An accademic project developed for the Web Information Retrieval class at Sapienza Università di Roma, Master Degree in Computer Engineering A.Y. 2019-20
The goal of the project is to rank computer scientists based on their influence using Google's PageRank and HITS algorithm.
For further information you can check our repository:
https://github.com/LucaTomei/Computer_Scientists
Members of the project:
Luca Tomei
Andrea Aurizi
Daniele Iacomini
This document discusses data citation and using identifiers to cite datasets. It explains that identifiers provide exposure, transparency, citation tracking and verification for datasets. Identifiers associate an alphanumeric string with the location of an object, like a dataset, and can include optional metadata. Common identifier systems like DOIs provide a precise way to identify and cite datasets. Services like EZID make it easy to create and manage identifiers for datasets. The document encourages attendees to get started with data citation by creating test identifiers and discussing options with librarians.
The document discusses the motivation for developing Semantic Automated Discovery and Integration (SADI) services as a way to represent important information that cannot be represented directly on the Semantic Web, such as data from analytical algorithms and statistical analyses, and presents SADI as a design pattern for making web services interoperable with the Semantic Web by explicitly labeling the relationships between entities.
Lokesh 's Ip project Pokemon informationbholu803201
This document describes a library management system project created by a student for their Class XII examination. It includes a certificate signed by the student and teacher, an acknowledgement, index, and sections on project analysis, functions and modules, source code, outputs and tables, and bibliography. The project uses Python and MySQL to create a database with tables for books, book issues, and returns to allow a library to track its inventory and lending activities.
This document describes a lightweight and precise information retrieval system for organizational wikis. The system crawls wikis to index proprietary information, processes natural language queries, and returns relevant results while integrating security policies to control access. An experimental model involves crawling, filtering, extracting, indexing, and integrating security. Experimental queries returned direct answers within 1-3 seconds, with 0-6 results, and nearly 33% returning a single answer. The source code is available online.
This document discusses problems with traditional scholarly publishing and proposes solutions centered around open data and transparency. It notes that traditional publishing hinders reproducibility due to lack of access to data and methods. This has led to an increasing number of non-reproducible findings and retractions. The document advocates for incentivizing the publication of data, software, workflows and other research objects to improve reproducibility and transparency. It highlights several examples where making these elements openly available improved scrutiny and identified errors in published works.
Facilitating Data Curation: a Solution Developed in the Toxicology DomainChristophe Debruyne
Christophe Debruyne, Jonathan Riggio, Emma Gustafson, Declan O'Sullivan, Mathieu Vinken, Tamara Vanhaecke, Olga De Troyer.
Presented at the 2020 IEEE 14th International Conference on Semantic Computing, San Diego, California, 3-5 February 2020
Toxicology aims to understand the adverse effects of
chemical compounds or physical agents on living organisms. For
chemicals, much information regarding safety testing of cosmetic
ingredients is now scattered in a plethora of safety evaluation
reports. Toxicologists in our university intend to collect this
information into a single repository. Their current approach uses
spreadsheets, does not scale well, and makes data curation and
querying cumbersome. Semantic technologies (e.g., RDF, OWL,
and Linked Data principles) would be more appropriate for
this purpose. However, this technology is not very accessible to
toxicologists without extensive training. In this paper, we report
on a tool that supports subject matter experts in the construction
of an RDF–based knowledge base for the toxicology domain. The
tool is using the jigsaw metaphor for guiding the subject matter
experts. We demonstrate that the jigsaw metaphor is a viable
option for generating RDF. Future work includes investigating
appropriate methods and tools for knowledge evolution and data
analysis.
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail ScienceUniversity of Washington
The document summarizes a system called SQLShare that aims to make SQL-based data analysis more accessible to scientists by lowering initial setup costs and providing automated tools. It has been used by 50 unique users at 4 UW campus labs on 16GB of uploaded data from various science domains like environmental science and metagenomics. The system provides data uploading, query sharing, automatic English-to-SQL translation, and personalized query recommendations to lower barriers to working with relational databases for analysis.
This document discusses data citation and using identifiers to cite datasets. It explains that identifiers provide exposure, transparency, citation tracking and verification for datasets. Identifiers associate an alphanumeric string with the location of an object, like a dataset, and can include optional metadata. Common identifier systems like DOIs provide a precise way to identify and cite datasets. Services like EZID make it easy to create and manage identifiers for datasets. The document encourages attendees to get started with data citation by creating test identifiers and discussing options with librarians.
The document discusses the motivation for developing Semantic Automated Discovery and Integration (SADI) services as a way to represent important information that cannot be represented directly on the Semantic Web, such as data from analytical algorithms and statistical analyses, and presents SADI as a design pattern for making web services interoperable with the Semantic Web by explicitly labeling the relationships between entities.
Lokesh 's Ip project Pokemon informationbholu803201
This document describes a library management system project created by a student for their Class XII examination. It includes a certificate signed by the student and teacher, an acknowledgement, index, and sections on project analysis, functions and modules, source code, outputs and tables, and bibliography. The project uses Python and MySQL to create a database with tables for books, book issues, and returns to allow a library to track its inventory and lending activities.
