The document describes an approach for automated comparative table generation to facilitate human intervention in multi-entity resolution. The approach includes holistic property matching to derive property cliques, measuring clique goodness based on discriminability, abundance and semantics, and generating comparative tables by selecting high-scoring cliques and values based on constraints. Experiments show the approach effectively matches properties and ranks cliques, and human tests found the generated tables helped identify matching entities.
Harvesting and Normalization at the Digital Public Library of America: Lesson...Sandra McIntyre
Dixon, K., McIntyre, S., and Rudersdorf, A. (2014). Harvesting and normalization at the Digital Public Library of America: Lessons from a diverse aggregation. Panel presentation at ALCTS Metadata Interest Group, American Library Association Midwinter Meeting, Philadelphia, PA, January 25, 2014.
Co-presenters: Kristy Berry Dixon, Sandra McIntyre, and Amy Rudersdorf.
The document describes two people, Ann Smith and Dan, as nodes in a graph. Ann is described by her name, user ID, and a unique identifier. Dan likes Ann, which is represented by an edge between their nodes labeled "LIKES". The graph also includes the date of the like.
Gleaning Types for Literals in RDF with Application to Entity SummarizationKalpa Gunaratna
ESWC 2016 talk about how to compute types (ontology classes) for literals and add semantics to them, making them richer. Then utilize them in an entity summarization usecase.
The document discusses different approaches to generating biographies through natural language processing, including information extraction and language modeling. It describes using information extraction patterns learned from Wikipedia to extract fields like date of birth and place of birth, and bouncing between Wikipedia and Google search results to learn patterns for other fields with less structured data. It also proposes selecting and ranking sentences from search results to improve recall when information extraction may miss relevant sentences. The goal is to build biographies by combining these techniques for high precision on structured fields and better recall on more complex fields.
Researchers in ancient text corpora can take control over their data. We show a way to do so by means of Text-Fabric.
Co-production of Cody Kingham and Dirk Roorda
The document discusses various natural language processing (NLP) tasks including named entity recognition, entity linking, question answering, sentiment analysis, dependency parsing, and semantic role labeling. It provides examples and explanations of how each task can be approached, common challenges, and relevant datasets and resources.
SECTION 1
Data Fundamentals Overview
Section Beginning (Dark Color Option )
1
Fundamental relationship
Relationship between two entity types
A person, place, or thing “acts” upon something to complete ”x” (i.e. purchase a product)
Binary Relationships
Data Fundamentals Overview
Salesperson
Product
Sells
2
Represents the maximum number of entities that can be involved in a relationship.
One-to-One Binary Relationship
One-to-Many Binary Relationship
Many-to-Many Binary Relationship
Cardinality
Data Fundamentals Overview
3
The minimum number of entity occurrences that can be involved in a relationship.
“inner” symbol on E-R diagram (“outer” symbol is cardinality)
Modality
Data Fundamentals Overview
Everyone order has exactly ONE customer AND Every customer has one or more ORDERS
4
Associate occurrences of an entity type with other occurrences of the same entity type.
Unary Relationships
Data Fundamentals Overview
5
A single occurrence of one entity type can be associated with a single occurrence of the other entity type and vice versa
1:1 Relationships
Data Fundamentals Overview
Student
Student ID #
6
Use “crow’s foot” to represent the multiple association.
“many” = the maximum number of occurrences that can be involved, means a number that can be 1, 2, 3, ... n.
1:M Relationships
Data Fundamentals Overview
Company
Product A
Product B
Product C
7
“Many” can be either an exact number or have a known maximum.
M:M Relationships
Data Fundamentals Overview
Musicians
Albums
Musicians
Musicians
8
Involves three different entity types
Ternary Relationships
Data Fundamentals Overview
9
Describes the relationship between two entities.
Used with many-to-many relationships.
Represented on E-R diagram as an “associative entity”
Intersection Data
Data Fundamentals Overview
10
Entities can have attributes; many-to-many relationships can have attributes.
Many-to-many relationship may be treated similarly to entities in an E-R diagram.
Associative Entity
Data Fundamentals
11
SECTION 2
Data Modeling Creation
Section Beginning (Dark Color Option )
12
A diagramming technique
Diagrams entities (with attributes) and the relationship between the entities.
There are many variations of E-R diagrams in use.
The E-R Diagram
Data Fundamentals Overview
13
Entity Relationship Diagram Basics
Entity type’s attributes are shown below the separator line.
PK and boldface denote the attribute(s) that constitute the entity type’s unique identifier
Rectangular shape
OBJECT = a type of entity
Name of entity is in caps above the separator line.
Data Fundamentals Overview
14
In an ER diagrams, common practice is to a convention that entity type and relationship type names are uppercase letters, attribute names have their initial letter capitalized, and role names are lowercase letter
Data Definition & Naming Conventions
Data Fundamentals Overview
15
SECTION 3
Assignment
Section Beginnin.
This document discusses community detection in networks. It begins by emphasizing the importance of defining what constitutes a community based on the goals and data of the specific network being analyzed. It then briefly describes four common community detection techniques: hierarchical clustering, k-means clustering, spectral clustering, and modularity maximization. Hierarchical and k-means clustering partition networks based on node similarity, while spectral clustering and modularity maximization detect communities as groups of densely connected nodes.
