In this talk, the speaker will demonstrate how to build a searchable knowledge base from scratch. The process includes data wrangling, entity indexing and full text search.
This presentation is an introduction to RDFa, as the fourth assignment of the IST 681 in iSchool, Syracuse University. The presentation is made by Kai Li, who is a library student in Syracuse University..
mailto : sovan107@gmail.com : To get this for FREE
Hi Viewers,
The reports for this seminar is also available. Please email me to get this for FREE...
Thanks
Sovan
This presentation is an introduction to RDFa, as the fourth assignment of the IST 681 in iSchool, Syracuse University. The presentation is made by Kai Li, who is a library student in Syracuse University..
mailto : sovan107@gmail.com : To get this for FREE
Hi Viewers,
The reports for this seminar is also available. Please email me to get this for FREE...
Thanks
Sovan
Training Project Report on Search EnginesShivam Saxena
This is a Summer Training Project Report Prepared by me to be submitted in my College... This report consist of a Tiny WEB SQL Search Engine made by me during training period...
Scaling Recommendations, Semantic Search, & Data Analytics with solrTrey Grainger
This presentation is from the inaugural Atlanta Solr Meetup held on 2014/10/21 at Atlanta Tech Village.
Description: CareerBuilder uses Solr to power their recommendation engine, semantic search, and data analytics products. They maintain an infrastructure of hundreds of Solr servers, holding over a billion documents and serving over a million queries an hour across thousands of unique search indexes. Come learn how CareerBuilder has integrated Solr into their technology platform (with assistance from Hadoop, Cassandra, and RabbitMQ) and walk through api and code examples to see how you can use Solr to implement your own real-time recommendation engine, semantic search, and data analytics solutions.
Speaker: Trey Grainger is the Director of Engineering for Search & Analytics at CareerBuilder.com and is the co-author of Solr in Action (2014, Manning Publications), the comprehensive example-driven guide to Apache Solr. His search experience includes handling multi-lingual content across dozens of markets/languages, machine learning, semantic search, big data analytics, customized Lucene/Solr scoring models, data mining and recommendation systems. Trey is also the Founder of Celiaccess.com, a gluten-free search engine, and is a frequent speaker at Lucene and Solr-related conferences.
Training Project Report on Search EnginesShivam Saxena
This is a Summer Training Project Report Prepared by me to be submitted in my College... This report consist of a Tiny WEB SQL Search Engine made by me during training period...
Scaling Recommendations, Semantic Search, & Data Analytics with solrTrey Grainger
This presentation is from the inaugural Atlanta Solr Meetup held on 2014/10/21 at Atlanta Tech Village.
Description: CareerBuilder uses Solr to power their recommendation engine, semantic search, and data analytics products. They maintain an infrastructure of hundreds of Solr servers, holding over a billion documents and serving over a million queries an hour across thousands of unique search indexes. Come learn how CareerBuilder has integrated Solr into their technology platform (with assistance from Hadoop, Cassandra, and RabbitMQ) and walk through api and code examples to see how you can use Solr to implement your own real-time recommendation engine, semantic search, and data analytics solutions.
Speaker: Trey Grainger is the Director of Engineering for Search & Analytics at CareerBuilder.com and is the co-author of Solr in Action (2014, Manning Publications), the comprehensive example-driven guide to Apache Solr. His search experience includes handling multi-lingual content across dozens of markets/languages, machine learning, semantic search, big data analytics, customized Lucene/Solr scoring models, data mining and recommendation systems. Trey is also the Founder of Celiaccess.com, a gluten-free search engine, and is a frequent speaker at Lucene and Solr-related conferences.
Presentation given at Barcamp Chiang Mai 4 on the basics of Semantic Web. A simple introduction with examples, aimed for those with a little Web development experience.
Raises questions about the true identity of Tim Berners-Lee.
LANL Research Library
March 12, 2009
Martin Klein & Michael L. Nelson
Department of Computer Science
Old Dominion University
Norfolk VA
www.cs.odu.edu/~{mklein,mln}
A lecture/conversation focusing on the first 12 years of Semantic Web - delivered on February 21, 2012.
See http://j.mp/SWIntro for more details. More detailed course material is at http://knoesis.org/courses/web3/
ES298 Computer Education
Learning Objectives:
1. Distinguish different information repositories in the World Wide Web
2.Know and apply knowledge of different types of search engines on actual web search process.
