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Presented by Professor Srinath Srinivasa
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Bx044461467
IJERA Editor
This slides are for a presentation at the 2009 IEEE/WIC/ACM International Conference on Web Intelligence. The major emphasis to this paper is concentrating on how to provide more personalized search support for a specific user considering his/her historical interests or recent interests. Cognitive memory retention like models are proposed and implemented in this system. Other supporting functionalities, such as domain distribution support, etc. are briefly mentioned. The whole paper can be downloaded from http://www.iwici.org/~yizeng/papers/WI2009-camera-ready.pdf
DBLP-SSE: A DBLP Search Support Engine
DBLP-SSE: A DBLP Search Support Engine
Yi Zeng
Keynote given at the workshop for Artificial Intelligence meets the Web of Data on Pragmatic Semantics. In this keynote I argue that the Web of Data is a Complex System or Marketplace of Ideas rather than a classical Database, and that the model theory on which classical semantics are based is not appropriate in all situations, and propose an alternative "Pragmatic Semantics" based on optimisation of possible interpretations. .
Keynote at AImWD
Keynote at AImWD
Stefan Schlobach
Keynote at Endorse in March 2023
The web of data: how are we doing so far
The web of data: how are we doing so far
Elena Simperl
International Journal of Scientific and Technical Advancements (IJSTA)
An Efficient Approach for Clustering High Dimensional Data
An Efficient Approach for Clustering High Dimensional Data
IJSTA
This slideshow presents a basic use of AutoMap and ORA NetScenes for text analysis.
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
Shalin Hai-Jew
A digital library is a type of information retrieval (IR) system. The existing information retrieval methodologies generally have problems on keyword-searching. We proposed a model to solve the problem by using concept-based approach (ontology) and metadata case base. This model consists of identifying domain concepts in user’s query and applying expansion to them. The system aims at contributing to an improved relevance of results retrieved from digital libraries by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to import the concept of ontology, making use of its advantage of abundant semantics and standard concept. Domain specific ontology can be used to improve information retrieval from traditional level based on keyword to the lay based on knowledge (or concept) and change the process of retrieval from traditional keyword matching to semantics matching. One approach is query expansion techniques using domain ontology and the other would be introducing a case based similarity measure for metadata information retrieval using Case Based Reasoning (CBR) approach. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
cscpconf
We describe current work in federating data from institutional research profiling systems – providing single-point access to substantial numbers of investigators through concept-driven search, visualization of the relationships among those investigators and the ability to interlink systems into a single information ecosystem.
Federating Research Profiling Data
Federating Research Profiling Data
ericmeeks
1) Introduction to the key Big Data concepts 1.1 The Origins of Big Data 1.2 What is Big Data ? 1.3 Why is Big Data So Important ? 1.4 How Is Big Data Used In Practice ? 2) Introduction to the key principles of Big Data Systems 2.1 How to design Data Pipeline in 6 steps 2.2 Using Lambda Architecture for big data processing 3) Practical case study : Chat bot with Video Recommendation Engine 4) FAQ for student
Concepts, use cases and principles to build big data systems (1)
Concepts, use cases and principles to build big data systems (1)
Trieu Nguyen
Jukka Huhtamäki, Tampere University of Technology
Visualizing Co-authorship Networks for Actionable Insights: Action Design Res...
Visualizing Co-authorship Networks for Actionable Insights: Action Design Res...
Mindtrek
JAMIA INSTITUTE OF ENGINEERING AND MANAGEMENT STUDIES, AKKALKUWA
Review Paper on Shared and Distributed Memory Parallel Algorithms to Solve Bi...
Review Paper on Shared and Distributed Memory Parallel Algorithms to Solve Bi...
JIEMS Akkalkuwa
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for Supporting discovery and reuse of OER. An approach based on Social Networks Analysis and Linked Open Data
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for...
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for...
eMadrid network
Talk given by prof. T.K. Prasad at the workshop on Semantics in Geospatial Architectures: Applications and Implementation. The workshop was held from October 28-29, 2013 at Pyle Center (702 Langdon Street, Madison, WI), University of Wisconsin-Madison.
