SlideShare a Scribd company logo
1 of 42
Download to read offline
DBpedia SpotlightShedding Light on the Web of Documents,[object Object],Pablo N. Mendes, Max Jakob, Andrés Garcia-Silva, Christian Bizer,[object Object],pablo.mendes@fu-berlin.de,[object Object],I-SEMANTICS, Graz, Austria,[object Object],September 9th 2011,[object Object],1,[object Object]
Agenda,[object Object],What is text annotation?,[object Object],What can you build with it?,[object Object],Why is it difficult?,[object Object],How did we approach the challenge?,[object Object],How well did it work?,[object Object],What are the next steps?,[object Object],2,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
What is it?,[object Object],3,[object Object]
Text Annotation,[object Object],From:,[object Object],To:,[object Object],(…) Upon their return, Lennon and McCartney went to New York to announce the formation of Apple Corps. ,[object Object],(…) Upon their return, Lennon and McCartney went to New York to announce the formation of Apple Corps. ,[object Object],http://dbpedia.org/resource/New_York_City,[object Object],http://dbpedia.org/resource/Apple_Corps,[object Object],4,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Challenge: Term Ambiguity,[object Object],5,[object Object],...this apple on the palm of my hand...,[object Object],...Apple tried to acquire Palm Inc....,[object Object],...eating an apple sitted by a palm tree...,[object Object],What do “apple” and “palm” mean in each case?,[object Object],Our objective is to recognize entities and disambiguate their meaning, generating DBpedia annotation in text.,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
What can you do with annotations?,[object Object],Links to complementary information,[object Object],“More about this”,[object Object],Faceted browsing of blog posts,[object Object],Show only posts with topics related to Sports,[object Object],Rich snippets on Google,[object Object],Search engines start to display info from annotations,[object Object],More expressive filtering of information streams,[object Object],Twarql (entry at I-SEMANTICS 2010 Challenge),[object Object],6,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Rich Snippets,[object Object],Search Engines already benefit from some kinds of annotations,[object Object],7,[object Object],http://www.google.com/webmasters/tools/richsnippets,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Twarql Example Use Case,[object Object],What competitors of my product are being mentioned with my product on Twitter?,[object Object],- comparative opinion!,[object Object],SELECT ? competitor,[object Object],WHERE {,[object Object],dbpedia:IPadskos:subject 	?category .,[object Object],  ?competitor 	skos:subject 	?category .,[object Object],  ?tweet 		moat:taggedWith 	?competitor .,[object Object],},[object Object],?tweet 		moat:taggedWithdbpedia:Ipad .,[object Object],8,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Twarql Example Use Case (2),[object Object],Incoming microposts…,[object Object],Background Knowledge (e.g. DBpedia),[object Object],@anonymized,[object Object],Loremipsumblabla this is an example tweet,[object Object],dbpedia:IPad,[object Object],skos:subject,[object Object],?category,[object Object],?category,[object Object],?competitor,[object Object],skos:subject,[object Object],skos:subject,[object Object],moat:taggedWith,[object Object],Competition is modeled as two products ,[object Object],in the same category in DBpedia,[object Object],?tweet,[object Object],9,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Twarql Example Use Case (3),[object Object],Incoming microposts…,[object Object],Background Knowledge (e.g. DBpedia),[object Object],@anonymized,[object Object],Loremipsumblabla this is an example tweet,[object Object],category:Wi-Fi,[object Object],dbpedia:IPad,[object Object],category:Touchscreen,[object Object],skos:subject,[object Object],?category,[object Object],?category,[object Object],?competitor,[object Object],skos:subject,[object Object],skos:subject,[object Object],moat:taggedWith,[object Object],Background knowledge is dynamically “brought into” microposts.,[object Object],?tweet,[object Object],10,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Twarql Example Use Case (4),[object Object],Background Knowledge (e.g. DBpedia),[object Object],@anonymized,[object Object],Loremipsumblabla this is an example tweet,[object Object],category:Wi-Fi,[object Object],dbpedia:IPad,[object Object],category:Touchscreen,[object Object],skos:subject,[object Object],?category,[object Object],?category,[object Object],?competitor,[object Object],skos:subject,[object Object],skos:subject,[object Object],moat:taggedWith,[object Object],?tweet,[object Object],Trigger action if micropost matches constraints.,[object Object],11,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
DBpedia Spotlight,[object Object],DBpedia is a collection of entity descriptions extracted from Wikipedia & shared as linked data,[object Object],DBpedia Spotlight uses data from DBpedia and text from associated Wikipedia pages ,[object Object],Learns how to recognize that a DBpedia resource was mentioned,[object Object],Given plain text as input, generates annotated text,[object Object],12,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Why is it difficult?,[object Object],13,[object Object]
Dataset overview,[object Object],Volume of Wikipedia,[object Object],56,9 GB in raw text data,[object Object],Occurrences of Ambiguous Terms in Wikipedia: 58.8%,[object Object],Sparsity: less data for some DBpedia resources,[object Object],14,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Histogram: URI occurrences,[object Object],Many “rare” URIs, ,[object Object],(few links on Wikipedia),[object Object],Most of previous work deals with these entities:,[object Object],People, Organization, Location,[object Object],Few “popular” URIs,[object Object],(lots of links on Wikipedia),[object Object],log(n(uri)))),[object Object],15,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Histogram: Surface Form Ambiguity,[object Object],Many “unambiguous” surface forms,[object Object],Max: 1199 (log=7.08),[object Object],Min: 1,[object Object],Mean: 1.328949,[object Object],Few very “ambiguous” surface forms,[object Object],log(n(uri,sf)))),[object Object],16,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Ambiguity,[object Object],17,[object Object],What are the most ambiguous surface forms?,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Name Variation,[object Object],18,[object Object],What are the URIs with many surface forms?,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
How did we approach the challenge?,[object Object],19,[object Object]
A 4-stage approach,[object Object],Spotting,[object Object],Candidate Mapping,[object Object],Disambiguation,[object Object],Linking,[object Object],20,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Stage 1: Spotting,[object Object],Find substrings that seem worthy of annotation,[object Object],Naïve implementation (impractical),[object Object],all n-grams of length (1,|text|),[object Object],Input:,[object Object],(…) Upon their return, Lennon and McCartney went to New York ,[object Object],to announce the formation of Apple Corps. ,[object Object],Output:,[object Object],“Lennon”, “McCartney”, “New York”, “Apple Corps”,[object Object],21,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Spotting in DBpedia Spotlight,[object Object],Detect that the label (surface form) of a DBpedia Resource was mentioned,[object Object],Lexicalized, Aho-Corasick algorithm (LingPipe),[object Object],Name variations from redirects, disambiguation pages, anchor texts,[object Object],Advantages: ,[object Object],Simple implementation, well studied problem,,[object Object],Produces a reduced set of spots, ,[object Object],Relies on user provided terms.,[object Object],Drawback: ,[object Object],high memory requirements (~7G),[object Object],22,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Stage 2: Candidate Mapping,[object Object],What are the possible senses of a given surface form (the candidate DBpedia resources)?,[object Object],Input:,[object