Harvesting and Normalization at the Digital Public Library of America: Lesson...Sandra McIntyre
Dixon, K., McIntyre, S., and Rudersdorf, A. (2014). Harvesting and normalization at the Digital Public Library of America: Lessons from a diverse aggregation. Panel presentation at ALCTS Metadata Interest Group, American Library Association Midwinter Meeting, Philadelphia, PA, January 25, 2014.
Co-presenters: Kristy Berry Dixon, Sandra McIntyre, and Amy Rudersdorf.
The document describes two people, Ann Smith and Dan, as nodes in a graph. Ann is described by her name, user ID, and a unique identifier. Dan likes Ann, which is represented by an edge between their nodes labeled "LIKES". The graph also includes the date of the like.
Gleaning Types for Literals in RDF with Application to Entity SummarizationKalpa Gunaratna
ESWC 2016 talk about how to compute types (ontology classes) for literals and add semantics to them, making them richer. Then utilize them in an entity summarization usecase.
The document discusses different approaches to generating biographies through natural language processing, including information extraction and language modeling. It describes using information extraction patterns learned from Wikipedia to extract fields like date of birth and place of birth, and bouncing between Wikipedia and Google search results to learn patterns for other fields with less structured data. It also proposes selecting and ranking sentences from search results to improve recall when information extraction may miss relevant sentences. The goal is to build biographies by combining these techniques for high precision on structured fields and better recall on more complex fields.
Researchers in ancient text corpora can take control over their data. We show a way to do so by means of Text-Fabric.
Co-production of Cody Kingham and Dirk Roorda
The document discusses various natural language processing (NLP) tasks including named entity recognition, entity linking, question answering, sentiment analysis, dependency parsing, and semantic role labeling. It provides examples and explanations of how each task can be approached, common challenges, and relevant datasets and resources.
SECTION 1
Data Fundamentals Overview
Section Beginning (Dark Color Option )
1
Fundamental relationship
Relationship between two entity types
A person, place, or thing “acts” upon something to complete ”x” (i.e. purchase a product)
Binary Relationships
Data Fundamentals Overview
Salesperson
Product
Sells
2
Represents the maximum number of entities that can be involved in a relationship.
One-to-One Binary Relationship
One-to-Many Binary Relationship
Many-to-Many Binary Relationship
Cardinality
Data Fundamentals Overview
3
The minimum number of entity occurrences that can be involved in a relationship.
“inner” symbol on E-R diagram (“outer” symbol is cardinality)
Modality
Data Fundamentals Overview
Everyone order has exactly ONE customer AND Every customer has one or more ORDERS
4
Associate occurrences of an entity type with other occurrences of the same entity type.
Unary Relationships
Data Fundamentals Overview
5
A single occurrence of one entity type can be associated with a single occurrence of the other entity type and vice versa
1:1 Relationships
Data Fundamentals Overview
Student
Student ID #
6
Use “crow’s foot” to represent the multiple association.
“many” = the maximum number of occurrences that can be involved, means a number that can be 1, 2, 3, ... n.
1:M Relationships
Data Fundamentals Overview
Company
Product A
Product B
Product C
7
“Many” can be either an exact number or have a known maximum.
M:M Relationships
Data Fundamentals Overview
Musicians
Albums
Musicians
Musicians
8
Involves three different entity types
Ternary Relationships
Data Fundamentals Overview
9
Describes the relationship between two entities.
Used with many-to-many relationships.
Represented on E-R diagram as an “associative entity”
Intersection Data
Data Fundamentals Overview
10
Entities can have attributes; many-to-many relationships can have attributes.
Many-to-many relationship may be treated similarly to entities in an E-R diagram.
Associative Entity
Data Fundamentals
11
SECTION 2
Data Modeling Creation
Section Beginning (Dark Color Option )
12
A diagramming technique
Diagrams entities (with attributes) and the relationship between the entities.
There are many variations of E-R diagrams in use.
The E-R Diagram
Data Fundamentals Overview
13
Entity Relationship Diagram Basics
Entity type’s attributes are shown below the separator line.
PK and boldface denote the attribute(s) that constitute the entity type’s unique identifier
Rectangular shape
OBJECT = a type of entity
Name of entity is in caps above the separator line.
Data Fundamentals Overview
14
In an ER diagrams, common practice is to a convention that entity type and relationship type names are uppercase letters, attribute names have their initial letter capitalized, and role names are lowercase letter
Data Definition & Naming Conventions
Data Fundamentals Overview
15
SECTION 3
Assignment
Section Beginnin.
This document discusses community detection in networks. It begins by emphasizing the importance of defining what constitutes a community based on the goals and data of the specific network being analyzed. It then briefly describes four common community detection techniques: hierarchical clustering, k-means clustering, spectral clustering, and modularity maximization. Hierarchical and k-means clustering partition networks based on node similarity, while spectral clustering and modularity maximization detect communities as groups of densely connected nodes.
"EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs" as presented in Sthe 17th International Semantic Web Conference ISWC, 9th of October 2018, held in Monterey, California, USA
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Slides from my lightning talk at the Boston Predictive Analytics Meetup hosted at Predictive Analytics World, Boston, October 1, 2012.
Full code and data are available on github: http://bit.ly/pawdata
Visible Partisanship Convolutional Neural Networks for the Analysis of Politi...Dhruvil Badani
1) Researchers analyzed over 300,000 Facebook images posted by US House and Senate members to identify the race of individuals pictured and compare it to the demographic makeup of their districts.
