Social media provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens share information, express opinions, and engage in discussions. Often such a Online Citizen Sensor Community (CSC) has stated or implied goals related to workflows of organizational actors with defined roles and responsibilities. For example, a community of crisis response volunteers, for informing the prioritization of responses for resource needs (e.g., medical) to assist the managers of crisis response organizations. However, in CSC, there are challenges related to information overload for organizational actors, including finding reliable information providers and finding the actionable information from citizens. This threatens awareness and articulation of workflows to enable cooperation between citizens and organizational actors. CSCs supported by Web 2.0 social media platforms offer new opportunities and pose new challenges. This work addresses issues of ambiguity in interpreting unconstrained natural language (e.g., ‘wanna help’ appearing in both types of messages for asking and offering help during crises), sparsity of user and group behaviors (e.g., expression of specific intent), and diversity of user demographics (e.g., medical or technical professional) for interpreting user-generated data of citizen sensors. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues in CSC, and allow better accessibility to user-generated data at higher level of information abstraction for organizational actors. This study presents a novel web information processing framework focused on actors and actions in cooperation, called Identify-Match-Engage (IME), which fuses top-down and bottom-up computing approaches to design a cooperative web information system between citizens and organizational actors. It includes a.) identification of action related seeking-offering intent behaviors from short, unstructured text documents using both declarative and statistical knowledge based classification model, b.) matching of intentions about seeking and offering, and c.) engagement models of users and groups in CSC to prioritize whom to engage, by modeling context with social theories using features of users, their generated content, and their dynamic network connections in the user interaction networks. The results show an improvement in modeling efficiency from the fusion of top-down knowledge-driven and bottom-up data-driven approaches than from conventional bottom-up approaches alone for modeling intent and engagement. Several applications of this work include use of the engagement interface tool during recent crises to enable efficient citizen engagement for spreading critical information of prioritized needs to ensure donation of only required supplies by the citizens. The engagement interface application also won the United Nations ICT agency ITU's Young Innovator 2014 award.
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.
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Semantic, Cognitive, and Perceptual Computing – three intertwined strands of ...Amit Sheth
Keynote at Web Intelligence 2017: http://webintelligence2017.com/program/keynotes/
Video: https://youtu.be/EIbhcqakgvA Paper: http://knoesis.org/node/2698
Abstract: While Bill Gates, Stephen Hawking, Elon Musk, Peter Thiel, and others engage in OpenAI discussions of whether or not AI, robots, and machines will replace humans, proponents of human-centric computing continue to extend work in which humans and machine partner in contextualized and personalized processing of multimodal data to derive actionable information.
In this talk, we discuss how maturing towards the emerging paradigms of semantic computing (SC), cognitive computing (CC), and perceptual computing (PC) provides a continuum through which to exploit the ever-increasing and growing diversity of data that could enhance people’s daily lives. SC and CC sift through raw data to personalize it according to context and individual users, creating abstractions that move the data closer to what humans can readily understand and apply in decision-making. PC, which interacts with the surrounding environment to collect data that is relevant and useful in understanding the outside world, is characterized by interpretative and exploratory activities that are supported by the use of prior/background knowledge. Using the examples of personalized digital health and a smart city, we will demonstrate how the trio of these computing paradigms form complementary capabilities that will enable the development of the next generation of intelligent systems. For background: http://bit.ly/PCSComputing
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.
There is a rapid intertwining of sensors and mobile devices into the fabric of our lives. This has resulted in unprecedented growth in the number of observations from the physical and social worlds reported in the cyber world. Sensing and computational components embedded in the physical world is termed as Cyber-Physical System (CPS). Current science of CPS is yet to effectively integrate citizen observations in CPS analysis. We demonstrate the role of citizen observations in CPS and propose a novel approach to perform a holistic analysis of machine and citizen sensor observations. Specifically, we demonstrate the complementary, corroborative, and timely aspects of citizen sensor observations compared to machine sensor observations in Physical-Cyber-Social (PCS) Systems.