Object],“Lennon”, “McCartney”, “New York”, “Apple Corps”,[object Object],Output:,[object Object],“Lennon”: { Lennon_(album), Lennon,_Michigan, … },[object Object],“McCartney”: { McCartney(surname), Paul_McCartney, … },[object Object],“New York”: { New_York_State, New_York_City, … },[object Object],“Apple Corps”: { Apple_Corps},[object Object],23,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Candidate Mapping in DBpedia Spotlight,[object Object],Sources of mappings between surface forms and DBpedia Resources,[object Object],Page titles offer “chosen names” for resources,[object Object],Redirects offer alternative spellings, aliases, etc.,[object Object],Disambiguation Pages: link a common term to many resources,[object Object],24,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Candidate Map: Disambiguation Pages,[object Object],Collectively provide a list of ambiguous terms and meanings for each,[object Object],25,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Candidate Map: Redirects,[object Object],AAPL,[object Object],Apple (Company),[object Object],Apple (Computers),[object Object],Apple (company),[object Object],Apple (computer),[object Object],Apple Company,[object Object],Apple Computer,[object Object],Apple Computer Co.,[object Object],Apple Computer Inc.,[object Object],Apple Computer Incorporated,[object Object],Apple Computer, Inc,[object Object],Apple Computer, Inc.,[object Object],Apple Computers,[object Object],Apple Inc,[object Object],Apple Incorporate,[object Object],Apple Incorporated,[object Object],Apple India,[object Object],Apple comp,[object Object],Apple compputer,[object Object],Apple computer,[object Object],Apple computer Inc,[object Object],Apple computers,[object Object],Apple inc,[object Object],Apple inc.,[object Object],Apple incoporated,[object Object],Apple incorporated,[object Object],Apple pc,[object Object],Apple's,[object Object],Apple, Inc,[object Object],Apple, Inc.,[object Object],Apple,inc.,[object Object],Apple.com,[object Object],AppleComputer,[object Object],Bowman Bank,[object Object],Cripple Inc.,[object Object],Inc. Apple Computer,[object Object],Jobs and Wozniak,[object Object],Option-Shift-K,[object Object], Inc.,[object Object],26,[object Object],Apple_Inc,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Stage 3: Disambiguation,[object Object],Select the correct candidate DBpedia Resource for a given surface form.,[object Object],Decision is made based on the context(1) the surface form was mentioned,[object Object],con·text  (kntkst)n.,[object Object],1. the parts of a discourse that surround a word or passage and can throw light on its meaning,[object Object],2. The circumstances in which an event occurs; a setting.,[object Object],27,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object],http://mw1.merriam-webster.com/dictionary/context,[object Object]
Learning the Context for a resource,[object Object],Collect context for DBpedia Resources from Wikipedia,[object Object],Types of context,[object Object],Wikipedia Pages ,[object Object],Definitions from disambiguation pages,[object Object],Paragraphs that link to resources,[object Object],28,[object Object],(…) Upon their return, Lennon and McCartney went to New York to announce the formation of Apple Corps. ,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Disambiguation in DBpedia Spotlight,[object Object],Model DBpedia Resources as vectors of terms found in Wikipedia text,[object Object],Define functions for term scoring and vector similarity (e.g. frequency and cosine),[object Object],Rank candidate resource vectors based on their similarity with vector of input text,[object Object],Choose highest ranking candidate,[object Object],29,[object Object],Lennon = {Beatles,McCartney,rock,guitar,...},[object Object],Lennon = {tf(Beatles)=320,tf(McCartney)=100,...},[object Object],Cos(Input,Lennon) = 0.12,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Scoring Strategies,[object Object],TF*IDF (Term Freq. * Inverse Doc. Freq.),[object Object],TF: insight into the relevance of the term in the context of a DBpedia Resource,[object Object],IDF: insight into the rarity of the term. Co-occurrence of rare terms is more informative,[object Object],ICF: Inverse Candidate Frequency,[object Object],IDF is the “rarity” in the entire Wikipedia,[object Object],ICF is the rarity of a word with relation to the possible senses only,[object Object],30,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Context-Independent Strategies,[object Object],NAÏVE,[object Object],Use surface form to build URI: “berlin” -> dbpedia:Berlin,[object Object],PROMINENCE,[object Object],P(u): n(u) / N (what is the ‘popularity’/importance of this URL),[object Object],n(u): number of times URI u occurred,[object Object],N: total number of occurrences,[object Object],Intuition: URIs that have appeared a lot are more likely to appear again,[object Object],DEFAULT SENSE,[object Object],P(u|s): n(u,s) / n(s),[object Object],n(u,s): number of times URI u occurred with surface form s,[object Object],Intuition: some surface forms are strongly associated to some specific URIs,[object Object],31,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Linking (Configuration),[object Object],Decide which spots to annotate with links to the disambiguated resources,[object Object],Different use cases have different needs,[object Object],Only annotate prominent resources?,[object Object],Only if you’re sure disambiguation is correct?,[object Object],Only people?,[object Object],Only things related to Berlin?,[object Object],32,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Linking in DBpedia Spotlight,[object Object],Can be configured based on:,[object Object],Thresholds,[object Object],Confidence,[object Object],Prominence (support),[object Object],Whitelist or Blacklist of types,[object Object],Hide all people, Show only organizations,[object Object],Complex definition of a “type” through a SPARQL query.,[object Object],33,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
How  well did it work?,[object Object],34,[object Object]
Evaluation: Disambiguation,[object Object],Used held out (unseen) Wikipedia occurrences as test data,[object Object],Evaluates accuracy of disambiguation stage,[object Object],Baselines,[object Object],Random: performs well with low ambiguity,[object Object],Default Sense: only prominence, without context,[object Object],Default Similarity (TF*IDF) : Lucene implementation,[object Object],35,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Disambiguation Evaluation Results,[object Object],36,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Evaluation: Annotation,[object Object],News text, different topics,[object Object],Hand-annotated examples by 4 annotators,[object Object],Gold standard from agreement	,[object Object],Evaluates precision and recall of annotations.,[object Object],37,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Annotation Evaluation Results (2),[object Object],38,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Annotation Evaluation Results,[object Object],39,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
Conclusions,[object Object],DBpedia Spotlight: a configurable annotation tool to support a variety of use cases,[object Object],Very simple methods work surprisingly well for disambiguation,[object Object],More work is needed to alleviate sparsity,[object Object],Most challenging step is linking,[object Object],More evaluation on larger annotation datasets is needed,[object Object],40,[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]
What are the next steps?,[object Object],41,[object Object]
A preview of next release,[object Object],CORS-enabled + jQuery client,[object Object],One line to annotate any web page:,[object Object],A new demo interface: based on the plugin,[object Object],Types: DBpedia 3.7, Freebase, Schema.org,[object Object],New configuration parameters,[object Object],E.g. perform smarter spotting,[object Object],Easier install: maven2, jar, debian package,[object Object],42,[object Object],$(“div”).annotate(),[object Object],Mendes, Jakob, Garcia-Silva, Bizer. DBpedia Spotlight: Shedding Light on the Web of Documents,[object Object]