3. Apply Boolean techniques in Web Research
4. Create Learning Activities that develop students skills on Web Search
Talk about Exploring the Semantic Web, and particularly Linked Data, and the Rhizomer approach. Presented August 14th 2012 at the SRI AIC Seminar Series, Menlo Park, CA
At Utah State University, a pilot project is under development to evaluate the benefits of tracking data sets and faculty publications using the online catalog and the Library’s institutional repository.
With federal mandates to make publications and data open, universities look for solutions to track compliance. At Utah State University, the Sponsored Programs Office follows up with researchers to determine where data has been or will be deposited, per the terms of their grant.
Interested in making this publicly discoverable, the Library, Sponsored Programs, and Research Office are working together to pilot a project that enables the creation of publicly accessible MARC and Dublin Core records for data deposited by USU faculty. This project aims to make data sets, as well as publications, visible in research portals such as WorldCat, as well through Google searches.
This presentation will describe the project and anticipated benefits, as well as outline the roles of the cataloging staff and data librarian, and the involvement of the Research Office.
These slides were presented at the "graph databases in life sciences workshop". There is an accompanying Neo4j guide that will walk you through importing data into Neo4j using web services form a number of databases at EMBL-EBI.
https://github.com/simonjupp/importing-lifesci-data-into-neo4j
Black, Flake8, isort, and Mypy are useful Python linters but it’s challenging to use them effectively at scale in the case of multiple codebases, in a large codebase, or with many developers. Manually managing consistent linter versions and configurations across codebases requires endless effort. Linter analysis on large codebases is slow. Linters may slow down developers by asking them to fix trivial issues. Running linters in distributed CI jobs makes it hard to understand the overall developer experience.
To handle these scale challenges, we developed a reusable linter framework that releases new linter updates automatically, reuses consistent configurations, runs linters on only updated code to speedup runtime, collects logs and metrics to provide observability, and builds auto fixes for common linter issues. Our linter runs are fast and scalable. Every week, they run 10k times on multiple millions of lines of code in over 25 codebases, generating 25k suggestions for more than 200 developers. Its autofixes also save 20 hours of developer time every week.
In this talk, we’ll walk you through popular Python linters and configuration recommendations, and we will discuss common issues and solutions when scaling them out. Using linters more effectively will make it much easier for you to apply best practices and more quickly write better code.
EuroPython 2022 - Automated Refactoring Large Python CodebasesJimmy Lai
Like many companies with multi-million-line Python codebases, Carta has struggled to adopt best practices like Black formatting and type annotation. The extra work needed to do the right thing competes with the almost overwhelming need for new development, and unclear code ownership and lack of insight into the size and scope of type problems add to the burden. We’ve greatly mitigated these problems by building an automated refactoring pipeline that applies Black formatting and backfills missing types via incremental Github pull requests. Our refactor applications use LibCST and MonkeyType to modify the Python syntax tree and use GitPython/PyGithub to create and manage pull requests. It divides changes into small, easily reviewed pull requests and assigns appropriate code owners to review them. After creating and merging more than 3,000 pull requests, we have fully converted our large codebase to Black format and have added type annotations to more than 50,000 functions. In this talk, you’ll learn to use LibCST to build automated refactoring tools that fix general Python code quality issues at scale and how to use GitPython/PyGithub to automate the code review process.
Annotate types in large codebase with automated refactoringJimmy Lai
Add missing type annotations to a large Python codebase is not easy. The major challenges include: limited developer time, tons of missing types, code ownership, and active development. We solved the problem by building an automated refactoring pipeline that run CircleCI jobs to create incremental Github pull requests to backfill missing types using heuristic rules and MonkeyType. The refactor apps use LibCST to modify Python syntax tree. Changes are split into small reviewable pull requests and assigned to code owners to review. So far, the work has added type annotations to more than 45,000 Python functions and saved tons of engineering efforts.
The journey of asyncio adoption in instagramJimmy Lai
In this talk, we share our strategy to adopt asyncio and the tools we built: including common helper library for asyncio testing/debugging/profiling, static analysis and profiling tools for identify call stack, bug fixes and optimizations for asyncio module, design patterns for asyncio, etc. Those experiences are learn from large scale project -- Instagram Django Service.