2) They found that white Democratic House members posted photos of African Americans, Hispanics, and Asians at higher rates than white Republicans, even after controlling for district demographics.
3) Democrats' Facebook photo posts more closely reflected the racial diversity of their districts compared to Republicans, suggesting Democrats strategically use images to signal empathy and shared identity with non-white constituents.
Visible Partisanship Convolutional Neural Networks for the Analysis of Politi...Dhruvil Badani
1) Researchers analyzed over 300,000 Facebook images posted by US House and Senate members to identify the race of individuals pictured and compare it to the racial demographics of their districts.
2) They found that white Democratic House members posted photos with African Americans, Hispanics, and Asians at higher rates than white Republicans.
3) Democrats' Facebook photos were more representative of the racial makeup of their districts compared to Republicans, suggesting Democrats strategically use images to signal racial identification and empathy to constituents.
The document describes techniques for truth discovery from multiple sources on the web that may provide conflicting information. It proposes modeling source dependence and accuracy to determine the true values. An algorithm is presented that iterates between estimating source dependence, accuracy, and the true values to converge on a solution. The algorithm is evaluated on a dataset of book metadata from multiple bookstores, and is shown to improve precision over naive voting by considering the different factors.
Social Network Analysis, Semantic Web and Learning NetworksRory Sie
Session 2 of the Learning Networks Social Networks Seminar. It presents a recap of SNA terms, and introduces the Semantic Web and how it could be applied to Learning Networks.
The document summarizes an entity extraction and typing framework proposed by the author. The framework constructs a heterogeneous graph connecting entity mentions, surface names, and relation phrases extracted from documents. It then performs joint type propagation and relation phrase clustering on the graph to infer types for entity mentions. Evaluation on news, tweets and reviews shows the framework outperforms existing methods in recognizing new types and domains without extensive feature engineering or human supervision. It obtains improvements by modeling each mention individually and addressing data sparsity through relation phrase clustering.
2021-04, EACL, T-NER: An All-Round Python Library for Transformer-based Named...asahiushio1
Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross-lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine-tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.
The document describes Lydia, a system for named entity recognition and text analysis that was adapted for question answering at TREC 2005. It summarizes Lydia's pipeline for entity recognition and relationship analysis. It then describes the question answering system, which takes questions as input, extracts targets, collects candidate answers from Lydia's database, scores and ranks candidates, and produces a single answer or list of answers. The system handles factoid, list, and other questions by analyzing the question type and scoring candidates based on features like target juxtaposition and question term matching.
This document discusses techniques for automatically generating topic facets from documents to enable semantic information discovery and retrieval. Topic facets are collections of shared phrases between documents that indicate a semantic agreement or relationship between them on a particular topic. The document provides an example of how topic facets could be generated from six sample documents that share terms, and how the documents could be grouped into sets according to their shared terms and topic facets. It also discusses how topic facets relax the requirement to assign single topics or concepts to documents by allowing fractional or multi-topic agreements.
This document describes a method for ranking entity types using contextual information from text. It presents several approaches for ranking types, including entity-centric, hierarchy-based, and context-aware methods. It also describes how the different ranking approaches are evaluated using crowdsourcing to collect relevance judgments on entity types within given contexts. The approaches are implemented in a system called TRank that uses inverted indices and MapReduce for scalability.
Kalpa Gunaratna's Ph.D. dissertation defense: April 19 2017
The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored.
In this dissertation, we focus on creating both comprehensive and concise entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance, which is computed by combining popularity and uniqueness, and diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts from these facets for the summary. The important and unique contribution of this approach is that because of its generation of facets, it adds diversity into entity summaries, making them comprehensive. For creating multiple entity summaries, we simultaneously process facts belonging to the given entities using combinatorial optimization techniques. In this process, we maximize diversity and importance of facts within each entity summary and relatedness of facts between the entity summaries. The proposed approach uniquely combines semantic expansion, graph-based relatedness, and combinatorial optimization techniques to generate relatedness-based multi-entity summaries.
Complementing the entity summarization approaches, we introduce a novel approach using light Natural Language Processing (NLP) techniques to enrich knowledge graphs by adding type semantics to literals.