More Related Content

What's hot

Training Week: Create a Knowledge Graph: A Simple ML Approach
Training Week: Create a Knowledge Graph: A Simple ML Approach Training Week: Create a Knowledge Graph: A Simple ML Approach
Training Week: Create a Knowledge Graph: A Simple ML Approach Neo4j
 
BrightonSEO March 2021 | Dan Taylor, Image Entity Tags
BrightonSEO March 2021 | Dan Taylor, Image Entity TagsBrightonSEO March 2021 | Dan Taylor, Image Entity Tags
BrightonSEO March 2021 | Dan Taylor, Image Entity TagsDan Taylor
 
ESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge GraphsESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge GraphsPeter Haase
 
Expediente Xpath #SEOnderground 2021
Expediente Xpath  #SEOnderground 2021Expediente Xpath  #SEOnderground 2021
Expediente Xpath #SEOnderground 2021MJ Cachón Yáñez
 
Brighton SEO July 2021 - Google Discover - optimisation, measurement, tech seo
Brighton SEO July 2021 - Google Discover - optimisation, measurement, tech seoBrighton SEO July 2021 - Google Discover - optimisation, measurement, tech seo
Brighton SEO July 2021 - Google Discover - optimisation, measurement, tech seoTom Capper
 
Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기
Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기
Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기Hyojun Kim
 
Antifragility in Digital Marketing
Antifragility in Digital MarketingAntifragility in Digital Marketing
Antifragility in Digital MarketingElias Dabbas
 
How Graph Databases efficiently store, manage and query connected data at s...
How Graph Databases efficiently  store, manage and query  connected data at s...How Graph Databases efficiently  store, manage and query  connected data at s...
How Graph Databases efficiently store, manage and query connected data at s...jexp
 
Deep Contexualized Representation
Deep Contexualized RepresentationDeep Contexualized Representation
Deep Contexualized RepresentationMinh Pham
 
OntoPiA e il knowledge graph della pubblica amministrazione italiana
OntoPiA e il knowledge graph della pubblica amministrazione italianaOntoPiA e il knowledge graph della pubblica amministrazione italiana
OntoPiA e il knowledge graph della pubblica amministrazione italianaGiorgia Lodi
 