This document describes a lightweight and precise information retrieval system for organizational wikis. The system crawls wikis to index proprietary information, processes natural language queries, and returns relevant results while integrating security policies to control access. An experimental model involves crawling, filtering, extracting, indexing, and integrating security. Experimental queries returned direct answers within 1-3 seconds, with 0-6 results, and nearly 33% returning a single answer. The source code is available online.
This document discusses problems with traditional scholarly publishing and proposes solutions centered around open data and transparency. It notes that traditional publishing hinders reproducibility due to lack of access to data and methods. This has led to an increasing number of non-reproducible findings and retractions. The document advocates for incentivizing the publication of data, software, workflows and other research objects to improve reproducibility and transparency. It highlights several examples where making these elements openly available improved scrutiny and identified errors in published works.
Facilitating Data Curation: a Solution Developed in the Toxicology DomainChristophe Debruyne
Christophe Debruyne, Jonathan Riggio, Emma Gustafson, Declan O'Sullivan, Mathieu Vinken, Tamara Vanhaecke, Olga De Troyer.
Presented at the 2020 IEEE 14th International Conference on Semantic Computing, San Diego, California, 3-5 February 2020
Toxicology aims to understand the adverse effects of
chemical compounds or physical agents on living organisms. For
chemicals, much information regarding safety testing of cosmetic
ingredients is now scattered in a plethora of safety evaluation
reports. Toxicologists in our university intend to collect this
information into a single repository. Their current approach uses
spreadsheets, does not scale well, and makes data curation and
querying cumbersome. Semantic technologies (e.g., RDF, OWL,
and Linked Data principles) would be more appropriate for
this purpose. However, this technology is not very accessible to
toxicologists without extensive training. In this paper, we report
on a tool that supports subject matter experts in the construction
of an RDF–based knowledge base for the toxicology domain. The
tool is using the jigsaw metaphor for guiding the subject matter
experts. We demonstrate that the jigsaw metaphor is a viable
option for generating RDF. Future work includes investigating
appropriate methods and tools for knowledge evolution and data
analysis.
SQL is Dead; Long Live SQL: Lightweight Query Services for Long Tail ScienceUniversity of Washington
The document summarizes a system called SQLShare that aims to make SQL-based data analysis more accessible to scientists by lowering initial setup costs and providing automated tools. It has been used by 50 unique users at 4 UW campus labs on 16GB of uploaded data from various science domains like environmental science and metagenomics. The system provides data uploading, query sharing, automatic English-to-SQL translation, and personalized query recommendations to lower barriers to working with relational databases for analysis.
OKE Challenge was hosted by European Semantic Web Conference ESWC, 3-7 June 2018, held in Heraklion, Crete, Greece (Aldemar Knossos Royal & Royal Villa).
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
EN1320 Module 2 Lab 2.1Capturing the Reader’s InterestSelec.docxYASHU40
EN1320: Module 2
Lab 2.1
Capturing the Reader’s Interest
Select and rewrite any one introduction and one conclusion to make them engaging and inspiring.
For the introduction:
Is the opening statement interesting?
Does it use an opening technique suggested in the textbook?
Does it include a thesis—a statement that tells the central idea of the essay?
For the conclusion:
Does it restate the thesis or indicate the essay’s central idea?
Does it summarize support for the thesis or central idea?
Does it leave the reader with something to ponder or act upon?
Introduction 1:
Whether student-athletes should be monetarily compensated has long been debated. On one side, the National Collegiate Athletic Association (NCAA) and its supporters stand by NCAA rules, regulations, and bylaws as their claim for why student-athletes should not be paid. On the other side, critics stand by business economics as well as other NCAA rules that regulate the amount of money a student-athlete can earn in the academic year. Although critics claim that the NCAA limits the amount of money a student-athlete can earn in a year, the NCAA should continue to not allow a pay-to-play standard. History has proven that pay-to play programs would be unmanageable, scholarships are compensation enough, and athletic departments would have to become separate entities from the universities.
Introduction 2:
There is no question that society believes in the familiar saying, “If you do the crime, then you have to do the time.” The prison system is not meant to be a fun or happy place for convicted criminals to serve their time. Its main purpose is to punish anyone who breaks the law in order to decrease the crime rate. Prison is not the safest and most humane place to be. Prisons are dirty, chaotic, dangerous, and depressing, and it is not a life that is worth living. The conditions in the prison systems need to be improved to have positive outcomes such as reducing recidivism and benefiting society financially.
Conclusion 1:
All of these points being said, euthanasia should never be used against a patient’s will. If euthanasia is ever to be completely legal in our states, we need to impose a very strict and concise set of guidelines. Euthanasia could benefit many citizens with no hope of a full recovery. Suffering cancer patients could have a dignified alternative to their lives getting more unbearable by the day, with countless tests, medications, procedures, and prodding by dozens of doctors and nurses.