Anthem Ayn Rand Essay. Book quot;Anthemquot; by Ayn Rand Review Free Essay S...Heidi Marshall
Anthem Essay Option 2 PDF Ayn Rand Egalitarianism. Symbolism In the book Anthem by Ayn Rand Essay Example Topics and .... Unit on Anthem by Ayn Rand Anthem ayn rand, Ayn rand, Anthem. Anthem by Ayn Rand: Discrimination Theme - 1729 Words Essay Example. Anthem Quotes by Ayn Rand Anthem Ayn Rand, Essay Contests, High School .... Anthem - Ayn Rand - PHDessay.com. Anthem by Ayn Rand. Anthem by Ayn Rand English Hardcover Book Free Shipping .... Anthem ayn rand essay help - The Future Dark Ages in Ayn Rands Novel .... Anthem Essay Contest Ayn Rand Education. Ayn Rand / Anthem First Edition 1961 eBay. Anthem By Ayn Rand Quotes. QuotesGram. Analysis of quot;Anthemquot; by Ayn Rand Free Essay Sample on Samploon.com. Anthem - Ayn Rand. - GCSE Sociology - Marked by Teachers.com. Anthem by Ayn Rand: an essay assignment by Anna K TpT. Anthem/Ayn Rand. Anthem By Ayn Rand Essay Help: Untitled document. AnthemPaperback Ayn rand, Anthem ayn rand, Essay contests. Ayn Rands Anthem: An Appreciation, The Atlas Society Ayn Rand .... Anthem by Ayn Rand Research paper, Public relations, Ayn rand. Anthem Essay Contest - AynRand.org. Book quot;Anthemquot; by Ayn Rand Review Free Essay Sample on Samploon.com. Anthem Ayn Rand Essay Help - Anthem Ayn Rand Essay Help. Essays on Ayn Rands Anthem by Robert Mayhew. Anthem by Ayn Rand PDF Download - Today Novels. Anthem by ayn rand setting. Anthem The Case Against Objectivism. 2019 .... Anthem- Ayn Rand Quest for the great American Novel. ANTHEM Book Analysis - In the novel Anthem, Ayn Rand expounds on the future. Ayn Rands View of Technology as seen in the novel Anthem Sample .... Ayn Rands Anthem: The Graphic Novel by Charles Santino, Paperback .... Anthem Ayn Rand Centennial Edition Signet Paperback . Anthem ayn rand .... Anthem: a Novel by Ayn Rand Research Paper Example Topics and Well ... Anthem Ayn Rand Essay Anthem Ayn Rand Essay. Book quot;Anthemquot; by Ayn Rand Review Free Essay Sample on Samploon.com
This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Mastering the Concepts Tested in the Databricks Certified Data Engineer Assoc...SkillCertProExams
• For a full set of 760+ questions. Go to
https://skillcertpro.com/product/databricks-certified-data-engineer-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
• SkillCertPro updates exam questions every 2 weeks.
• You will get life time access and life time free updates
• SkillCertPro assures 100% pass guarantee in first attempt.
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
"EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs" as presented in Sthe 17th International Semantic Web Conference ISWC, 9th of October 2018, held in Monterey, California, USA
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Slides from my lightning talk at the Boston Predictive Analytics Meetup hosted at Predictive Analytics World, Boston, October 1, 2012.
Full code and data are available on github: http://bit.ly/pawdata
Visible Partisanship Convolutional Neural Networks for the Analysis of Politi...Dhruvil Badani
1) Researchers analyzed over 300,000 Facebook images posted by US House and Senate members to identify the race of individuals pictured and compare it to the demographic makeup of their districts.
2) They found that white Democratic House members posted photos of African Americans, Hispanics, and Asians at higher rates than white Republicans, even after controlling for district demographics.
3) Democrats' Facebook photo posts more closely reflected the racial diversity of their districts compared to Republicans, suggesting Democrats strategically use images to signal empathy and shared identity with non-white constituents.
Visible Partisanship Convolutional Neural Networks for the Analysis of Politi...Dhruvil Badani
1) Researchers analyzed over 300,000 Facebook images posted by US House and Senate members to identify the race of individuals pictured and compare it to the racial demographics of their districts.
2) They found that white Democratic House members posted photos with African Americans, Hispanics, and Asians at higher rates than white Republicans.
3) Democrats' Facebook photos were more representative of the racial makeup of their districts compared to Republicans, suggesting Democrats strategically use images to signal racial identification and empathy to constituents.
The document describes techniques for truth discovery from multiple sources on the web that may provide conflicting information. It proposes modeling source dependence and accuracy to determine the true values. An algorithm is presented that iterates between estimating source dependence, accuracy, and the true values to converge on a solution. The algorithm is evaluated on a dataset of book metadata from multiple bookstores, and is shown to improve precision over naive voting by considering the different factors.
Social Network Analysis, Semantic Web and Learning NetworksRory Sie
Session 2 of the Learning Networks Social Networks Seminar. It presents a recap of SNA terms, and introduces the Semantic Web and how it could be applied to Learning Networks.
The document summarizes an entity extraction and typing framework proposed by the author. The framework constructs a heterogeneous graph connecting entity mentions, surface names, and relation phrases extracted from documents. It then performs joint type propagation and relation phrase clustering on the graph to infer types for entity mentions. Evaluation on news, tweets and reviews shows the framework outperforms existing methods in recognizing new types and domains without extensive feature engineering or human supervision. It obtains improvements by modeling each mention individually and addressing data sparsity through relation phrase clustering.
2021-04, EACL, T-NER: An All-Round Python Library for Transformer-based Named...asahiushio1
Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross-lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine-tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.
The document describes Lydia, a system for named entity recognition and text analysis that was adapted for question answering at TREC 2005. It summarizes Lydia's pipeline for entity recognition and relationship analysis. It then describes the question answering system, which takes questions as input, extracts targets, collects candidate answers from Lydia's database, scores and ranks candidates, and produces a single answer or list of answers. The system handles factoid, list, and other questions by analyzing the question type and scoring candidates based on features like target juxtaposition and question term matching.
This document discusses techniques for automatically generating topic facets from documents to enable semantic information discovery and retrieval. Topic facets are collections of shared phrases between documents that indicate a semantic agreement or relationship between them on a particular topic. The document provides an example of how topic facets could be generated from six sample documents that share terms, and how the documents could be grouped into sets according to their shared terms and topic facets. It also discusses how topic facets relax the requirement to assign single topics or concepts to documents by allowing fractional or multi-topic agreements.