The Future of Link Building: What Got Us Here, Won't Get Us There
The Future of Link Building: What Got Us Here, Won't Get Us ThereThe Future of Link Building: What Got Us Here, Won't Get Us There
The Future of Link Building: What Got Us Here, Won't Get Us TherePaddy Moogan
 
Lexical Semantics, Semantic Similarity and Relevance for SEO
Lexical Semantics, Semantic Similarity and Relevance for SEOLexical Semantics, Semantic Similarity and Relevance for SEO
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
 
Applications of Large Language Models in Materials Discovery and Design
Applications of Large Language Models in Materials Discovery and DesignApplications of Large Language Models in Materials Discovery and Design
Applications of Large Language Models in Materials Discovery and DesignAnubhav Jain
 
Common MongoDB Use Cases
Common MongoDB Use CasesCommon MongoDB Use Cases
Common MongoDB Use CasesDATAVERSITY
 
Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH)
Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH) Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH)
Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH) David Fombella Pombal
 
Introduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdfIntroduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdfHeather Hedden
 
Natural Language Processing with Graph Databases and Neo4j
Natural Language Processing with Graph Databases and Neo4jNatural Language Processing with Graph Databases and Neo4j
Natural Language Processing with Graph Databases and Neo4jWilliam Lyon
 
Efficient Spark Analytics on Encrypted Data with Gidon Gershinsky
 Efficient Spark Analytics on Encrypted Data with Gidon Gershinsky Efficient Spark Analytics on Encrypted Data with Gidon Gershinsky
Efficient Spark Analytics on Encrypted Data with Gidon GershinskyDatabricks
 

What's hot (20)

Training Week: Create a Knowledge Graph: A Simple ML Approach
Training Week: Create a Knowledge Graph: A Simple ML Approach Training Week: Create a Knowledge Graph: A Simple ML Approach
Training Week: Create a Knowledge Graph: A Simple ML Approach
 
BrightonSEO March 2021 | Dan Taylor, Image Entity Tags
BrightonSEO March 2021 | Dan Taylor, Image Entity TagsBrightonSEO March 2021 | Dan Taylor, Image Entity Tags
BrightonSEO March 2021 | Dan Taylor, Image Entity Tags
 
ESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge GraphsESWC 2017 Tutorial Knowledge Graphs
ESWC 2017 Tutorial Knowledge Graphs
 
Expediente Xpath #SEOnderground 2021
Expediente Xpath  #SEOnderground 2021Expediente Xpath  #SEOnderground 2021
Expediente Xpath #SEOnderground 2021
 
Brighton SEO July 2021 - Google Discover - optimisation, measurement, tech seo
Brighton SEO July 2021 - Google Discover - optimisation, measurement, tech seoBrighton SEO July 2021 - Google Discover - optimisation, measurement, tech seo
Brighton SEO July 2021 - Google Discover - optimisation, measurement, tech seo
 
Python for SEO
Python for SEOPython for SEO
Python for SEO
 
Emacs on WSL
Emacs on WSLEmacs on WSL
Emacs on WSL
 
Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기
Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기
Luft : 10억 데이터를 10초만에 쿼리하는 DB 개발기
 
Antifragility in Digital Marketing
Antifragility in Digital MarketingAntifragility in Digital Marketing
Antifragility in Digital Marketing
 
How Graph Databases efficiently store, manage and query connected data at s...
How Graph Databases efficiently  store, manage and query  connected data at s...How Graph Databases efficiently  store, manage and query  connected data at s...
How Graph Databases efficiently store, manage and query connected data at s...
 
Deep Contexualized Representation
Deep Contexualized RepresentationDeep Contexualized Representation
Deep Contexualized Representation
 
OntoPiA e il knowledge graph della pubblica amministrazione italiana
OntoPiA e il knowledge graph della pubblica amministrazione italianaOntoPiA e il knowledge graph della pubblica amministrazione italiana
OntoPiA e il knowledge graph della pubblica amministrazione italiana
 
The Future of Link Building: What Got Us Here, Won't Get Us There
The Future of Link Building: What Got Us Here, Won't Get Us ThereThe Future of Link Building: What Got Us Here, Won't Get Us There
The Future of Link Building: What Got Us Here, Won't Get Us There
 
Lexical Semantics, Semantic Similarity and Relevance for SEO
Lexical Semantics, Semantic Similarity and Relevance for SEOLexical Semantics, Semantic Similarity and Relevance for SEO
Lexical Semantics, Semantic Similarity and Relevance for SEO
 
Applications of Large Language Models in Materials Discovery and Design
Applications of Large Language Models in Materials Discovery and DesignApplications of Large Language Models in Materials Discovery and Design
Applications of Large Language Models in Materials Discovery and Design
 
Common MongoDB Use Cases
Common MongoDB Use CasesCommon MongoDB Use Cases
Common MongoDB Use Cases
 
Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH)
Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH) Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH)
Neo4j Introduction (Basics, Cypher, RDBMS to GRAPH)
 
Introduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdfIntroduction to Knowledge Graphs for Information Architects.pdf
Introduction to Knowledge Graphs for Information Architects.pdf
 
Natural Language Processing with Graph Databases and Neo4j
Natural Language Processing with Graph Databases and Neo4jNatural Language Processing with Graph Databases and Neo4j
Natural Language Processing with Graph Databases and Neo4j
 
Efficient Spark Analytics on Encrypted Data with Gidon Gershinsky
 Efficient Spark Analytics on Encrypted Data with Gidon Gershinsky Efficient Spark Analytics on Encrypted Data with Gidon Gershinsky
Efficient Spark Analytics on Encrypted Data with Gidon Gershinsky
 