Distributed system coordination by zookeeper and introduction to kazoo python...Jimmy Lai
Zookeeper is a coordination tool to let people build distributed systems easier. In this slides, the author summarizes the usage of zookeeper and provides Kazoo Python library as example.
In this slide, we introduce the mechanism of Solr used in Search Engine Back End API Solution for Fast Prototyping (LDSP). You will learn how to create a new core, update schema, query and sort in Solr.
[LDSP] Search Engine Back End API Solution for Fast PrototypingJimmy Lai
In this slides, I propose a solution for fast prototyping of search engine back end API. It consists of Linux + Django + Solr + Python (LDSP), and all are open source softwares. The solution also provides code repository with automation scripts. Everyone can build a Search Engine back end API in seconds by exploiting LDSP.
Big data analysis in python @ PyCon.tw 2013Jimmy Lai
Big data analysis involves several processes: collecting, storage, computing, analysis and visualization. In this slides, the author demonstrates these processes by using python tools to build a data product. The example is based on text-analyzing an online forum.
Text Classification in Python – using Pandas, scikit-learn, IPython Notebook ...Jimmy Lai
Big data analysis relies on exploiting various handy tools to gain insight from data easily. In this talk, the speaker demonstrates a data mining flow for text classification using many Python tools. The flow consists of feature extraction/selection, model training/tuning and evaluation. Various tools are used in the flow, including: Pandas for feature processing, scikit-learn for classification, IPython, Notebook for fast sketching, matplotlib for visualization.
In this slides, the author demonstrates many software development practices in Python. Including: runtime environment setup, source code management, version control, unit test, coding convention, code duplication, documentation and automation.
Fast data mining flow prototyping using IPython NotebookJimmy Lai
Big data analysis requires fast prototyping on data mining process to gain insight into data. In this slides, the author introduces how to use IPython Notebook to sketch code pieces for data mining stages and make fast observations easily.
Apache thrift-RPC service cross languagesJimmy Lai
This slides illustrate how to use Apache Thrift for building RPC service and provide demo example code in Python. The example scenario is: we have a prepared machine learning model, and we'd like to load the model in advance as a server for providing prediction service.
Big Data consists of several issues: data collecting, storage, computing, analysis and visualization. Python is a popular scripting language with good code readability and thus is suitable for fast development. In this slides, the author shares how to solve Big Data issues using Python open source tools.
Nltk natural language toolkit overview and application @ PyCon.tw 2012Jimmy Lai
This slides introduce a python toolkit for Natural Language Processing (NLP). The author introduces several useful topics in NLTK and demonstrates with code examples.
Nltk natural language toolkit overview and application @ PyHugJimmy Lai
NLTK is a python toolkit for Natural Language Processing. In this slide, the author provides overview for NLTK and demonstrates an application in Chinese text classification.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
1. Build a Searchable
Knowledge Base
Jimmy Lai
Yahoo! Search Engineer
r97922028 [at] ntu.edu.tw
2014/05/18
http://www.slideshare.net/jimmy_lai/build-a-searchable-knowledge-base
2. Outline
• Introduction to Knowledge Base
• Construct a Knowledge Base
• Search the Knowledge Base
• string match
• synonym search
• full text search
• geo search
• put all together
• More Applications
2
3. Knowledge
• Knowledge is power. - Francis Bacon, 1597
• Knowledge is boundless and connected. So, an
efficient interface to search and browse the
knowledge base is essential.
• Let’s try to build a searchable knowledge base.
3
5. Construct a Knowledge
Base
1. Find good data sources.
2. Aggregate data as knowledge entity.
3. Construct structured data of knowledge entity.
4. Search the knowledge base.
5. Navigate the knowledge base.
5
6. Wikipedia
• A collaborated encyclopedia with more than 30M
articles over 287 languages.
!
!
!
• A good source of knowledge base. However the
data of Wikipedia is not well-structured.