Physical processes are inherently complex and embody uncertainties. They manifest as machine and citizen sensor observations in PCS Systems. We propose a generic framework to move from observations to decision-making and actions in PCS systems consisting of: (a) PCS event extraction, (b) PCS event understanding, and (c) PCS action recommendation. We demonstrate the role of Probabilistic Graphical Models (PGMs) as a unified framework to deal with uncertainty, complexity, and dynamism that help translate observations into actions. Data driven approaches alone are not guaranteed to be able to synthesize PGMs reflecting real-world dependencies accurately. To overcome this limitation, we propose to empower PGMs using the declarative domain knowledge. Specifically, we propose four techniques: (a) automatic creation of massive training data for Conditional Random Fields (CRFs) using domain knowledge of entities used in PCS event extraction, (b) Bayesian Network structure refinement using causal knowledge from Concept Net used in PCS event understanding, (c) knowledge-driven piecewise linear approximation of nonlinear time series dynamics using Linear Dynamical Systems (LDS) used in PCS event understanding, and the (d) transforming knowledge of goals and actions into a Markov Decision Process (MDP) model used in PCS action recommendation.
We evaluate the benefits of the proposed techniques on real-world applications involving traffic analytics and Internet of Things (IoT).
This is a brief a brief review of current multi-disciplinary and collaborative projects at Kno.e.sis led by Prof. Amit Sheth. They cover research in big social data, IoT, semantic web, semantic sensor web, health informatics, personalized digital health, social data for social good, smart city, crisis informatics, digital data for material genome initiative, etc. Dec 2015 edition.
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road link can be interpreted in terms of traffic related incident reports from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner.
We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.
This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analy- sis, and reasoning will be discussed. The tutorial will de- scribe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions re- lated to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, do- main knowledge integration and management, querying large- scale IoT data, and AI applications for automated knowledge extraction from real world data.
Related: Semantic Sensor Web: http://knoesis.org/projects/ssw
Physical-Cyber-Social Computing: http://wiki.knoesis.org/index.php/PCS
Semantic, Cognitive, and Perceptual Computing – three intertwined strands of ...Amit Sheth
Keynote at Web Intelligence 2017: http://webintelligence2017.com/program/keynotes/
Video: https://youtu.be/EIbhcqakgvA Paper: http://knoesis.org/node/2698
Abstract: While Bill Gates, Stephen Hawking, Elon Musk, Peter Thiel, and others engage in OpenAI discussions of whether or not AI, robots, and machines will replace humans, proponents of human-centric computing continue to extend work in which humans and machine partner in contextualized and personalized processing of multimodal data to derive actionable information.
In this talk, we discuss how maturing towards the emerging paradigms of semantic computing (SC), cognitive computing (CC), and perceptual computing (PC) provides a continuum through which to exploit the ever-increasing and growing diversity of data that could enhance people’s daily lives. SC and CC sift through raw data to personalize it according to context and individual users, creating abstractions that move the data closer to what humans can readily understand and apply in decision-making. PC, which interacts with the surrounding environment to collect data that is relevant and useful in understanding the outside world, is characterized by interpretative and exploratory activities that are supported by the use of prior/background knowledge. Using the examples of personalized digital health and a smart city, we will demonstrate how the trio of these computing paradigms form complementary capabilities that will enable the development of the next generation of intelligent systems. For background: http://bit.ly/PCSComputing
Knowledge Will Propel Machine Understanding of Big DataAmit Sheth
Preview video: https://youtu.be/4e0dtV7CTWM
CCKS Keynote, August 2017: http://www.ccks2017.com/?page_id=358
SEAS Summer School, July 2017
https://sites.google.com/view/seasschool2017/talks
Related paper: http://knoesis.org/node/2835
CCKS Conf had over 500 attendees- some photos: https://photos.app.goo.gl/5CdlfAX1uYwvgqsQ2
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Amit Sheth
Amit Sheth's Keynote at Semantic Web Technologies for Science and Engineering Workshop (held in conjunction with ISWC2003), Sanibel Island, FL, October 20, 2003.
Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Inte...Amit Sheth
Ora Lassila and Amit Sheth, "Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Interoperability", Invited Talk at ONC-HHS Invitational Workshop on Next Generation Interoperability for Health, Washington DC, January 19-20, 2011.
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Presented at the Panel on
Sensor, Data, Analytics and Integration in Advanced Manufacturing, at the Connected Manufacturing track of Bosch-USA organized "Leveraging Public-Private Partnerships for Regional Growth Summit". Panel statement: Sensors, data and analytics are the core of any smart manufacturing system. What are the main challenges to create actionable outputs, replicate systems and scale efficiency gains across industries?
Moderator: Thomas Stiedl, Bosch
Panelists:
1. Amit Sheth, Wright State University
2. Howie Choset, Carnegie Melon University
3. Nagi Gebraeel, Georgia Institute of Technology
4. Brian Anthony, Massachusetts Institute of Technology
5. Yarom Polosky, Oak Ridget National Laboratory
For in-depth look:
Smart IoT: IoT as a human agent, human extension, and human complement
http://amitsheth.blogspot.com/2015/03/smart-iot-iot-as-human-agent-human.html
Semantic Gateway: http://knoesis.org/library/resource.php?id=2154
SSN Ontology: http://knoesis.org/library/resource.php?id=1659
Applications of Multimodal Physical (IoT), Cyber and Social Data for Reliable and Actionable Insights: http://knoesis.org/library/resource.php?id=2018
Smart Data: Transforming Big Data into Smart Data...: http://wiki.knoesis.org/index.php/Smart_Data
Historic use of the term Smart Data (2004): http://www.scribd.com/doc/186588820
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, ...Amit Sheth
Keynote given at ICDE2014, April 2014. Details at: http://ieee-icde2014.eecs.northwestern.edu/keynotes.html
A video of a version of this talk is available here: http://youtu.be/8RhpFlfpJ-A
(download to see many hidden slides).
Two versions of this talk, targeted at Smart Energy and Personalized Digital Health domains/apps at: http://wiki.knoesis.org/index.php/Smart_Data
Previous (older) version replaced by this version: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Structured data on the Web frequently referred to as knowledge graphs consists of large number of datasets representing diverse domains. Widely used commercial applications such as entity recommendation, search, question answering and knowledge discovery use these knowledge graphs as their knowledge source. Majority of these applications have a particular domain of interest, hence require only the segment of the Web of data representing that domain (e.g., movie, biomedical, sports). In fact, leveraging the entire Web of data for a domain-specific application is not only computationally intensive, but also the irrelevant portion negatively impact the accuracy of the application. Hence, finding the relevant portion of the Web of data for domain-specific applications has become a paramount issue. Identifying the relevant portion of the Web of data consists of two sub-tasks; 1) find the relevant datasets that contain knowledge on the domain of interest, and 2) extract the subgraph representing domain of interest from the knowledge graphs that represent multiple domains (e.g., DBpedia, YAGO, Freebase). In this talk, I will discuss both data-driven and knowledge-driven approaches to solve these two sub-tasks. The domain-specific subgraphs extracted by our approach were 80% less in size in terms of the number of paths compared to original KG and resulted in more than tenfold reduction of required computational time for domain-specific tasks, yet produced better accuracy on domain-specific applications. We believe that this work can significantly contribute for utilizing knowledge graphs for domain-specific applications, specially with the explosive growth in the creation of knowledge graphs.
Presented at SW2012 @ ISWC2012.
http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html
This is an old version of this talk, for more recent information on this topic (eg talks, papers, events), see: http://wiki.knoesis.org/index.php/PCS
SMART Infrastructure Facility Associate Professor Rodney Clark, shared his work with the wider university community recently when he presented a SMART Seminar. Titled, ‘Tweets, Emergencies and Experience - New Theory and Methods in support of the PetaJakarta Project’, SMART's Co-Lab Manager presented this seminar on November 18th, 2014.