Viewers also liked

A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...
A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...
A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...Pablo Mendes
 
Graph Based Machine Learning with Applications to Media Analytics
Graph Based Machine Learning with Applications to Media AnalyticsGraph Based Machine Learning with Applications to Media Analytics
Graph Based Machine Learning with Applications to Media AnalyticsNYC Predictive Analytics
 
How to use Latent Semantic Analysis to Glean Real Insight - Franco Amalfi
How to use Latent Semantic Analysis to Glean Real Insight - Franco AmalfiHow to use Latent Semantic Analysis to Glean Real Insight - Franco Amalfi
How to use Latent Semantic Analysis to Glean Real Insight - Franco AmalfiSocial Media Camp
 
Latent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyLatent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyAuro Tripathy
 
Latent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with SparkLatent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with SparkSandy Ryza
 
Introduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic AnalysisIntroduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic AnalysisNYC Predictive Analytics
 
Syntactic Analysis
Syntactic AnalysisSyntactic Analysis
Syntactic AnalysisAleli Lac
 
Semantics: Seven types of meaning
Semantics: Seven types of meaningSemantics: Seven types of meaning
Semantics: Seven types of meaningMiftadia Laula
 
NERD: Evaluating Named Entity Recognition Tools in the Web of Data
NERD: Evaluating Named Entity Recognition Tools in the Web of DataNERD: Evaluating Named Entity Recognition Tools in the Web of Data
NERD: Evaluating Named Entity Recognition Tools in the Web of DataGiuseppe Rizzo
 
Collaborative Filtering at Spotify
Collaborative Filtering at SpotifyCollaborative Filtering at Spotify
Collaborative Filtering at SpotifyErik Bernhardsson
 
DBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, DublinDBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, Dublinm_ackermann
 

Viewers also liked (15)

A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...
A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...
A Virtuous Cycle of Semantic Enhancement with DBpedia Spotlight - SemTech Ber...
 
LOD2 Webinar Series: DBpedia Spotlight
LOD2 Webinar Series: DBpedia SpotlightLOD2 Webinar Series: DBpedia Spotlight
LOD2 Webinar Series: DBpedia Spotlight
 
GoogLeNet Insights
GoogLeNet InsightsGoogLeNet Insights
GoogLeNet Insights
 
Entity Linking
Entity LinkingEntity Linking
Entity Linking
 
Graph Based Machine Learning with Applications to Media Analytics
Graph Based Machine Learning with Applications to Media AnalyticsGraph Based Machine Learning with Applications to Media Analytics
Graph Based Machine Learning with Applications to Media Analytics
 
How to use Latent Semantic Analysis to Glean Real Insight - Franco Amalfi
How to use Latent Semantic Analysis to Glean Real Insight - Franco AmalfiHow to use Latent Semantic Analysis to Glean Real Insight - Franco Amalfi
How to use Latent Semantic Analysis to Glean Real Insight - Franco Amalfi
 
Latent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro TripathyLatent Semanctic Analysis Auro Tripathy
Latent Semanctic Analysis Auro Tripathy
 
Latent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with SparkLatent Semantic Analysis of Wikipedia with Spark
Latent Semantic Analysis of Wikipedia with Spark
 
Introduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic AnalysisIntroduction to Probabilistic Latent Semantic Analysis
Introduction to Probabilistic Latent Semantic Analysis
 
Syntactic Analysis
Syntactic AnalysisSyntactic Analysis
Syntactic Analysis
 
Semantics: Seven types of meaning
Semantics: Seven types of meaningSemantics: Seven types of meaning
Semantics: Seven types of meaning
 
NERD: Evaluating Named Entity Recognition Tools in the Web of Data
NERD: Evaluating Named Entity Recognition Tools in the Web of DataNERD: Evaluating Named Entity Recognition Tools in the Web of Data
NERD: Evaluating Named Entity Recognition Tools in the Web of Data
 
Semantics
SemanticsSemantics
Semantics
 
Collaborative Filtering at Spotify
Collaborative Filtering at SpotifyCollaborative Filtering at Spotify
Collaborative Filtering at Spotify
 
DBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, DublinDBpedia Tutorial - Feb 2015, Dublin
DBpedia Tutorial - Feb 2015, Dublin
 

Similar to DBpedia Spotlight at I-SEMANTICS 2011

Capturing emerging relations between schema ontologies on the Web of Data
Capturing emerging relations between schema ontologies on the Web of DataCapturing emerging relations between schema ontologies on the Web of Data
Capturing emerging relations between schema ontologies on the Web of DataAndriy Nikolov
 
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...Artificial Intelligence Institute at UofSC
 
Filtering Inaccurate Entity Co-references on the Linked Open Data
Filtering Inaccurate Entity Co-references on the Linked Open DataFiltering Inaccurate Entity Co-references on the Linked Open Data
Filtering Inaccurate Entity Co-references on the Linked Open Dataebrahim_bagheri
 
bridging formal semantics and social semantics on the web
bridging formal semantics and social semantics on the webbridging formal semantics and social semantics on the web
bridging formal semantics and social semantics on the webFabien Gandon
 
5 Lessons Learned from Designing Neural Models for Information Retrieval
5 Lessons Learned from Designing Neural Models for Information Retrieval5 Lessons Learned from Designing Neural Models for Information Retrieval
5 Lessons Learned from Designing Neural Models for Information RetrievalBhaskar Mitra
 
