06/2013 4
Roadmap
iSemantics 2013
7. Existing techniques for property alignment fall into three
categories.
I. Syntactic/dictionary based
– Uses string manipulation techniques, external dictionaries and
lexical databases like WordNet.
II. Schema dependent
– Uses schema information such as, domain and range, definitions.
III. Schema independent
– Uses instance level information for the alignment.
Our approach falls under schema independent.
09/06/2013 5
Background
iSemantics 2013
8. Properties capture meaning of triples and hence they are
complex in nature.
09/06/2013 6iSemantics 2013
9. Properties capture meaning of triples and hence they are
complex in nature.
Syntactic or dictionary based approaches analyze property
names for equivalence. But in LOD, name heterogeneities exist.
09/06/2013 6iSemantics 2013
10. Properties capture meaning of triples and hence they are
complex in nature.
Syntactic or dictionary based approaches analyze property
names for equivalence. But in LOD, name heterogeneities exist.
Therefore, syntactic or dictionary based approaches have
limited coverage in property alignment.
09/06/2013 6iSemantics 2013
11. Properties capture meaning of triples and hence they are
complex in nature.
Syntactic or dictionary based approaches analyze property
names for equivalence. But in LOD, name heterogeneities exist.
Therefore, syntactic or dictionary based approaches have
limited coverage in property alignment.
Schema dependent approaches including processing domain
and range, class level tags do not capture semantics of
properties well.
09/06/2013 6iSemantics 2013
12. Background
Statistical Equivalence of properties
Evaluation
Discussion, interesting facts, and future directions
Conclusion
09/06/2013 7
Roadmap
iSemantics 2013
13. Statistical Equivalence is based on analyzing owl:equivalentProperty.
owl:equivalentProperty - properties that have same property
extensions.
09/06/2013 8
Statistical Equivalence of properties
iSemantics 2013
14. Statistical Equivalence is based on analyzing owl:equivalentProperty.
owl:equivalentProperty - properties that have same property
extensions.
Example 1:
Property P is defined by the triples, { a P b, c P d, e P f }
Property Q is defined by the triples, { a Q b, c Q d, e Q f }
P and Q are owl:equivalentProperty, because they have the same extension,
{ {a,b}, {c,d}, {e,f} }
09/06/2013 8
Statistical Equivalence of properties
iSemantics 2013
15. Statistical Equivalence is based on analyzing owl:equivalentProperty.
owl:equivalentProperty - properties that have same property
extensions.
Example 1:
Property P is defined by the triples, { a P b, c P d, e P f }
Property Q is defined by the triples, { a Q b, c Q d, e Q f }
P and Q are owl:equivalentProperty, because they have the same extension,
{ {a,b}, {c,d}, {e,f} }
Example 2:
Property P is defined by the triples, { a P b, c P d, e P f }
Property Q is defined by the triples, { a Q b, c Q d, e Q h }
Then, P and Q are not owl:equivalentProperty, because their extensions are not the
same. But they provide statistical evidence in support of equivalence.
09/06/2013 8
Statistical Equivalence of properties
iSemantics 2013
16. Intuition
Higher rate of subject-object matches in extensions leads to
equivalent properties. In practice, it is hard to have exact same
extensions for matching properties. Because,
– Datasets are incomplete.
– Same instance may be modelled differently in different datasets.
Therefore, we analyze the property extensions to identify equivalent
properties between datasets.
We define the following notions. Let the statement below be true
for all the definitions.
S1P1O1 and S2P2O2 be two triples in Dataset D1 and D2 respectively.
09/06/2013 iSemantics 2013 9
26. 09/06/2013 iSemantics 2013 13
Complexity:
If the average number of properties for an entity is x and for each property, average
number of objects is j. For n subjects, it requires n*j2*x2+2n comparisons. Since n > j,
n > x, and x and j are independent of n, O(n).
29. Background
Statistical Equivalence of properties
Evaluation
Discussion, interesting facts, and future directions
Conclusion
09/06/2013 16
Roadmap
iSemantics 2013
30. Objectives of the evaluation
– Show the effectiveness of the approach in linked datasets
– Compare with existing aligning techniques
09/06/2013 iSemantics 2013 17
Evaluation
31. Objectives of the evaluation
– Show the effectiveness of the approach in linked datasets
– Compare with existing aligning techniques
We selected 5000 instance samples from
DBpedia, Freebase, LinkedMDB, DBLP L3S , and DBLP RKB
Explorer datasets.
09/06/2013 iSemantics 2013 17
Evaluation
32. Objectives of the evaluation
– Show the effectiveness of the approach in linked datasets
– Compare with existing aligning techniques
We selected 5000 instance samples from
DBpedia, Freebase, LinkedMDB, DBLP L3S , and DBLP RKB
Explorer datasets.