Student Achievement Review (initially presented during Inauguration Function of the Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)) - updated since
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
Where are all the Semantic Web agents? There are billions of "machine readable" open facts on the Semantic Web, i.e. Linked Open Data (LOD), isn't that enough? It looks like it's not. We're still far from seeing Lucy's and Pete's agents brilliantly solving their tasks with the help of other Semantic Web agents they can trust (Tim Berners Lee et al., The Semantic Web, Scientific American (2001) ). Despite its technological impact on many applications and areas, the Semantic Web promised to cause a breakthrough that we didn't yet experience. One issue is that LOD ontologies are not as linked as they should be. Another issue is that formalising only semi-structured Web pages or databases is not enough for making them able to operate. They also need to reason with commonsense knowledge, the encoding of which is a long-standing challenge in Artificial Intelligence. A third consideration is that most existing commonsense knowledge bases lack formal semantics and situational constraints. In this talk I will advocate the role of the Semantic Web as a provider of a knowledge graph of commonsense to Artificial Intelligence, and discuss ways and obstacles towards the achievement of this goal.
Full day lectures @International University, HCM City, Vietnam, May 2019. Review of AI in 2019; outlook into the future; empirical research in AI; introduction to AI research at Deakin University
Vahid Taslimitehrani's Dissertation Defense: Friday, February 19 2015.
Ph.D. Committee: Drs. Guozhu Dong, Advisor, T.K. Prasad, Amit Sheth, Keke Chen
and Jyotishman Pathak, Division of Health Informatics, Weill Cornell Medical College, Cornell University.
ABSTRACT:
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy.
In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where a given baseline model has large modeling errors, characterizing such regions using patterns, and learning specialized models for those regions. Each PXR/PXC model contains several pairs of contrast patterns and local models, where a local classifier is applied only to data instances matching its associated pattern. We also propose a class of classification and regression techniques called Contrast Pattern Aided Regression (CPXR) and Contrast Pattern Aided Classification (CPXC) to build accurate and interpretable PXR and PXC models.
We have conducted a set of comprehensive performance studies to evaluate the performance of CPXR and CPXC. The results show that CPXR and CPXC outperform state-of-the-art regression and classification algorithms, often by significant margins. The results also show that CPXR and CPXC are especially effective for heterogeneous and high dimensional datasets. Besides being new types of modeling, PXR and PXC models can also provide insights into data heterogeneity and diverse predictor-response relationships.
We have also adapted CPXC to handle classifying imbalanced datasets and introduced a new algorithm called Contrast Pattern Aided Classification for Imbalanced Datasets (CPXCim). In CPXCim, we applied a weighting method to boost minority instances as well as a new filtering method to prune patterns with imbalanced matching datasets.
Finally, we applied our techniques on three real applications, two in the healthcare domain and one in the soil mechanic domain. PXR and PXC models are significantly more accurate than other learning algorithms in those three applications.
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010
Knowledge Will Propel Machine Understanding of Big DataAmit Sheth
Preview video: https://youtu.be/4e0dtV7CTWM
CCKS Keynote, August 2017: http://www.ccks2017.com/?page_id=358
SEAS Summer School, July 2017
https://sites.google.com/view/seasschool2017/talks
Related paper: http://knoesis.org/node/2835
CCKS Conf had over 500 attendees- some photos: https://photos.app.goo.gl/5CdlfAX1uYwvgqsQ2
Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.
Semantics for Bioinformatics: What, Why and How of Search, Integration and An...Amit Sheth
Amit Sheth's Keynote at Semantic Web Technologies for Science and Engineering Workshop (held in conjunction with ISWC2003), Sanibel Island, FL, October 20, 2003.
Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Inte...Amit Sheth
Ora Lassila and Amit Sheth, "Semantic Web for 360-degree Health: State-of-the-Art & Vision for Better Interoperability", Invited Talk at ONC-HHS Invitational Workshop on Next Generation Interoperability for Health, Washington DC, January 19-20, 2011.
Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
Presented at the Panel on
Sensor, Data, Analytics and Integration in Advanced Manufacturing, at the Connected Manufacturing track of Bosch-USA organized "Leveraging Public-Private Partnerships for Regional Growth Summit". Panel statement: Sensors, data and analytics are the core of any smart manufacturing system. What are the main challenges to create actionable outputs, replicate systems and scale efficiency gains across industries?