The Empirical Turn in Knowledge Representation
The Empirical Turn in Knowledge RepresentationThe Empirical Turn in Knowledge Representation
The Empirical Turn in Knowledge RepresentationFrank van Harmelen
 
Web & text mining lecture10
Web & text mining lecture10Web & text mining lecture10
Web & text mining lecture10Houw Liong The
 
Similarity on DBpedia
Similarity on DBpediaSimilarity on DBpedia
Similarity on DBpediaSamantha Lam
 
Mongodbworkshop I: get started
Mongodbworkshop I: get startedMongodbworkshop I: get started
Mongodbworkshop I: get startedVivian S. Zhang
 
Towards research data knowledge graphs
Towards research data knowledge graphsTowards research data knowledge graphs
Towards research data knowledge graphsStefan Dietze
 
2009 December NodeXL Overview
2009 December NodeXL Overview2009 December NodeXL Overview
2009 December NodeXL OverviewMarc Smith
 
Harmony project - JISC Synthesis meeting 2001
Harmony project - JISC Synthesis meeting 2001Harmony project - JISC Synthesis meeting 2001
Harmony project - JISC Synthesis meeting 2001Dan Brickley
 
Wikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization SystemsWikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization SystemsJakob .
 
Moving Library Metadata Toward Linked Data: Opportunities Provided by the eX...
Moving Library Metadata Toward Linked Data:  Opportunities Provided by the eX...Moving Library Metadata Toward Linked Data:  Opportunities Provided by the eX...
Moving Library Metadata Toward Linked Data: Opportunities Provided by the eX...Jennifer Bowen
 
Ch03 Mining Massive Data Sets stanford
Ch03 Mining Massive Data Sets  stanfordCh03 Mining Massive Data Sets  stanford
Ch03 Mining Massive Data Sets stanfordSakthivel C R
 

Similar to DBpedia Spotlight at I-SEMANTICS 2011 (20)

ITWS Capstone Lecture (Spring 2013)
ITWS Capstone Lecture (Spring 2013)ITWS Capstone Lecture (Spring 2013)
ITWS Capstone Lecture (Spring 2013)
 
The Semantic Web: RPI ITWS Capstone (Fall 2012)
The Semantic Web: RPI ITWS Capstone (Fall 2012)The Semantic Web: RPI ITWS Capstone (Fall 2012)
The Semantic Web: RPI ITWS Capstone (Fall 2012)
 
Capturing emerging relations between schema ontologies on the Web of Data
Capturing emerging relations between schema ontologies on the Web of DataCapturing emerging relations between schema ontologies on the Web of Data
Capturing emerging relations between schema ontologies on the Web of Data
 
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
Pablo Mendes' Defense: Adaptive Semantic Annotation of Entity and Concept Men...
 
Filtering Inaccurate Entity Co-references on the Linked Open Data
Filtering Inaccurate Entity Co-references on the Linked Open DataFiltering Inaccurate Entity Co-references on the Linked Open Data
Filtering Inaccurate Entity Co-references on the Linked Open Data
 
bridging formal semantics and social semantics on the web
bridging formal semantics and social semantics on the webbridging formal semantics and social semantics on the web
bridging formal semantics and social semantics on the web
 
Towards Knowledge-Enabled Society
Towards Knowledge-Enabled SocietyTowards Knowledge-Enabled Society
Towards Knowledge-Enabled Society
 
5 Lessons Learned from Designing Neural Models for Information Retrieval
5 Lessons Learned from Designing Neural Models for Information Retrieval5 Lessons Learned from Designing Neural Models for Information Retrieval
5 Lessons Learned from Designing Neural Models for Information Retrieval
 
The Empirical Turn in Knowledge Representation
The Empirical Turn in Knowledge RepresentationThe Empirical Turn in Knowledge Representation
The Empirical Turn in Knowledge Representation
 
Web & text mining lecture10
Web & text mining lecture10Web & text mining lecture10
Web & text mining lecture10
 
Similarity on DBpedia
Similarity on DBpediaSimilarity on DBpedia
Similarity on DBpedia
 
MongoDB Workshop
MongoDB WorkshopMongoDB Workshop
MongoDB Workshop
 
Mongodbworkshop I: get started
Mongodbworkshop I: get startedMongodbworkshop I: get started
Mongodbworkshop I: get started
 
Towards research data knowledge graphs
Towards research data knowledge graphsTowards research data knowledge graphs
Towards research data knowledge graphs
 
2009 December NodeXL Overview
2009 December NodeXL Overview2009 December NodeXL Overview
2009 December NodeXL Overview
 
Where is the World is my Open Government Data?
Where is the World is my Open Government Data?Where is the World is my Open Government Data?
Where is the World is my Open Government Data?
 
Harmony project - JISC Synthesis meeting 2001
Harmony project - JISC Synthesis meeting 2001Harmony project - JISC Synthesis meeting 2001
Harmony project - JISC Synthesis meeting 2001
 
Wikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization SystemsWikipedia as source of collaboratively created Knowledge Organization Systems
Wikipedia as source of collaboratively created Knowledge Organization Systems
 
Moving Library Metadata Toward Linked Data: Opportunities Provided by the eX...
Moving Library Metadata Toward Linked Data:  Opportunities Provided by the eX...Moving Library Metadata Toward Linked Data:  Opportunities Provided by the eX...
Moving Library Metadata Toward Linked Data: Opportunities Provided by the eX...
 