This document describes a method for ranking entity types using contextual information from text. It presents several approaches for ranking types, including entity-centric, hierarchy-based, and context-aware methods. It also describes how the different ranking approaches are evaluated using crowdsourcing to collect relevance judgments on entity types within given contexts. The approaches are implemented in a system called TRank that uses inverted indices and MapReduce for scalability.
Kalpa Gunaratna's Ph.D. dissertation defense: April 19 2017
The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored.
In this dissertation, we focus on creating both comprehensive and concise entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance, which is computed by combining popularity and uniqueness, and diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts from these facets for the summary. The important and unique contribution of this approach is that because of its generation of facets, it adds diversity into entity summaries, making them comprehensive. For creating multiple entity summaries, we simultaneously process facts belonging to the given entities using combinatorial optimization techniques. In this process, we maximize diversity and importance of facts within each entity summary and relatedness of facts between the entity summaries. The proposed approach uniquely combines semantic expansion, graph-based relatedness, and combinatorial optimization techniques to generate relatedness-based multi-entity summaries.
Complementing the entity summarization approaches, we introduce a novel approach using light Natural Language Processing (NLP) techniques to enrich knowledge graphs by adding type semantics to literals.
Anthem Ayn Rand Essay. Book quot;Anthemquot; by Ayn Rand Review Free Essay S...Heidi Marshall
Anthem Essay Option 2 PDF Ayn Rand Egalitarianism. Symbolism In the book Anthem by Ayn Rand Essay Example Topics and .... Unit on Anthem by Ayn Rand Anthem ayn rand, Ayn rand, Anthem. Anthem by Ayn Rand: Discrimination Theme - 1729 Words Essay Example. Anthem Quotes by Ayn Rand Anthem Ayn Rand, Essay Contests, High School .... Anthem - Ayn Rand - PHDessay.com. Anthem by Ayn Rand. Anthem by Ayn Rand English Hardcover Book Free Shipping .... Anthem ayn rand essay help - The Future Dark Ages in Ayn Rands Novel .... Anthem Essay Contest Ayn Rand Education. Ayn Rand / Anthem First Edition 1961 eBay. Anthem By Ayn Rand Quotes. QuotesGram. Analysis of quot;Anthemquot; by Ayn Rand Free Essay Sample on Samploon.com. Anthem - Ayn Rand. - GCSE Sociology - Marked by Teachers.com. Anthem by Ayn Rand: an essay assignment by Anna K TpT. Anthem/Ayn Rand. Anthem By Ayn Rand Essay Help: Untitled document. AnthemPaperback Ayn rand, Anthem ayn rand, Essay contests. Ayn Rands Anthem: An Appreciation, The Atlas Society Ayn Rand .... Anthem by Ayn Rand Research paper, Public relations, Ayn rand. Anthem Essay Contest - AynRand.org. Book quot;Anthemquot; by Ayn Rand Review Free Essay Sample on Samploon.com. Anthem Ayn Rand Essay Help - Anthem Ayn Rand Essay Help. Essays on Ayn Rands Anthem by Robert Mayhew. Anthem by Ayn Rand PDF Download - Today Novels. Anthem by ayn rand setting. Anthem The Case Against Objectivism. 2019 .... Anthem- Ayn Rand Quest for the great American Novel. ANTHEM Book Analysis - In the novel Anthem, Ayn Rand expounds on the future. Ayn Rands View of Technology as seen in the novel Anthem Sample .... Ayn Rands Anthem: The Graphic Novel by Charles Santino, Paperback .... Anthem Ayn Rand Centennial Edition Signet Paperback . Anthem ayn rand .... Anthem: a Novel by Ayn Rand Research Paper Example Topics and Well ... Anthem Ayn Rand Essay Anthem Ayn Rand Essay. Book quot;Anthemquot; by Ayn Rand Review Free Essay Sample on Samploon.com
Similar to Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution (13)
This presentation by OECD, OECD Secretariat, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Mastering the Concepts Tested in the Databricks Certified Data Engineer Assoc...SkillCertProExams
• For a full set of 760+ questions. Go to
https://skillcertpro.com/product/databricks-certified-data-engineer-associate-exam-questions/
• SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
• It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
• SkillCertPro updates exam questions every 2 weeks.
• You will get life time access and life time free updates
• SkillCertPro assures 100% pass guarantee in first attempt.
XP 2024 presentation: A New Look to Leadershipsamililja
Presentation slides from XP2024 conference, Bolzano IT. The slides describe a new view to leadership and combines it with anthro-complexity (aka cynefin).
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Nathaniel Lane, Associate Professor in Economics at Oxford University, was made during the discussion “Pro-competitive Industrial Policy” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/pcip.
This presentation was uploaded with the author’s consent.
This presentation by Thibault Schrepel, Associate Professor of Law at Vrije Universiteit Amsterdam University, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Juraj Čorba, Chair of OECD Working Party on Artificial Intelligence Governance (AIGO), was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
We can combine our innate empathy with our analytical skills to gain a deeper understanding of complex situations at work. Join this session to learn how to prepare for difficult conversations and how to improve our agile conversations in order to be more influential without power. We will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
In the session you will experience how preparing and reflecting on your conversation can help you be more influential at work. You will learn how to communicate more effectively with the people needed to achieve positive change. You will leave with a self-revised version of a difficult conversation and a practical model to use when you get back to work.