Semantics-enhanced Geoscience Interoperability, Analytics, and Applications
Semantics-enhanced Geoscience Interoperability, Analytics, and Applications
Artificial Intelligence Institute at UofSC
Data Quality and Data Usage in a large-scale Multilingual Knowledge Graph
Sebastian Hellmann
Sebastian Hellmann
Connected Data World
119 128
119 128
Editor IJARCET
119 128
119 128
Editor IJARCET
This thesis is motivated by a case study of SSME, in which we digitalize and integrate cultural assets of TCM and provide Web-based knowledge services to medical experts. The major quest is to turn cultural assets from prolonged Chinese history into knowledge services contributing to modern biomedicine. In our view, the essence of knowledge service is cross-domain collaboration in knowledge discovery on the Web of data. Whereas the Service-Oriented Architecture enables interactions between Web agents, and the Semantic Web provides a knowledge representation and integration framework, the feasibility and benefits of Web-based collaborative knowledge discovery need to be further investigated. We propose a methodology named Semantic Graph Mining (SGM), which uses the semantic graph model to integrate graph mining and ontology reasoning for better analyzing biomedical complex networks (an important KDD problem). Potential methods of SGM include Web resource ranking, semantic association discovery, frequent subgraph mining, and clustering. The effectiveness of these methods is investigated in use cases such as TCM semantic search, TCM formulae analysis, drug-interaction analysis.
Semantic Web Development for Traditional Chinese Medicine
Semantic Web Development for Traditional Chinese Medicine
Tong Yu
Presentation by Slava Tykhonov (DANS-KNAW R&D) for the Spanish National Research Council, 20 April 2021.
Fighting COVID-19 with Artificial Intelligence
Fighting COVID-19 with Artificial Intelligence
vty
Semelhante a Utilitarian aggregation of open data
(20)
The Materials Data Facility: A Distributed Model for the Materials Data Commu...
The Materials Data Facility: A Distributed Model for the Materials Data Commu...
Building an Open Source, Real-Time, Billion Object Spatio-Temporal Search Pla...
Building an Open Source, Real-Time, Billion Object Spatio-Temporal Search Pla...
Bx044461467
Bx044461467
DBLP-SSE: A DBLP Search Support Engine
DBLP-SSE: A DBLP Search Support Engine
Keynote at AImWD
Keynote at AImWD
The web of data: how are we doing so far
The web of data: how are we doing so far
An Efficient Approach for Clustering High Dimensional Data
An Efficient Approach for Clustering High Dimensional Data
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
Federating Research Profiling Data
Federating Research Profiling Data
Concepts, use cases and principles to build big data systems (1)
Concepts, use cases and principles to build big data systems (1)
Visualizing Co-authorship Networks for Actionable Insights: Action Design Res...
Visualizing Co-authorship Networks for Actionable Insights: Action Design Res...
Review Paper on Shared and Distributed Memory Parallel Algorithms to Solve Bi...
Review Paper on Shared and Distributed Memory Parallel Algorithms to Solve Bi...
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for...
2015 03 19 (EDUCON2015) eMadrid UPM Towards a Learning Analytics Approach for...