Conclusion 2:
In conclusion we cannot allow NASA to be dissolved or underfunded due to the importance of the future of space exploration and travel and the technology produced by NASA that saves lives and benefits humanity. We need Washington and NASA to come up with a 10-year plan and a funding system to allow NASA to do the work that it needs to do unhindered. Without a 10-year plan NASA will continue to be plagued by the rapidly changing political sc ...
Detection of Related Semantic Datasets Based on Frequent Subgraph MiningMikel Emaldi Manrique
This document proposes a method to detect related semantic datasets based on frequent subgraph mining. The method extracts the most frequent subgraphs from RDF graphs using the SUBDUE algorithm. These subgraphs are then matched across datasets to identify potential links. The method is evaluated against gold standard links and baselines, showing precise but limited recall. Future work to improve recall is discussed, such as using string similarity techniques.
How the Web can change social science research (including yours)Frank van Harmelen
A presentation for a group of PhD students from the Leibniz Institutes (section B, social sciences) to discuss how they could use the Web, and even better the Web of Data, as an instrument in their research.
This document summarizes a lecture on graph-theoretic models and algorithms for finding shortest paths in graphs. It introduces graphs and their representations as nodes connected by edges. It presents Python classes for representing graphs and shows implementations of depth-first search (DFS) and breadth-first search (BFS) algorithms to find the shortest path between two nodes in a graph. Examples are provided to demonstrate how DFS and BFS can find shortest paths in a graph modeling connected cities.
This presentation shows an overview of the main concepts introduced in the EDBT2015 Summer School, which took place in Palamos. For each area, we summarize the main issues and current approaches. We also describe the challenges and main activities that were undertaken in the summer school
The document discusses knowledge graphs and their application to scholarly communication. It describes how knowledge graphs can be used to represent scholarly concepts, artifacts, and their relationships in a structured way. This facilitates intuitive exploration and question answering over the represented scholarly content. The document provides examples of how a chemistry experiment could be represented in a knowledge graph and discusses how such representations enable new ways of comparing and surveying research.
Guest lecture at the Syracuse University School of Information Studies eScience Librarianship Lecture Series (08 Dec 2011).
Description: It’s your government, is it your data? New approaches to building interlinked catalogs of government-produced data. Dr. John S. Erickson, Director of Web Science Operations for the Tetherless World Constellation at Rensselaer Polytechnic Institute will present technical methods being developed to manage the delivery of large-scale open government data projects based on semantic web and linked data best practices.
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
Knowledge graph construction with a façade - The SPARQL Anything ProjectEnrico Daga
The document discusses a project called "SPARQL Anything" which aims to simplify knowledge graph construction by using SPARQL as the single language for representing and transforming diverse data formats into RDF. It presents an approach called "Facade-X" which defines a common RDF structure that can be applied over different formats like CSV, JSON, HTML, etc. This facade focuses on the RDF meta-model and aims to apply minimal ontological commitments. The document outlines how Facade-X can be used to represent different formats and provides examples of using SPARQL to transform sample data into RDF without committing to a specific domain ontology.
Interactive Browsing and Navigation in Relational DatabasesMinsuk Kahng
This document proposes ETable, an interface for interactively browsing and navigating relational databases. ETable introduces a hybrid representation between relational and nested-relational models to address challenges of interpreting join query results. It allows direct user manipulation of query results to refine them. A user study found ETable faster than an existing visual query builder for database querying tasks, and users found its pivot operation and intuitive interface made complex queries easy to specify.
A practical guide on how to query and visualize Linked Open Data with eea.daviz Plone add-on.
In this presentation you will get an introduction to Linked Open Data and where it is applied. We will see how to query this large open data cloud over the web with the language SPARQL. We will then go through real examples and create interactive and live data visualizations with full data tracebility using eea.sparql and eea.daviz.
Presented at the PLOG2013 conference http://www.coactivate.org/projects/plog2013
For more course tutorials visit
www.newtonhelp.com
INF 103 Week 1 DQ 1 How Do You Currently Use Information Technology
INF 103 Week 1 DQ 2 Innovations in Hardware and Software
INF 103 Week 2 DQ 1 Copyright and Fair Use
INF 103 Week 2 DQ 2 Searching for Information
INF 103 Week 2 Assignment Using Microsoft Word What Does the Library Have to Offer
INF 103(ASH) Possible Is Everything/newtonhelp.comlechenau71
For more course tutorials visit
www.newtonhelp.com
INF 103 Week 1 DQ 1 How Do You Currently Use Information Technology
INF 103 Week 1 DQ 2 Innovations in Hardware and Software
INF 103 Week 2 DQ 1 Copyright and Fair Use
INF 103 Week 2 DQ 2 Searching for Information
INF 103 Week 2 Assignment Using Microsoft Word What Does the Library Have to Offer
Open Harvester - Search publications for a researcher from CrossRef, PubMed a...Muhammad Javed
A java prototype that processes the result set of pre-downloaded data (from a database) and allows one to claim his/her publications from a ranked list.