Come learn more on how to become a real influencer!
Collapsing Narratives: Exploring Non-Linearity • a micro report by Rosie WellsRosie Wells
Insight: In a landscape where traditional narrative structures are giving way to fragmented and non-linear forms of storytelling, there lies immense potential for creativity and exploration.
'Collapsing Narratives: Exploring Non-Linearity' is a micro report from Rosie Wells.
Rosie Wells is an Arts & Cultural Strategist uniquely positioned at the intersection of grassroots and mainstream storytelling.
Their work is focused on developing meaningful and lasting connections that can drive social change.
Please download this presentation to enjoy the hyperlinks!
Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution
1. Jiacheng Huang, Wei Hu*, Haoxuan Li, Yuzhong Qu
Nanjing University, China
* Corresponding author: whu@nju.edu.cn
Automated Comparative Table Generation for
Facilitating Human Intervention in Multi-Entity Resolution
SIGIR’18, July 8–12, Ann Arbor, MI, USA
2. Outline
Introduction
Knowledge graph
(Crowd) Entity resolution
Related work
Our approach
Experiments and results
Conclusion
2Introduction ➤ Our approach ➤ Experiments and results ➤ Conclusion
3. Knowledge graph (KG)
Knowledge graph (KG) is a knowledge base used by Google
to enhance its search engine’s results
Other famous knowledge bases
DBpedia, Freebase, Wikidata, YAGO …
Linked Open Data (LOD) cloud
KGs have reached a scale in billions of entities!
Problem: Many different entities refer to
the same real-world thing
3
4. Entity Resolution
Entity resolution (ER): find different entities referring to the same
a.k.a. entity linkage, entity matching …
also widely studied in DB and NLP
resolve heterogeneity and achieve interoperability
Crowd ER
use humans, in addition to machines, to obtain
the truths of ER tasks
Key issues
How to present a single ER task?
How to select “right” humans?
How to pick tasks under a budget?
……
4
Little effort has been made on how to
present the critical information (such as
important properties and values) to help
complete a task efficiently and accurately
[Verroios et al., SIGMOD’17]
5. Related work
Multi-entity
resolution (MER)
1. Display multiple entities in a form of list
just like what is typically seen from a Web search engine
2. Use pairwise presentation
compare two entities at a time and align similar properties between them
Pros & cons for MER
1. List: remember and compare in mind
2. Pairwise: focus, but difficult to scale
Both lost transitivity & grouping info
5
entities with similar properties & values
⦿ match
⦿ nonmatch
e1 [dbp:Lil_Eazy-E]
– rdf:type : Person, MusicalArtist
– rdfs:label : Lil Eazy-E
– owl:sameAs : fb:m.01wf_p_
– birthDate : 1984-4-23
– birthPlace : Compton
– gender : male
– genre : Gangsta rap, Hip hop
– givenName : Eric Darnell Wright
(146 property-values in total)
e2 [fb:m.01wf_p_]
– alias : Eric Wright, Eazy-E
– date_of_birth : 1963-9-7
– gender : male
– genre : gangsta rap, hip hop
– name : Eazy-E
– place_of_birth : Compton
– profession : rapper, producer
– type : person, music.artist
(1,253 property-values in total)
e3 [wd:Q36804]
– rdfs:label : Eazy-E
– altLabel : Eric Lynn Wright
– date_of_birth : 1963-9-7
– desc : Gangsta rapper, producer
– genre : gangsta rap
– instance_of : human
– occupation : musician, rapper
– place_of_birth : Compton
(141 property-values in total)
group?
givenName
alias
altLabel
rdf:type
type
instance_of
birthDate
date_of_birth
date_of_birth
1 e1 Eric Darnell Wright Person, MusicalArtist 1984-4-23
2 e2 Eric Wright, Eazy-E person, music.artist 1963-9-7
givenName
alias
altLabel
birthDate
date_of_birth
date_of_birth
e1 Eric Darnell Wright 1984-4-23
e2 Eric Wright, Eazy-E 1963-9-7
e3 Eric Lynn Wright 1963-9-7
e1 [dbp:Lil_Eazy-E]
– rdf:type : Person …
– rdfs:label : Lil Eazy-E
– owl:sameAs : fb:m…
– birthDate : 1984-4-23
– birthPlace : Compton
– gender : male
– genre : Gangsta rap …
…
e2 [fb:m.01wf_p_]
– alias : Eric Wright …
– date_of_birth : 1963-9-7
– genre : gangsta rap …
…
e3 [wd:Q36804]
– rdfs:label : Eazy-E
– altLabel : Eric Lynn …
– date_of_birth : 1963-9-7
…
e1 [dbp:Lil_Eazy-E]
– rdf:type : Person, MusicalArtist
– genre : Gangsta rap, Hip hop
– givenName : Eric Darnell Wright
– rdfs:label : Lil Eazy-E
– birthPlace : Compton
– gender : male
e2 [fb:m.01wf_p_]
– type : person, music.artist
– genre : gangsta rap, hip hop
– alias : Eric Wright, Eazy-E
– place_of_birth : Compton
– gender : male
e1 [dbp:Lil_Eazy-E]
– rdf:type : Person, MusicalArtist
– rdfs:label : Lil Eazy-E
– owl:sameAs : fb:m.01wf_p_
– birthDate : 1984-4-23
– birthPlace : Compton
– gender : male
– genre : Gangsta rap, Hip hop
– givenName : Eric Darnell Wright
(146 property-values in total)
e2 [fb:m.01wf_p_]
– alias : Eric Wright, Eazy-E
– date_of_birth : 1963-9-7
– gender : male
– genre : gangsta rap, hip hop
– name : Eazy-E
– place_of_birth : Compton
– profession : rapper, producer
– type : person, music.artist
(391 property-values in total)
e3 [wd:Q36804]
– rdfs:label : Eazy-E
– altLabel : Eric Lynn Wright
– date_of_birth : 1963-9-7
– desc : Gangsta rapper, producer
– genre : gangsta rap
– instance_of : human
– occupation : musician, rapper
– place_of_birth : Compton
(141 property-values in total)
group?