Semantics-enhanced Geoscience Interoperability, Analytics, and Applications
Semantics-enhanced Geoscience Interoperability, Analytics, and Applications
Sebastian Hellmann
Sebastian Hellmann
119 128
119 128
119 128
119 128
Semantic Web Development for Traditional Chinese Medicine
Semantic Web Development for Traditional Chinese Medicine
Fighting COVID-19 with Artificial Intelligence
Fighting COVID-19 with Artificial Intelligence
Mais de UK Science & Innovation Network in India
Delivered by Mr Bharadwaj Raghuraman (AJ)
Social Media Research
Social Media Research
UK Science & Innovation Network in India
Delivered by Dr Jai Ganesh, Cognizant
Network Analysis
Network Analysis
UK Science & Innovation Network in India
Presented by Dr Cathy Mulligan
Sustainable Society Network
Sustainable Society Network
UK Science & Innovation Network in India
Presented by Professor Suresh Bhagavatula
Organisational Network Analysis
Organisational Network Analysis
UK Science & Innovation Network in India
Presented by Dr Richard Adams
Social Media, Big Data and New Business Models
Social Media, Big Data and New Business Models
UK Science & Innovation Network in India
Presented by Professor Gerard Parr
Utilising Cloud Resources to Gain Knowledge from Social Media
Utilising Cloud Resources to Gain Knowledge from Social Media
UK Science & Innovation Network in India
Presented by Professor Pete Edwards
Social Media, Linked'Data & The Context Question
Social Media, Linked'Data & The Context Question
UK Science & Innovation Network in India
Mais de UK Science & Innovation Network in India
(7)
Social Media Research
Social Media Research
Network Analysis
Network Analysis
Sustainable Society Network
Sustainable Society Network
Organisational Network Analysis
Organisational Network Analysis
Social Media, Big Data and New Business Models
Social Media, Big Data and New Business Models
Utilising Cloud Resources to Gain Knowledge from Social Media
Utilising Cloud Resources to Gain Knowledge from Social Media
Social Media, Linked'Data & The Context Question
Social Media, Linked'Data & The Context Question
Último
Capstone
Capstone slidedeck for my capstone final edition.pdf
Capstone slidedeck for my capstone final edition.pdf
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Sociocosmos empowers you to go trendy on social media with a few clicks..pdf
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SocioCosmos
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Capstone
Capstone slidedeck for my capstone project part 2.pdf
Capstone slidedeck for my capstone project part 2.pdf
eliklein8
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The unpredictability of life, influences, and occurrences defies classifications and assigned designations or limitations unless we concede to them. Random experiences carry latent ramifications producing magnified and unforeseen outcomes.
The Butterfly Effect
The Butterfly Effect
Thurston K. Atlas
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Utilitarian aggregation of open data
1.
Utilitarian Aggregation of Open Data Srinath Srinivasa, Sweety Agrawal Chinmay Jog, Jayati Deshmukh IIIT Bangalore
2.
OSL@IIITB ● ● Started in 2002 Current strength: 3 PhD students, 5 MS (by Research) students, 24 MTech project students ● Research Graduates: 4 PhDs, 1 MS ● Part of the Intel PlanetLab grid between 2003—2006 ● Broad research areas: data, systems and cognition ● Specific research areas over the years ● Data models for graph databases ● Distributed query processing ● Data management in ad hoc networks ● Semantics mining from text data ● Analytics of online social spaces ● Community knowledge management ● Linked Open Data ● Computational Cognition Current focus areas
3.
OSL Releases Agama: A graph database for storing large undirected graphs for efficient traversal (not structurebased retrieval) Currently Agama powers a co occurrence graph of all noun phrases from Wikipedia articles hosted in OSL, managing 10s of millions of nodes and 100s of millions of edges
4.
OSL Releases Topical Anchors: Given a list of noun phrases, identify a semantic topic for these terms. Powered by Wikipedia cooccurrence graph hosted by Agama Web APIs enable use of Topical Anchors in third party applications
5.
OSL Releases Topic Expansion: Given
a term, expands it into semantically relevant topical clusters with different senses. Uses co-occurrence datasets from Wikipedia 2006 or 2011. Web APIs enable use by third party applications
6.
OSL Releases Silverfish: A social space for managing and discussing research papers Supports automatic indexing, recommendations and social networking features
7.
Utilitarian Aggregation of Open Data
8.
Open Data Data hosted publicly for use and re publication with a free or open license Usually comprising of structured datasets in the form of tables Major government, NGO and corporate players in the open data space
9.
Open Data in India: A Summary [Agrawal et al. 2013]
10.
Sandesh A “semantic data mesh” over Indian Open Data Connecting elements from different datasets under an overarching semantic structure Challenges Open data about no single topic in particular, fits into no single ontology Contextual boundaries of open data assertions unable to model using LinkedData standards The problem of “openended” data
11.