This document introduces Marco Roos and discusses his transition from traditional molecular biology and bioinformatics work to e-science. It describes how e-science approaches can help address challenges in biology by enabling greater data and knowledge sharing, reuse of tools and workflows, and integrated analysis across multiple data types and sources. Examples discussed include semantic web technologies, workflow systems, and proposed e-laboratory platforms to empower scientists with virtual collaborative environments and intelligent assistance. The goal is to help biologists better exploit computational resources and expertise through enhanced and standardized e-science frameworks.
Session 24 - JDBC, Intro to Enterprise JavaPawanMM
This document provides an overview of JDBC and introduces Java Enterprise Edition. It begins with a continued discussion of JDBC, explaining the purpose of databases and how JDBC drivers connect Java applications to databases. It then demonstrates how to connect to an Oracle database using JDBC and perform basic operations like queries, inserts, updates and deletes. The document concludes by introducing Java EE and noting that hands-on examples will use the HR schema in Oracle.
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
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OKE Challenge was hosted by European Semantic Web Conference ESWC, 3-7 June 2018, held in Heraklion, Crete, Greece (Aldemar Knossos Royal & Royal Villa).
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
EN1320 Module 2 Lab 2.1Capturing the Reader’s InterestSelec.docxYASHU40
EN1320: Module 2
Lab 2.1
Capturing the Reader’s Interest
Select and rewrite any one introduction and one conclusion to make them engaging and inspiring.
For the introduction:
Is the opening statement interesting?
Does it use an opening technique suggested in the textbook?
Does it include a thesis—a statement that tells the central idea of the essay?
For the conclusion:
Does it restate the thesis or indicate the essay’s central idea?
Does it summarize support for the thesis or central idea?
Does it leave the reader with something to ponder or act upon?
Introduction 1:
Whether student-athletes should be monetarily compensated has long been debated. On one side, the National Collegiate Athletic Association (NCAA) and its supporters stand by NCAA rules, regulations, and bylaws as their claim for why student-athletes should not be paid. On the other side, critics stand by business economics as well as other NCAA rules that regulate the amount of money a student-athlete can earn in the academic year. Although critics claim that the NCAA limits the amount of money a student-athlete can earn in a year, the NCAA should continue to not allow a pay-to-play standard. History has proven that pay-to play programs would be unmanageable, scholarships are compensation enough, and athletic departments would have to become separate entities from the universities.
Introduction 2:
There is no question that society believes in the familiar saying, “If you do the crime, then you have to do the time.” The prison system is not meant to be a fun or happy place for convicted criminals to serve their time. Its main purpose is to punish anyone who breaks the law in order to decrease the crime rate. Prison is not the safest and most humane place to be. Prisons are dirty, chaotic, dangerous, and depressing, and it is not a life that is worth living. The conditions in the prison systems need to be improved to have positive outcomes such as reducing recidivism and benefiting society financially.
Conclusion 1:
All of these points being said, euthanasia should never be used against a patient’s will. If euthanasia is ever to be completely legal in our states, we need to impose a very strict and concise set of guidelines. Euthanasia could benefit many citizens with no hope of a full recovery. Suffering cancer patients could have a dignified alternative to their lives getting more unbearable by the day, with countless tests, medications, procedures, and prodding by dozens of doctors and nurses.
Conclusion 2:
In conclusion we cannot allow NASA to be dissolved or underfunded due to the importance of the future of space exploration and travel and the technology produced by NASA that saves lives and benefits humanity. We need Washington and NASA to come up with a 10-year plan and a funding system to allow NASA to do the work that it needs to do unhindered. Without a 10-year plan NASA will continue to be plagued by the rapidly changing political sc ...
Detection of Related Semantic Datasets Based on Frequent Subgraph MiningMikel Emaldi Manrique
This document proposes a method to detect related semantic datasets based on frequent subgraph mining. The method extracts the most frequent subgraphs from RDF graphs using the SUBDUE algorithm. These subgraphs are then matched across datasets to identify potential links. The method is evaluated against gold standard links and baselines, showing precise but limited recall. Future work to improve recall is discussed, such as using string similarity techniques.
How the Web can change social science research (including yours)Frank van Harmelen
A presentation for a group of PhD students from the Leibniz Institutes (section B, social sciences) to discuss how they could use the Web, and even better the Web of Data, as an instrument in their research.
This document summarizes a lecture on graph-theoretic models and algorithms for finding shortest paths in graphs. It introduces graphs and their representations as nodes connected by edges. It presents Python classes for representing graphs and shows implementations of depth-first search (DFS) and breadth-first search (BFS) algorithms to find the shortest path between two nodes in a graph. Examples are provided to demonstrate how DFS and BFS can find shortest paths in a graph modeling connected cities.
This presentation shows an overview of the main concepts introduced in the EDBT2015 Summer School, which took place in Palamos. For each area, we summarize the main issues and current approaches. We also describe the challenges and main activities that were undertaken in the summer school
The document discusses knowledge graphs and their application to scholarly communication. It describes how knowledge graphs can be used to represent scholarly concepts, artifacts, and their relationships in a structured way. This facilitates intuitive exploration and question answering over the represented scholarly content. The document provides examples of how a chemistry experiment could be represented in a knowledge graph and discusses how such representations enable new ways of comparing and surveying research.