givenName
alias
altLabel
rdf:type
type
instance_of
birthDate
date_of_birth
date_of_birth
1 � e1 Eric Darnell Wright Person, MusicalArtist 1984-4-23
2 � e2 Eric Wright, Eazy-E person, music.artist 1963-9-7
2 � e3 Eric Lynn Wright human 1963-9-7
givenName
alias
altLabel
birthDate
date_of_birth
date_of_birth
e1 Eric Darnell Wright 1984-4-23
e2 Eric Wright, Eazy-E 1963-9-7
e3 Eric Lynn Wright 1963-9-7
e1 [dbp:Lil_Eazy-E
– rdf:type : Person, M
– genre : Gangsta ra
– givenName : Eric D
– rdfs:label : Lil Eaz
– birthPlace : Comp
– gender : male
7. Our approach: comparative table
Comparative table
arrange entities and properties as
row and column headers, resp.
assign values in cells
Workflow
1. Holistic property matching: similarity calculation property clique derivation
2. Goodness measurement: discriminability, abundance, semantics & diversity
3. Comparative table generation: property clique selection value selection
7
group?
givenName
alias
altLabel
rdf:type
type
instance_of
birthDate
date_of_birth
date_of_birth
1 e1 Eric Darnell Wright Person, MusicalArtist 1984-4-23
2 e2 Eric Wright, Eazy-E person, music.artist 1963-9-7
3 e3 Eric Lynn Wright human 1963-9-7
givenName
alias
altLabel
birthDate
date_of_birth
date_of_birth
e1 Eric Darnell Wright 1984-4-23
e2 Eric Wright, Eazy-E 1963-9-7
e3 Eric Lynn Wright 1963-9-7
Similarity computation
Clique generation
Holistic Property Matching
{rdfs:label, name, rdfs:label}
{givenName, alias, altLabel}
{rdf:type, type, occupation}
…
Abundance
Discriminability
Comparability measurement
Semantics
Refinement by diversity
0.9 {givenName, alias, altLabel}
0.8 {birthDate, dateofbirth, DOB}
0.6 {rdf:type, type, occupation}
Coverage-constrained
Budget-constrained
Comparative table generation
Input: candidate entities Property cliques
Property clique comparabilitiesOutput: comparative table
Human
Intervention
Similarity calculation
Prop. clique derivation
Holistic property matching
Abundance
Discriminability
Goodness measurement
Diversity
Semantics
Property cliques
Prop. clique selection
Value selection
Comparative table generationGoodness scores Comparative tableMultiple entities
e1 [dbp:Lil_Eazy-E]
– rdf:type : Person …
– rdfs:label : Lil Eazy-E
– owl:sameAs : fb:m…
– birthPlace : Compton
– desc : CEO NWA…
– gender : male
– genre : Gangsta rap …
e2 [fb:m.01wf_p_]
– alias : Eric Wright …
– date_of_birth : 1963-9-7
– genre : gangsta rap …
e3 [wd:Q36804]
– rdfs:label : Eazy-E
– altLabel : Eric Lynn …
– date_of_birth : 1963-9-7
{rdfs:label, name}
{givenName, alias, altLabel}
{rdf:type, type, instance_of}
…
0.2 {givenName, alias, altLabel}
0.4 {rdf:type, type, instance_of}
0.5 {birthDate, date_of_birth}
…
divide into groups
Challenge: heterogeneity, large-scale
vs. limited presentation space
8. 1. Holistic property matching
Heterogeneous properties
Label, local name & value similarity, combined with logistic regression
Property cliques for multiple entities
restrict each property can match at most one other property
choose the pairs with highest match probability estimate may lead to conflicts
Holistic property matching
maximize the overall match probability
estimate among all matched property pairs
s.t. 1:1 matching constraint is satisfied
NP-hard (3-dimensional assignment)
Greedy algorithm
8
9. 2. Goodness measurement
Goodness of property cliques
1. Discriminability: a property clique that holds completely different or exactly identical
values for all the entities may not good
2. Abundance: a property clique whose values
are largely missing may be less convincing
3. Semantics gives extra scores to the ones
particularly useful, e.g., owl:sameAs
4. Diversity evaluates the redundancy between
different property cliques (MMR)
2-phase combination: (discriminability + abundance + semantics) + diversity
Goodness of values
Longer length, less redundancy
9
0 1.0
proportion of distinct values
0
0.7
discriminability
proportion of
entities
goodness
proportion of
distinct values
0
0.11.0
1
1.00.1
1
= 0.5
2
= 0.3
3
= 0.2
0 1.0
proportion of distinct values
0
0.7
discriminability
proportion of
entities
goodness
proportion of
distinct values
0
0.11.0
1
1.00.1
1
= 0.5
2
= 0.3
3
= 0.2
10. 3. Comparative table generation
Property clique selection
Greedy method
Given the maximal number of property cliques in a comparative table, simply