Challenges in Open Data Aggregation Fragmentation
12.
Challenges in Open Data Aggregation Bounded validity of utilitarian data Consider the following RDF statements: ( Einstein ,
HasWon , NobelPrize) (Wheat , PricePerKilo , 50) Encyclopedic knowledge Utilitarian knowledge Valid everywhere without contextual boundaries Valid only within specific contextual boundaries (market, place, time, etc.) No immediate or specific utility Has immediate and/or specific utility
13.
Challenges in Open Data Aggregation The “divergent” nature of utilitarian aggregation The “convergent” nature of encyclopedic aggregation like Wikipedia articles
14.
Challenges in Open Data Aggregation The “divergent” nature of utilitarian aggregation Utilitarian aggregation involves creation of several “utility worlds” each of which combine a given data with different other data sets for different utilitarian goals.
15.
Challenges in Open Data Aggregation Open Data and Credibility Open data portals hosting utilitarian data (Ex: Data.gov.in) requires credential checks from data sources for establishing trust, which is not so critical for open data portals hosting encyclopedic data (Ex: Wikipedia).
16.
Challenges in Open Data Aggregation The problem of “openended” data Data containing private information about entity p, but which may need to be (legitimately) disseminated and used by several entities unrelated to p Owner (p) of data may not have knowledge or control over consumers of data; but trusts the system to disseminate this data to legitimate consumers Example case studies: ● ICSE marks data ● BPL data
17.
Many Worlds on a Frame (MWF) A trusted, distributed middleware for utilitarian aggregation and dissemination of open data Users as knowledge elements Users MWF Datasets Formal model of MWF developed independently, but representable as a superposition of two Frames in Kripke Semantics Aggregated knowledge in utilitarian “worlds”
18.
MWF: Conceptual World Place Person Conceptual World: A semantic context to host data about something Institution Crop
19.
City is-a is-in is-a is-in State is-a is-in MWF: Frame Place Every conceptual world has a “type” and a “location” specified by an “isa” parent and “isin” parent respectively. The data structure formed by isa and isin connections is called the Frame
20.
MWF: World Structure and Participation Institution Person Office Location Member Components Member Member Associations Heads Office Location ReportsTo Member Place
21.
MWF: Privileges Institution User :: Person Credentials of a Person (User) defined by the roles played by the Person in different worlds Administrator Schema Manager Data owner Casual user Public Credentials determine privilege level in a target world
22.
MWF: Inheritances Place City is-a is-in State isa hierarchy inherits: ● World structure ● Attributes ●
Participations ● Constraints isin hierarchy inherits: ● Privilege levels ● Visibility ● Construction ● Destruction
23.
MWF: Other Features (ongoing work) Constraints Uniqueness constraints Dual Associations Bulk loading of data Cognitive gapfillers Query semantics ● Selectin Answer a query by matching query condition inside a world and its contained worlds ● Selecton Answer a query by matching query condition on a set of worlds of a given type ● Selectworld Answer a query about the participation of a given world in other worlds
24.
MWF: Future Work Distributed MWF with proxy worlds From privileges and constraints to an integrity management subsystem
25.
Some Screenshots
26.
Some Screenshots
27.
Some Screenshots
28.
Some Screenshots
29.
Thank You!
30.
References [Agrawal et al, 2013] Sweety Agrawal, Jayati Deshmukh, Srinath Srinivasa, Chinmay Jog, Sri Sayi Bhavani Kakarla, Rahul Dhek, Sneha Deshpande, Sana Javed and Vikas Mohandoss. A Survey of Indian Open Data. Proceedings of IBM ICARE 2013. ACM Press. New Delhi, India. Oct 2013 [Srinivasa et al, 2014] Srinath Srinvasa, Sweety Agrawal, Chinmay Jog, Jayati Deshmukh. Characterizing Open Utilitarian Knowledge. Proceedings of CoDS 2014, New Delhi, India, March 2014.
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