Guest lecture at the Syracuse University School of Information Studies eScience Librarianship Lecture Series (08 Dec 2011).
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Knowledge graph construction with a façade - The SPARQL Anything ProjectEnrico Daga
The document discusses a project called "SPARQL Anything" which aims to simplify knowledge graph construction by using SPARQL as the single language for representing and transforming diverse data formats into RDF. It presents an approach called "Facade-X" which defines a common RDF structure that can be applied over different formats like CSV, JSON, HTML, etc. This facade focuses on the RDF meta-model and aims to apply minimal ontological commitments. The document outlines how Facade-X can be used to represent different formats and provides examples of using SPARQL to transform sample data into RDF without committing to a specific domain ontology.
Interactive Browsing and Navigation in Relational DatabasesMinsuk Kahng
This document proposes ETable, an interface for interactively browsing and navigating relational databases. ETable introduces a hybrid representation between relational and nested-relational models to address challenges of interpreting join query results. It allows direct user manipulation of query results to refine them. A user study found ETable faster than an existing visual query builder for database querying tasks, and users found its pivot operation and intuitive interface made complex queries easy to specify.
A practical guide on how to query and visualize Linked Open Data with eea.daviz Plone add-on.
In this presentation you will get an introduction to Linked Open Data and where it is applied. We will see how to query this large open data cloud over the web with the language SPARQL. We will then go through real examples and create interactive and live data visualizations with full data tracebility using eea.sparql and eea.daviz.
Presented at the PLOG2013 conference http://www.coactivate.org/projects/plog2013
For more course tutorials visit
www.newtonhelp.com
INF 103 Week 1 DQ 1 How Do You Currently Use Information Technology
INF 103 Week 1 DQ 2 Innovations in Hardware and Software
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INF 103 Week 2 DQ 2 Searching for Information
INF 103 Week 2 Assignment Using Microsoft Word What Does the Library Have to Offer
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For more course tutorials visit
www.newtonhelp.com
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INF 103 Week 1 DQ 2 Innovations in Hardware and Software
INF 103 Week 2 DQ 1 Copyright and Fair Use
INF 103 Week 2 DQ 2 Searching for Information
INF 103 Week 2 Assignment Using Microsoft Word What Does the Library Have to Offer
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A java prototype that processes the result set of pre-downloaded data (from a database) and allows one to claim his/her publications from a ranked list.
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Session 24 - JDBC, Intro to Enterprise JavaPawanMM
This document provides an overview of JDBC and introduces Java Enterprise Edition. It begins with a continued discussion of JDBC, explaining the purpose of databases and how JDBC drivers connect Java applications to databases. It then demonstrates how to connect to an Oracle database using JDBC and perform basic operations like queries, inserts, updates and deletes. The document concludes by introducing Java EE and noting that hands-on examples will use the HR schema in Oracle.
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1. Computer Scientists Retrieval
WIR Project
Students:
• Luca Tomei
• Daniele Iacomini
• Andrea Aurizi
Directed by:
Andrea Vitaletti
Luca Becchetti
Project Repository
A.Y. 2019-2020
Summary
The paper we chose presents a method to find
the most influential rock guitarist by applying
Google’s PageRank algorithm to information ex-
tracted from Wikipedia articles. The influence
of a guitarist is computed by considering the
number of guitarists citing him/her as influence.
Basically, the experiment consists of building a directed
graph where nodes are rock guitarists. There is an out-
going edge from guitarist A to another guitarist B, if
guitarist A is influenced by guitarist B.
Joe Satriani is influenced by Steve Vai and the latter by Frank Zappa
We decided to replicate the same experiment with the
same methodology but choosing computer scientists as
the field of study.
1
3. Engineering in Computer Science Academic Year 2019-2020
1 Introduction
In this project we focused on the analysis of data concerning a different category
from that of guitarists: computer scientists. Unlike a guitarist or a philosopher,
a computer engineer does not have much relevant data on wikipedia and there is
also no information regarding his school of thought or the influences he had during
his life. In fact, the first difficulty encountered during the preliminary phase of
the project was precisely to try to create a one-to-one correspondence between two
computer scientists, which is not always possible.
Figure 1: Poet vs Computer Scientist
The previous figures show the data fields on the WikiPedia ”infobox biography
vcard” tables. The difference in the number of fields between a philosopher and a
computer scientist is clear and the latter also has no ”Influences” and ”Influenced”
type fields that we can draw on to create connections between one computer scientist
and another.
Therefore, to overcome this problem, we first decided to use SPARQL to explore and
extract the information contained in RDF graphs from a knowledge base distributed
on DBpedia.
Subsequently it was decided to carry out a further verification of the results by going
directly to Wikipedia since it is undoubtedly one of the greatest resources of the Web
for all knowledge.
3
4. Engineering in Computer Science Computer Scientists Retrieval
2 Query DBpedia with SPARQL
To query DBpedia it was decided to use SPARQLWrapper, wrapper with the aim
of creating the URI of the query and possibly converting the result into a more
manageable format.