These datasets have,
– Complete data for instances in different viewpoints
– Many inter-links
– Complex properties
09/06/2013 iSemantics 2013 17
Evaluation
33. Experiment details
– α = 0.5 for all experiments (works for LOD) except DBpedia and
Freebase movie alignment where it was 0.7.
09/06/2013 iSemantics 2013 18
34. Experiment details
– α = 0.5 for all experiments (works for LOD) except DBpedia and
Freebase movie alignment where it was 0.7.
– k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and
Software between DBpedia and Freebase, Film between
LinkedMDB and DBpedia, and article between DBLP datasets.
09/06/2013 iSemantics 2013 18
35. Experiment details
– α = 0.5 for all experiments (works for LOD) except DBpedia and
Freebase movie alignment where it was 0.7.
– k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and
Software between DBpedia and Freebase, Film between
LinkedMDB and DBpedia, and article between DBLP datasets.
– k can be estimated using the data as follows,
– Set α = 0.5 and k = 2 (lowest positive values).
– Get exact matching property (property names) pairs not identified by
the algorithm and their μ
– Get the average of those μ values
09/06/2013 iSemantics 2013 18
36. Experiment details
– α = 0.5 for all experiments (works for LOD) except DBpedia and
Freebase movie alignment where it was 0.7.
– k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and
Software between DBpedia and Freebase, Film between
LinkedMDB and DBpedia, and article between DBLP datasets.
– k can be estimated using the data as follows,
– Set α = 0.5 and k = 2 (lowest positive values).
– Get exact matching property (property names) pairs not identified by
the algorithm and their μ
– Get the average of those μ values
– 0.92 for string similarity algorithms.
09/06/2013 iSemantics 2013 18
37. Experiment details
– α = 0.5 for all experiments (works for LOD) except DBpedia and
Freebase movie alignment where it was 0.7.
– k was set as 14, 6, 2, 2, and 2 respectively for Person, Film and
Software between DBpedia and Freebase, Film between
LinkedMDB and DBpedia, and article between DBLP datasets.
– k can be estimated using the data as follows,
– Set α = 0.5 and k = 2 (lowest positive values).
– Get exact matching property (property names) pairs not identified by
the algorithm and their μ
– Get the average of those μ values
– 0.92 for string similarity algorithms.
– 0.8 for WordNet similarity.
09/06/2013 iSemantics 2013 18
38. Measure
type
DBpedia –
Freebase
(Person)
DBpedia –
Freebase
(Film)
DBpedia –
Freebase
(Software)
DBpedia –
LinkedMDB
(Film)
DBLP_RKB –
DBLP_L3S
(Article)
Average
Extension
Based
Algorithm
Precision 0.8758 0.9737 0.6478 0.7560 1.0000 0.8427
Recall 0.8089* 0.5138 0.4339 0.8157 1.0000 0.7145
F measure 0.8410* 0.6727 0.5197 0.7848 1.0000 0.7656
WordNet
Similarity
Precision 0.5200 0.8620 0.7619 0.8823 1.0000 0.8052
Recall 0.4140* 0.3472 0.3018 0.3947 0.3333 0.3582
F measure 0.4609* 0.4950 0.4324 0.5454 0.5000 0.4867
Dice
Similarity
Precision 0.8064 0.9666 0.7659 1.0000 0.0000 0.7078
Recall 0.4777* 0.4027 0.3396 0.3421 0.0000 0.3124
F measure 0.6000* 0.5686 0.4705 0.5098 0.0000 0.4298
Jaro
Similarity
Precision 0.6774 0.8809 0.7755 0.9411 0.0000 0.6550
Recall 0.5350* 0.5138 0.3584 0.4210 0.0000 0.3656
F measure 0.5978* 0.6491 0.4903 0.5818 0.0000 0.4638
09/06/2013 iSemantics 2013 19
Alignment results
* Marks estimated values for experiment 1 because of very large comparisons to check manually. Boldface
marks highest result for each experiment.
41. Background
Statistical Equivalence of properties
Evaluation
Discussion, interesting facts, and future directions
Conclusion
09/06/2013 21
Roadmap
iSemantics 2013
42. Our experiment covered multi-domain to multi-domain, multi-
domain to specific domain and specific-domain to specific-domain
dataset property alignment.
09/06/2013 iSemantics 2013 22
Discussion, interesting facts, and future directions
43. Our experiment covered multi-domain to multi-domain, multi-
domain to specific domain and specific-domain to specific-domain
dataset property alignment.