6
http://www.theguardian.com/technology/blog/2009/aug/13/wikipedia-edits
10. What can Python do for us
• Data Wrangling
• Process the raw text data
• Aggregate the data from different sources
• Output data as json format
• Connecting the Data flow between systems
• Automation script for starting services and
feeding data
• REST API implementing search strategy
10
12. Data Preparation
1. Download data from DBpedia
http://downloads.dbpedia.org/current/en/
2. Filter out some specific knowledge entity
zcat instance_types_en.nt.bz2 | get_id_list.py
3. Parse and aggregate data entity from files.
12
data file script data field
short_abstracts_en.nt.bz2 get_abstract.py abstract
raw_infobox_properties_en.nt.bz2 get_relation.py relations
geo_coordinates_en.nt.bz2 get_geo.py latlon
redirects_en.nt.bz2 get_redirect.py redirects
13. Aggregated Data Format
"http://dbpedia.org/resource/Lake_Yosemite": {
"latlon": "37.376389,-120.428889",
"redirects": [
"Lake_yosemite"
],
"abstract": "Lake Yosemite is an artificial freshwater lake located approximately
five miles (8 km) east of Merced, California in the rolling Sierra Foothills. UC Merced
is situated approximately half a mile (0.8 km) south of Lake Yosemite. The university
is bounded by the lake on one side and two canals (Fairfield Canal and Le Grand
Canal) run through the campus. In 2007, a myth featured in the Mythbusters' James
Bond Special 1 episode was filmed and tested at Lake Yosemite.",
"relations": {
"type": "http://dbpedia.org/resource/Reservoir",
"location": "http://dbpedia.org/resource/California"
}
}
13
14. Search by
• Solr is a full-text, real-time search engine based on Apache
lucene.
• Provides REST-like API.
• pysolr make the use of Solr easily.
• Download the latest version 4.8.0 from
http://www.apache.org/dyn/closer.cgi/lucene/solr/4.8.0
and extract to solr/solr-4.8.0 dir
• Start Solr server and then check the web UI
fab start_solr
http://localhost:8983/solr/
14
15. Search - String Match
• To be able to search by entity name
python feed_data.py string_match
• config: solr/conf/string_match/schema.xml
<field name="name" type="string" indexed="true" stored="true"
multiValued="false"/>
<field name="abstract" type="string" indexed="false" stored="true"
multiValued="false"/>
• Feed the entities to Solr. Each entity with name and
abstract fields.
15
16. Search - String Match
16
http://localhost:8983/solr/string_match/select?q=name%3A%22San+Francisco
%22&wt=json&indent=true
Search by entity name.
17. Search - Synonym
• To be able to search by synonym of entity name
python feed_data.py synonym_string_match
• config: solr/conf/synonym_string_match/schema.xml
<field name="name" type=“name_text" indexed="true" stored="true" multiValued="false"/>
!
<fieldType name="name_text" class="solr.TextField" positionIncrementGap="100">
<analyzer type="index">
<tokenizer class="solr.StandardTokenizerFactory"/>
<filter class="solr.SynonymFilterFactory" synonyms="synonyms.txt"
ignoreCase="true" expand="true"/>
<filter class="solr.LowerCaseFilterFactory"/>
</analyzer>
…
• Restart Solr server and the synonym file will be reloaded.
17
21. Search - Full Text Search
• To be able to search by entity name
python feed_data.py full_text_search
• config: solr/conf/full_text_search/schema.xml
<copyField source="name" dest="text"/>
<copyField source="abstract" dest=“text"/>
!
• Feed the entities to Solr. Each name and abstract
field will be copied to the text field. After that we
can do full text search without specify field to
search.
21
23. Search - Geo Search
• To be able to search by distance given a location
python feed_data.py geo_search
• config: solr/conf/geo_search/schema.xml
<field name="location" type="location" indexed="true" stored="true"
required="false" multiValued="false" />
• Feed the entities to Solr. Each entity contains a location
field and the format is like "51.670100,-3.230100".
23
25. Search - Put All Together
• Search Strategy
1. Input a query
2. Search by synonym match
3. Search by full text
1. If input a location, filter the result by geo
search
• Implement the search strategy as an API
25
28. Review
• A Knowledge Base with synonym, full-text and geo
search API.
• The knowledge entities are connected by relation.
28
29. More Applications
• Question answering system:
1.Query analysis: identify the intension (e.g. looking
for specific type of entity)
2.Search in the knowledge base
3.Return the knowledge entity
29
30. The modern search engine don’t just provide web page urls. They provide the
direct answer to users.
30
31. More Data Sources and
Knowledge Entities
• Open Data
!
!
!
• Open APIs
31
32. My Life in
• Build online services for billions of users.
• Big data mining on cloud infrastructures.
• Open and Innovative working environment.
• International teamwork and English communication.
• Business trips to Silicon Valley.
• Send me your resume if you need a referral.
r97922028 [at] ntu.edu.tw
32