Ch03 Mining Massive Data Sets stanford
Ch03 Mining Massive Data Sets  stanfordCh03 Mining Massive Data Sets  stanford
Ch03 Mining Massive Data Sets stanford
 

More from Pablo Mendes

Entity Aware Click Graph
Entity Aware Click GraphEntity Aware Click Graph
Entity Aware Click GraphPablo Mendes
 
WWW2012 Tutorial Visualizing SPARQL Queries
WWW2012 Tutorial Visualizing SPARQL QueriesWWW2012 Tutorial Visualizing SPARQL Queries
WWW2012 Tutorial Visualizing SPARQL QueriesPablo Mendes
 
Sieve - Data Quality and Fusion - LWDM2012
Sieve - Data Quality and Fusion - LWDM2012Sieve - Data Quality and Fusion - LWDM2012
Sieve - Data Quality and Fusion - LWDM2012Pablo Mendes
 
Ligado nos Políticos at ESWC'2011 Workshop
Ligado nos Políticos at ESWC'2011 WorkshopLigado nos Políticos at ESWC'2011 Workshop
Ligado nos Políticos at ESWC'2011 WorkshopPablo Mendes
 
SMWCon Fall 2011 Lightning Talk
SMWCon Fall 2011 Lightning TalkSMWCon Fall 2011 Lightning Talk
SMWCon Fall 2011 Lightning TalkPablo Mendes
 
Dados Ligados (Linked Data) CONSEGI 2011
Dados Ligados (Linked Data) CONSEGI 2011Dados Ligados (Linked Data) CONSEGI 2011
Dados Ligados (Linked Data) CONSEGI 2011Pablo Mendes
 
Cuebee Architecture
Cuebee ArchitectureCuebee Architecture
Cuebee ArchitecturePablo Mendes
 
Twarql Architecture - Streaming Annotated Tweets
Twarql Architecture - Streaming Annotated TweetsTwarql Architecture - Streaming Annotated Tweets
Twarql Architecture - Streaming Annotated TweetsPablo Mendes
 
Dynamic Associative Relationships on the Linked Open Data Web
Dynamic Associative Relationships on the Linked Open Data WebDynamic Associative Relationships on the Linked Open Data Web
Dynamic Associative Relationships on the Linked Open Data WebPablo Mendes
 

More from Pablo Mendes (9)

Entity Aware Click Graph
Entity Aware Click GraphEntity Aware Click Graph
Entity Aware Click Graph
 
WWW2012 Tutorial Visualizing SPARQL Queries
WWW2012 Tutorial Visualizing SPARQL QueriesWWW2012 Tutorial Visualizing SPARQL Queries
WWW2012 Tutorial Visualizing SPARQL Queries
 
Sieve - Data Quality and Fusion - LWDM2012
Sieve - Data Quality and Fusion - LWDM2012Sieve - Data Quality and Fusion - LWDM2012
Sieve - Data Quality and Fusion - LWDM2012
 
Ligado nos Políticos at ESWC'2011 Workshop
Ligado nos Políticos at ESWC'2011 WorkshopLigado nos Políticos at ESWC'2011 Workshop
Ligado nos Políticos at ESWC'2011 Workshop
 
SMWCon Fall 2011 Lightning Talk
SMWCon Fall 2011 Lightning TalkSMWCon Fall 2011 Lightning Talk
SMWCon Fall 2011 Lightning Talk
 
Dados Ligados (Linked Data) CONSEGI 2011
Dados Ligados (Linked Data) CONSEGI 2011Dados Ligados (Linked Data) CONSEGI 2011
Dados Ligados (Linked Data) CONSEGI 2011
 
Cuebee Architecture
Cuebee ArchitectureCuebee Architecture
Cuebee Architecture
 
Twarql Architecture - Streaming Annotated Tweets
Twarql Architecture - Streaming Annotated TweetsTwarql Architecture - Streaming Annotated Tweets
Twarql Architecture - Streaming Annotated Tweets
 
Dynamic Associative Relationships on the Linked Open Data Web
Dynamic Associative Relationships on the Linked Open Data WebDynamic Associative Relationships on the Linked Open Data Web
Dynamic Associative Relationships on the Linked Open Data Web
 

Recently uploaded

Where developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is goingWhere developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is goingFrancesco Corti
 
UiPath Studio Web workshop series - Day 1
UiPath Studio Web workshop series  - Day 1UiPath Studio Web workshop series  - Day 1
UiPath Studio Web workshop series - Day 1DianaGray10
 
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptxGraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptxNeo4j
 
Oracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptxOracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptxSatishbabu Gunukula
 
Outage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedIn
Outage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedInOutage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedIn
Outage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedInThousandEyes
 
How to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptxHow to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptxKaustubhBhavsar6
 
LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0DanBrown980551
 
The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)codyslingerland1
 
Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024
Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024
Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024Alkin Tezuysal
 
2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdf2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdfThe Good Food Institute
 
Technical SEO for Improved Accessibility WTS FEST
Technical SEO for Improved Accessibility  WTS FESTTechnical SEO for Improved Accessibility  WTS FEST
Technical SEO for Improved Accessibility WTS FESTBillieHyde
 
EMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarEMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarThousandEyes
 
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdfQ4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdfTejal81
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)IES VE
 
Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.IPLOOK Networks
 
Keep Your Finger on the Pulse of Your Building's Performance with IES Live
Keep Your Finger on the Pulse of Your Building's Performance with IES LiveKeep Your Finger on the Pulse of Your Building's Performance with IES Live
Keep Your Finger on the Pulse of Your Building's Performance with IES LiveIES VE
 