In every experiment, the extension based algorithm outperformed
others (F measure). F measure gain is in the range of 57% to 78%.
09/06/2013 iSemantics 2013 22
Discussion, interesting facts, and future directions
44. Our experiment covered multi-domain to multi-domain, multi-
domain to specific domain and specific-domain to specific-domain
dataset property alignment.
In every experiment, the extension based algorithm outperformed
others (F measure). F measure gain is in the range of 57% to 78%.
Some properties that are identified are intentionally
different, e.g., db:distributor vs fb:production_companies.
– This is because many companies produce and also distribute their
films.
09/06/2013 iSemantics 2013 22
Discussion, interesting facts, and future directions
45. Our experiment covered multi-domain to multi-domain, multi-
domain to specific domain and specific-domain to specific-domain
dataset property alignment.
In every experiment, the extension based algorithm outperformed
others (F measure). F measure gain is in the range of 57% to 78%.
Some properties that are identified are intentionally
different, e.g., db:distributor vs fb:production_companies.
– This is because many companies produce and also distribute their
films.
Some identified pairs are incorrect due to errors in data modeling.
– For example, db:issue and fb:children.
09/06/2013 iSemantics 2013 22
Discussion, interesting facts, and future directions
46. Our experiment covered multi-domain to multi-domain, multi-
domain to specific domain and specific-domain to specific-domain
dataset property alignment.
In every experiment, the extension based algorithm outperformed
others (F measure). F measure gain is in the range of 57% to 78%.
Some properties that are identified are intentionally
different, e.g., db:distributor vs fb:production_companies.
– This is because many companies produce and also distribute their
films.
Some identified pairs are incorrect due to errors in data modeling.
– For example, db:issue and fb:children.
owl:sameAs linking issues in LOD (not linking exact same
thing), e.g., linking London and Greater London.
– We believe few misused links wont affect the algorithm as it decides
on a match after analyzing many matches for a pair.
09/06/2013 iSemantics 2013 22
Discussion, interesting facts, and future directions
47. Less number of interlinks.
– Evolve over time.
– Look for possible other types of ECR links (i.e., rdf:seeAlso).
09/06/2013 iSemantics 2013 23
48. Less number of interlinks.
– Evolve over time.
– Look for possible other types of ECR links (i.e., rdf:seeAlso).
Properties do not have uniform distribution in a dataset.
– Hence, some properties do not have enough matches or appearances.
– This is due to rare classes and domains they belong to.
– We can run the algorithm on instances that these less frequent
properties appear iteratively.
09/06/2013 iSemantics 2013 23
49. Less number of interlinks.
– Evolve over time.
– Look for possible other types of ECR links (i.e., rdf:seeAlso).
Properties do not have uniform distribution in a dataset.
– Hence, some properties do not have enough matches or appearances.
– This is due to rare classes and domains they belong to.
– We can run the algorithm on instances that these less frequent
properties appear iteratively.
Current limitations,
– Requires ECR links
– Requires overlapping datasets
– Object-type properties
– Inability to identify property – sub property relationships
09/06/2013 iSemantics 2013 23
50. Background
Statistical Equivalence of properties
Evaluation
Discussion, interesting facts, and future directions
Conclusion
09/06/2013 24
Roadmap
iSemantics 2013
51. We approximate owl:equivalentProperty using Statistical
Equivalence of properties by analyzing property
extensions, which is schema independent.
09/06/2013 iSemantics 2013 25
Conclusion
52. We approximate owl:equivalentProperty using Statistical
Equivalence of properties by analyzing property
extensions, which is schema independent.
This novel extension based approach works well with
interlinked datasets.
09/06/2013 iSemantics 2013 25
Conclusion
53. We approximate owl:equivalentProperty using Statistical
Equivalence of properties by analyzing property
extensions, which is schema independent.
This novel extension based approach works well with
interlinked datasets.
The extension based approach outperforms syntax or
dictionary based approaches. F measure gain in the range of
57% - 78%.
09/06/2013 iSemantics 2013 25
Conclusion
54. We approximate owl:equivalentProperty using Statistical
Equivalence of properties by analyzing property extensions,
which is schema independent.
This novel extension based approach works well with
interlinked datasets.
The extension based approach outperforms syntax or
dictionary based approaches. F measure gain in the range of
57% - 78%.
It requires many comparisons, but can be easily parallelized
evidenced by our Map-Reduce implementation.
09/06/2013 iSemantics 2013 25
Conclusion
k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.
k in some datasets are low because there are low appearing property pairs as well as popular property pairs. Because of this, sometimes lower k values as 2 produces better results but may be with low confidence.