select top property cliques with best goodness
cannot guarantee each entity to be at least described by several properties
Optimal property clique selection
with entity coverage constraint
NP-hard (set cover)
𝐻(𝑁)-approximation
Value selection
model it based on the classic 0/1 knapsack
problem with a table cell size constraint
10
11. Outline
Introduction
Our approach
Experiments and results
Test on holistic property matching
Test on property clique ranking
Test on human intervention
Conclusion
11Introduction ➤ Our approach ➤ Experiments and results ➤ Conclusion
12. Test on holistic property matching
Quality of matched property pairs
“Official” property matches
Label others by 3 graduate students
484 matches, 1397 non-matches
Quality of derived property cliques
Compute connected components
135 reference property cliques
12
MER tasks
10 popular domains, 25 DBpedia entities per domain as seeds
Wikipedia disambiguation page, 2~4 Freebase, Wikidata, YAGO entities
randomly select 10 entities to constitute an MER task
250 tasks, 804 distinct real objects
0.868
0.727
0.791
0.824
0.73
0.773
0.893
0.669
0.763
0.877
0.706
0.782
0
0.5
1
Precision Recall F1-score
CTab (LR) LinReg DecTree SVM
0.868
0.727
0.791
0.983
0.233
0.377
0.97
0.066
0.124
Precision Recall F1-score
CTab (LR) Falcon LogMap
0.789
0.869
0.787
0.558
0.64
0.548
0.65
0.741
0.648
0.43
0.55
0.422
0.2
0.4
0.6
0.8
1
NMI Purity V-measure
CTab K-medoids DBSCAN APCluster
13. Test on property clique ranking
1. Directly rank ref. property cliques
Assess property clique derivation &
ranking together
The Hausdorf version of Kendall
tau distance
treat property clique rankings as
partial rankings of properties (the
properties with the same grade
and in the same clique are tied)
Ablation study
13
3 experienced humans score property cliques in each task
Highly-useful (3), fairly-useful (2), marginally-useful (1) and useless (0)
Comparative systems
FACES (list) [Gunaratna et al., AAAI’15]
C3D+P (pairwise) [Cheng et al., JWS’15]
CTab, CTab (entropy), CTab (greedy)
Use reference property cliques
KHaus
P@1 P@5 P@10 nDCG@5
FACES 0.176 0.310 0.290 0.239 0.753
C3D+P 0.040 0.347 0.511 0.154 0.647
CTab (entropy) 0.180 0.178 0.184 0.092 0.811
CTab (greedy) 0.632 0.660 0.615 0.684 0.647
CTab 0.756 0.754 0.643 0.798 0.615
KHaus Discr. Abund. Sem. w/o Div. Good
CTab (greedy) 0.678 0.686 0.673 0.655 0.647
CTab 0.675 0.633 0.815 0.618 0.615
14. Test on human intervention
60 graduate students (top-5/top-10), 30 orthogonal tasks per human, 100RMB
Task difficulty is not significantly different in statistics among FACES, C3D+P, CTab
1. Completion time
2. Precision
Break the entities in each
entity group down to pairs
3. Human scoring and comments
For CTab, the least cover times was not always satisfiable
14
FACES (L) C3D+P (P) CTab (T) p-value Post-hoc
Top-5
Time (s) 153 208 96 0.01% P < L < T
Prec. 0.63 0.69 0.77 0.07% L, P < T
Top-10
Time (s) 175 180 131 1.13% L, P < T
Prec. 0.79 0.77 0.80 69.8%
Questions [from 1: “totally disagree” to 5: “totally agree”] FACES (L) C3D+P (P) CTab (T) p-value Post-hoc
Q1. The system provided adequate information of entities. 3.11 3.17 3.70 0.76% L, P < T
Q2. The system provided unsuperfluous information of entities. 2.67 3.30 3.23 4.46% L < T, P
Q3. The system helped me easily compare entities of interest. 2.43 3.37 4.00 < 0.01% L < P < T
Q4. I found the system easy to use. 3.00 3.13 3.70 2.28% L, P < T
16. Conclusion
Main contributions
1. Discovery of matched property cliques
2. Scoring functions to measure the goodness of property cliques and values
3. Optimal comparative table generation with the entity coverage constraint
An 𝐻(𝑁) algorithm to obtain approximate solutions
4. Comparison to state-of-the-art methods and user study
Accuracy of matched properties, effectiveness of goodness measures and user
satisfaction of comparative tables for MER
Future work
Combine comparative tables with other presentation enhancements
Extend to other areas such as knowledge base summarization
16
17. Datasets & source code: http://ws.nju.edu.cn/ctab/
Acknowledgements
National Natural Science Foundation of China (No. 61772264)
Collaborative Innovation Center of Novel Software Technology and Industrialization
Thank you for your time!
SIGIR’18, July 8–12, Ann Arbor, MI, USA