✞ ☎
1 """ Query DBpedia with SPARQL Code """
2 from SPARQLWrapper import SPARQLWrapper , JSON
3
4 def query_dbpedia ( self ) :
5 sparql = SPARQLWrapper ( "http :// dbpedia.org/sparql" )
6 sparql . setQuery ( """
7 PREFIX dbo: <http :// dbpedia.org/ontology/>
8 PREFIX foaf: <http :// xmlns.com/foaf /0.1/ >
9 PREFIX dct: <http :// purl.org/dc/terms/>
10 PREFIX dbr: <http :// dbpedia.org/resource/>
11 PREFIX dbc: <http :// dbpedia.org/resource/Category:>
12
13 SELECT distinct ?name ?person ?subject WHERE {
14 ?person foaf:name ?name.
15 ?person dct:subject dbc: Computer_scientists .
16 ?person dct:subject ?subject.
17 filter regex (? subject , "http :// dbpedia.org/resource/Category:
Computer_scientists ")
18 }
19 ORDER BY ?name
20 """ )
21 sparql . setReturnFormat ( JSON )
22 results = sparql . query () . convert ()
23 return results
✝ ✆
Listing 1: Query DBpedia
After encapsulating the query output in the variable result we are ready to examine
the data obtained.
Going to analyze the updated data, we realized that in many cases the same person
is registered in multiple repositories but under different URIs. However, only a small
part of the possible links between them is available.
Since there is no ontology regarding computer scientists, more links can be discovered
by performing automatic coreference resolution, but this task is complicated by two
issues:
• Datasets do not contain overlapping properties for their individuals apart from
personal names.
• Individuals which belong to overlapping subsets are not distinguished from
others: in DBPedia the majority of computer scientists is assigned to a generic
class dbpedia:Person and not distinguished from other people. As a result, it
becomes complicated to extract the subset of computer scientists from DBPe-
dia.
Individuals in DBPedia are connected by rdf:type links to classes defined in the
YAGO repository. The YAGO ontology is based on Wikipedia categories and pro-
vides a more detailed hierarchy of classes than the DBPedia ontology.
The data returned by the query are not satisfactory, therefore it was decided to take
a winding road: the manual scraping of wikipedia pages.
4
5. Engineering in Computer Science Academic Year 2019-2020
3 Manual scraping Wikipedia
DBpedia offers few results for Computer Scientists, which is why it was decided to
take a more demanding path but that would give us more results: Web Scarping.
It was decided to divide this process into 4 logical phases:
1. Collect in a file all the links of computer scientists present in the English
version of Wikipedia (List Of Computer Scientists).
2. For each link of a computer scientist obtained from the previous phase, it was
checked whether the relative web page contained an information table that
included the list of influences and influences of this computer scientist.
3. From the results obtained, the Pagerank was calculated with the aim of quanti-
fying the importance of the relative computer scientist within the set of related
documents.
4. It was decided to dwell on the classification of the various fields of study of a
computer scientist.
3.1 First Phase: Collect data
The English version of Wikipedia contains a list of all the computer scientists (link)
with an existing article, alphabetically sorted. In the first phase, we simply copy
each link and its associated name in a .json file.
The total number of computer scientists retrieved is 509.
3.2 Second Phase: Check informations in Biographic Table
Before starting to extrapolate the information of each computer scientist, for a
matter of code optimization and efficiency in terms of performance it was decided to
download the entire Wikipedia pages of each of them, so as not to make n cs = 509
requests.
After collecting the web pages of each computer scientist, for each of them it was
checked whether the corresponding page contained an information table (Infobox
HTML) which included the list of influences and influencers of the said computer
scientist.
The number of Computer Scientists who meet this requirement is 62.
✞ ☎
1 def bio_table ( self , page ) :
2 name = page . rsplit ( ’/’) [ −1]
3 page = open ( page , ’r’)
4 soup = BeautifulSoup ( page , "html.parser" )
5 table = soup . find ( ’table ’ , class_=’infobox biography vcard ’)
6 try : influencers = table . find_all ( ’ul’ , class_=’NavContent ’) [ 0 ]
7 except : influencers = [ ]
8 try : influenced = table . find_all ( ’ul’ , class_=’NavContent ’) [ 1 ]
9 except : influenced = [ ]
10 final_influencers , final_influenced = ( [ ] for i in range (2) )
11 if influencers != [ ] :
12 for a in influencers . find_all ( ’a’) :
13 final_influencers . append ( a . get ( ’title ’) )
14 if influenced != [ ] :
15 for a in influenced . find_all ( ’a’) :
16 final_influenced . append ( a . get ( ’title ’) )
✝ ✆
Listing 2: Checking Bio Table Function
5
6. Engineering in Computer Science Computer Scientists Retrieval
3.3 Third Phase: Second Approach and Pagerank
Since the result obtained from the second phase was not at all satisfactory, it was
decided to use a second approach: instead of checking only the biography table of a
computer scientist, the entire page associated with him was analyzed.