CyberSecurity - Computers In Libraries 2024
CyberSecurity - Computers In Libraries 2024CyberSecurity - Computers In Libraries 2024
CyberSecurity - Computers In Libraries 2024Brian Pichman
 
AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024Brian Pichman
 
From the origin to the future of Open Source model and business
From the origin to the future of  Open Source model and businessFrom the origin to the future of  Open Source model and business
From the origin to the future of Open Source model and businessFrancesco Corti
 
Extra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfExtra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfInfopole1
 

Recently uploaded (20)

Where developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is goingWhere developers are challenged, what developers want and where DevEx is going
Where developers are challenged, what developers want and where DevEx is going
 
UiPath Studio Web workshop series - Day 1
UiPath Studio Web workshop series  - Day 1UiPath Studio Web workshop series  - Day 1
UiPath Studio Web workshop series - Day 1
 
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptxGraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
GraphSummit Copenhagen 2024 - Neo4j Vision and Roadmap.pptx
 
Oracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptxOracle Database 23c Security New Features.pptx
Oracle Database 23c Security New Features.pptx
 
Outage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedIn
Outage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedInOutage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedIn
Outage Analysis: March 5th/6th 2024 Meta, Comcast, and LinkedIn
 
How to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptxHow to become a GDSC Lead GDSC MI AOE.pptx
How to become a GDSC Lead GDSC MI AOE.pptx
 
LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0LF Energy Webinar - Unveiling OpenEEMeter 4.0
LF Energy Webinar - Unveiling OpenEEMeter 4.0
 
The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)The New Cloud World Order Is FinOps (Slideshow)
The New Cloud World Order Is FinOps (Slideshow)
 
Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024
Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024
Design and Modeling for MySQL SCALE 21X Pasadena, CA Mar 2024
 
2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdf2024.03.12 Cost drivers of cultivated meat production.pdf
2024.03.12 Cost drivers of cultivated meat production.pdf
 
Technical SEO for Improved Accessibility WTS FEST
Technical SEO for Improved Accessibility  WTS FESTTechnical SEO for Improved Accessibility  WTS FEST
Technical SEO for Improved Accessibility WTS FEST
 
EMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? WebinarEMEA What is ThousandEyes? Webinar
EMEA What is ThousandEyes? Webinar
 
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdfQ4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
Q4 2023 Quarterly Investor Presentation - FINAL - v1.pdf
 
The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)The Importance of Indoor Air Quality (English)
The Importance of Indoor Air Quality (English)
 
Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.Introduction - IPLOOK NETWORKS CO., LTD.
Introduction - IPLOOK NETWORKS CO., LTD.
 
Keep Your Finger on the Pulse of Your Building's Performance with IES Live
Keep Your Finger on the Pulse of Your Building's Performance with IES LiveKeep Your Finger on the Pulse of Your Building's Performance with IES Live
Keep Your Finger on the Pulse of Your Building's Performance with IES Live
 
CyberSecurity - Computers In Libraries 2024
CyberSecurity - Computers In Libraries 2024CyberSecurity - Computers In Libraries 2024
CyberSecurity - Computers In Libraries 2024
 
AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024AI Workshops at Computers In Libraries 2024
AI Workshops at Computers In Libraries 2024
 
From the origin to the future of Open Source model and business
From the origin to the future of  Open Source model and businessFrom the origin to the future of  Open Source model and business
From the origin to the future of Open Source model and business
 
Extra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdfExtra-120324-Visite-Entreprise-icare.pdf
Extra-120324-Visite-Entreprise-icare.pdf
 

DBpedia Spotlight at I-SEMANTICS 2011

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.
  • 46.

Editor's Notes

  1. This use case requires merging streaming data with background knowledge information (e.g. from DBpedia). Examples of ?category include category:Wi-Fi devices and category:Touchscreen portable media players amongst others. As a result, without having to elicit all products of interest as keywords to lter a stream, a user is able to leverage relationships in background knowledge to more effectively narrow down the stream of tweets to a subset of interest.
  2. This use case requires merging streaming data with background knowledge information (e.g. from DBpedia). Examples of ?category include category:Wi-Fi devices and category:Touchscreen portable media players amongst others. As a result, without having to elicit all products of interest as keywords to lter a stream, a user is able to leverage relationships in background knowledge to more effectively narrow down the stream of tweets to a subset of interest.
  3. This use case requires merging streaming data with background knowledge information (e.g. from DBpedia). Examples of ?category include category:Wi-Fi devices and category:Touchscreen portable media players amongst others. As a result, without having to elicit all products of interest as keywords to lter a stream, a user is able to leverage relationships in background knowledge to more effectively narrow down the stream of tweets to a subset of interest.
  4. $ gunzip -c MostCommon-surfaceForm.count.gz | grep -Pc "\\t1$"4258908$ gunzip -c MostCommon-surfaceForm.count.gz | wc -l72442894258908 / 7244289 = 0.58789868819424514952399055311018
  5. Max = 200,474 (log = 12.2)Min = 1Mean = 8.343878
  6. Lexicalized: uses a list of resource namesComes from titles, redirects, disambiguates, anchor texts
  7. The agreement between individual annotators is:Annotator 1 vs Annotator 2 (Kappa = 0.674)Annotator 1 vs Annotator 3 (Kappa = 0.606)Annotator 2 vs Annotator 3 (Kappa = 0.577)Annotator 2 vs Annotator 4 (Kappa = 0.528)Annotator 1 vs Annotator 4 (Kappa = 0.469)Annotator 3 vs Annotator 4 (Kappa = 0.385)