✞ ☎
1 def make_links ( self , path ) :
2 # Define output dict
3 inlinks = SortedDict ()
4 outlinks = SortedDict ()
5
6 SetofNames = SortedSet ()
7
8 #reading all the folders from the path and creating a set of CS names
9 for name in self . read_names ( path ) :
10 if name == "Guy_L._Steele ,_Jr" : name = "Guy_L._Steele ,_Jr."
11
12 SetofNames . add ( name )
13
14 #creating an empty inlinks of names as sortedSet
15 inlinks [ name ] = SortedSet ()
16
17 #reading their inlinks and outlinks
18 for name in SetofNames :
19 SetOfInLinks = SortedSet ()
20 fp = open ( path + "/"+ name , ’r’ , encoding = "utf -8" )
21 soup = BeautifulSoup ( fp . read () , "html.parser" )
22 linksFound = [ ]
23 linksFound = soup . findAll ( ’a’ , href=re . compile ( "/wiki/" ) )
24
25 HTML = ""
26 for link in linksFound :
27 HTML = HTML + str ( link )
28 HTML = HTML + " and "
29
30 #get All the outlinks by calling get_links
31 outlinks [ name ] = self . get_links ( SetofNames , HTML )
32
33 if name in outlinks [ name ] : outlinks [ name ] . remove ( name )
34
35 for outlink in outlinks [ name ] :
36 SetOfInLinks . add ( name )
37 inlinks [ outlink ] . update ( SetOfInLinks )
38 return ( inlinks , outlinks )
✝ ✆
Listing 3: Making Links
The previous function, after reading the html pages of each computer scientist as
input, creates and returns two SortedDict:
• inlinks: maps from a name to a SortedSet of names that link to it.
• outlinks: maps from a name to a SortedSet of names that it links to.
For example:
• inlinks[’Ada_Lovelace’] = SortedSet([’Charles_Babbage’, ’David_Gelernter’], key=None, load=1000)
• outlinks[’Ada_Lovelace’] = SortedSet([’Alan_Turing’, ’Charles_Babbage’], key=None, load=1000)
To obtain all the outlinks we call self.get_links(SetofNames,HTML), which return a SortedSet
of computer scientist names that are linked from this html page. The return set is
restricted to those people in the provided set of names. The returned list should
contain no duplicates. This function take as input:
• A SortedSet of computer scientist names, one per filename.
• A string representing one html page.
6
7. Engineering in Computer Science Academic Year 2019-2020
✞ ☎
1 def get_links ( self , names , html ) :
2 listofHrefs = [ ]
3 listofHrefTexts = [ ]
4 FinalSortedSet = SortedSet ()
5 splice_char = ’/’
6
7 for i in range (0 , len ( listofHrefs ) ) :
8 value = listofHrefs [ i ] [ 6 : ]
9 listofHrefTexts . append ( value )
10
11 listofHrefTexts = re . findall ( r"href=""]*)" , html )
12
13 for i in listofHrefTexts :
14 value = i [ 6 : ]
15 listofHrefs . append ( value )
16 listofHrefs = list ( set ( listofHrefs ) )
17
18 for href in listofHrefs :
19 for name in names :
20 if ( name == "Guy_L._Steele ,_Jr" ) :
21 names . remove ( name )
22 names . add ( "Guy_L._Steele ,_Jr." )
23 if ( href == name ) : FinalSortedSet . add ( name )
24
25 return FinalSortedSet
✝ ✆
Listing 4: Get Links
With the results obtained by the function make links we can calculate the pagerank
by calling the function compute_pagerank.
✞ ☎
1 def compute_pagerank ( self , urls , inlinks , outlinks , b =.85 , iters=20) :
2 rw = defaultdict ( lambda : 0 . 0 )
3 pageRank = defaultdict ( lambda : 1 . 0 )
4
5 for outlink in outlinks : rw [ outlink]=len ( outlinks [ outlink ] )
6
7 #initialize page ranks scores to 1
8 for url in urls : pageRank [ url ] = 1.0
9
10 for i in range ( iters ) :
11 for url in urls :
12 summ = 0.0
13 for link in inlinks [ url ] : summ += 1.0 ∗ pageRank [ link ]/ rw [ link ]
14 pageRank [ url ] = (1/ len ( urls ) ) ∗ (1.0 − b )+b∗summ
15 return SortedDict ( dict ( pageRank ) )
✝ ✆
Listing 5: Computing Pagerank
This function return a SortedDict mapping each url to its final PageRank value
(float) by using this formula:
R(u) =
! 1
N
"
(1 − b) + b ·
#
w∈Bu
R(w)
|Fw|
where:
• R(u) = Pagerank value of the page u we want to calculate;
• B(u) = A set of pages that contain at least one link to the u page. w represents
each of these pages;
• R(w) = PageRank values of each page w;
• Fw = Total number of links contained on the page offering the link;
• b = Damping factor. It is generally assumed that it will be set around 0.85.
7
8. Engineering in Computer Science Computer Scientists Retrieval
3.3.1 My Pagerank Results
The following figure shows the terminal output after making the call to compute pagerank
with 20 iterations e 509 computer scientists.
Figure 2: compute pagerank output
After applying the pagerank, it was decided to build a direct graph that highlighted
the connections between each computer scientist. The figure shown below, built with
the Python PIL library, shows that there is a thickening in the upper part which
highlights the numerous connections that are among the top computer scientists
obtained as an output from the compute pagerank function.
Figure 3: Pagerank Graph
8
9. Engineering in Computer Science Academic Year 2019-2020
4 Categorization
A further experiment was to try to draw up a ranking of the best branches of study
carried out by these people.
In evaluating the fields that can be used, it has been verified that the majority of
computer scientists present in the famous Wikipedia infobox table a field called Field
which is right for us: it contains every category of study carried out from the person
being examined.
Out of a total of 509 computer scientists, this field is present in about 480 people.
In the lower left is shown the code used to extrapolate this information and a function
designed to create a json file that associates the name of a computer scientist with
the categories to which it belongs.
✞ ☎
1 def get_fields ( self , file_name ) :
2 soup = BeautifulSoup ( self .
get_html_content ( file_name ) , ’html.
parser ’)
3 infobox = soup . find ( ’table ’ , {’class ’
: ’infobox ’})
4 fields_list = [ ]
5 if infobox != None :
6 for item in infobox . findAll ( ’tr’) :
7 infobox_key = item . find ( ’th’)
8 if infobox_key != None and
infobox_key . get_text () == ’Fields ’ :
9 infobox_value = item . find ( ’td’)
10 fields_list . append (
infobox_value . get_text () )
11 this_name = self .
get_cs_name_from_filename ( file_name
)
12 return fields_list
✝ ✆
Listing 6: Get Fields
✞ ☎
1 def compute_categorization ( self ,
all_cs_files_list ) :
2 to_ret = [ ]
3 none = 0
4 for file_name in
all_cs_files_list :
5 cs_name = self .
get_cs_name_from_filename (
file_name )
6 his_fields = self . clean_fields
( self . get_fields ( file_name ) )
7
8 new_fields = [ ]
9 for item in his_fields :
new_fields . append ( item .
capitalize () )
10 if new_fields != [ ] :
11 cs_name = urllib . parse .
unquote ( cs_name )
12 to_ret . append ({ cs_name :
new_fields })
13 else :
14 none += 1
15 return to_ret
✝ ✆
Listing 7: Categorization
After obtaining a json file containing the ordered set of categories associated with a
computer scientist, a ranking was finally drawn up through the computation of the
pagerank and hits algorithm.
The results obtained by the two algorithms are shown below:
hits top 20 categories.txt
’Computer science’, 0.326117140120153
’Mathematics’, 0.0905002288461773
’Artificial intelligence’, 0.06685354382887093
’Logic’, 0.03427669758106854
’Electrical engineering’, 0.02833238326892856
’Human-computer interaction’, 0.021348931558433284
’Cognitive psychology’, 0.01891198265547915
’Internet’, 0.017865405333952915
’Cryptography’, 0.01738984860471658
’Engineering’, 0.017205005058716537
’Parallel computing’, 0.016884266897918044
’Computer engineering’, 0.01666589916367132
’Cognitive science’, 0.012768419943534962
’Machine learning’, 0.012000867181936405
’Theoretical biology’, 0.011521251294523721
’Cryptanalysis’, 0.011521251294523721
’Complex systems’, 0.01109965631694415
’Political science’, 0.0105244314790377
’Economics’, 0.0105244314790377
’Biology’, 0.010380178200082258
’Philosophy’, 0.010380178200082258
pagerank top 20 categories.txt
’Computer science’, 0.04919569025380936
’Mathematics’, 0.021521266029255075
’Artificial intelligence’, 0.01889296875653205
’Human-computer interaction’, 0.011111147419646208
’Theoretical computer science’, 0.008998202553339458
’Logic’, 0.008379779665639922
’Semantic web’, 0.008122103462431782
’Machine learning’, 0.00812210346243178
’Robotics’, 0.007864427259223643
’Electrical engineering’, 0.0077613567779403845
’Entrepreneur’, 0.006730651965107824
’Statistics’, 0.006730651965107824
’Computer engineering’, 0.006730651965107824
’Computational information systems’, 0.006730651965107824
’Cognitive science’, 0.006576046243182939
’Cryptography’, 0.006215299558691543
’Parallel computing’, 0.006215299558691543
’Computer graphics’, 0.006215299558691543
’Operating systems’, 0.006215299558691543
’Engineering’, 0.005957623355483403
’Physics’, 0.005957623355483403
9
10. Engineering in Computer Science Computer Scientists Retrieval
5 Conclusions
The experiment consisted of applying the same procedure to computer scientists
as described in the original document: the discrepancy between the rankings is
minimal, especially if the list of the top twenty is taken into account (instead of just
the list of the top ten). The complete rankings can be consulted on the Github page.
One of the biggest difficulties in the experiment was polishing the data in order to
provide reliable results. For this reason, the dataset obtained with DBpedia was
not used in subsequent tests with custom HITS and PageRank. A larger number of
datasets would have made the results of the experiment more interesting.
6 Contacts
Here is the GitHub link of our project: https://github.com/LucaTomei/Computer Scientists
10