Moderator: Thomas Stiedl, Bosch
Panelists:
1. Amit Sheth, Wright State University
2. Howie Choset, Carnegie Melon University
3. Nagi Gebraeel, Georgia Institute of Technology
4. Brian Anthony, Massachusetts Institute of Technology
5. Yarom Polosky, Oak Ridget National Laboratory
For in-depth look:
Smart IoT: IoT as a human agent, human extension, and human complement
http://amitsheth.blogspot.com/2015/03/smart-iot-iot-as-human-agent-human.html
Semantic Gateway: http://knoesis.org/library/resource.php?id=2154
SSN Ontology: http://knoesis.org/library/resource.php?id=1659
Applications of Multimodal Physical (IoT), Cyber and Social Data for Reliable and Actionable Insights: http://knoesis.org/library/resource.php?id=2018
Smart Data: Transforming Big Data into Smart Data...: http://wiki.knoesis.org/index.php/Smart_Data
Historic use of the term Smart Data (2004): http://www.scribd.com/doc/186588820
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, ...Amit Sheth
Keynote given at ICDE2014, April 2014. Details at: http://ieee-icde2014.eecs.northwestern.edu/keynotes.html
A video of a version of this talk is available here: http://youtu.be/8RhpFlfpJ-A
(download to see many hidden slides).
Two versions of this talk, targeted at Smart Energy and Personalized Digital Health domains/apps at: http://wiki.knoesis.org/index.php/Smart_Data
Previous (older) version replaced by this version: http://www.slideshare.net/apsheth/big-data-to-smart-data-keynote
Presentation at the AAAI 2013 Fall Symposium on Semantics for Big Data, Arlington, Virginia, November 15-17, 2013
Additional related material at: http://wiki.knoesis.org/index.php/Smart_Data
Related paper at: http://www.knoesis.org/library/resource.php?id=1903
Abstract: We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. We organize our research around the five V's of Big Data, where four of the Vs are harnessed to produce the fifth V - value. To handle the challenge of Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle the challenge of Variety, we resort to the use of semantic models and annotations of data so that much of the intelligent processing can be done at a level independent of heterogeneity of data formats and media. To handle the challenge of Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize new concepts, entities and facts. To handle Veracity, we explore the formalization of trust models and approaches to glean trustworthiness. The above four Vs of Big Data are harnessed by the semantics-empowered analytics to derive Value for supporting practical applications transcending physical-cyber-social continuum.
Structured data on the Web frequently referred to as knowledge graphs consists of large number of datasets representing diverse domains. Widely used commercial applications such as entity recommendation, search, question answering and knowledge discovery use these knowledge graphs as their knowledge source. Majority of these applications have a particular domain of interest, hence require only the segment of the Web of data representing that domain (e.g., movie, biomedical, sports). In fact, leveraging the entire Web of data for a domain-specific application is not only computationally intensive, but also the irrelevant portion negatively impact the accuracy of the application. Hence, finding the relevant portion of the Web of data for domain-specific applications has become a paramount issue. Identifying the relevant portion of the Web of data consists of two sub-tasks; 1) find the relevant datasets that contain knowledge on the domain of interest, and 2) extract the subgraph representing domain of interest from the knowledge graphs that represent multiple domains (e.g., DBpedia, YAGO, Freebase). In this talk, I will discuss both data-driven and knowledge-driven approaches to solve these two sub-tasks. The domain-specific subgraphs extracted by our approach were 80% less in size in terms of the number of paths compared to original KG and resulted in more than tenfold reduction of required computational time for domain-specific tasks, yet produced better accuracy on domain-specific applications. We believe that this work can significantly contribute for utilizing knowledge graphs for domain-specific applications, specially with the explosive growth in the creation of knowledge graphs.
Presented at SW2012 @ ISWC2012.
http://amitsheth.blogspot.com/2012/08/semantics-empowered-physical-cyber.html
This is an old version of this talk, for more recent information on this topic (eg talks, papers, events), see: http://wiki.knoesis.org/index.php/PCS
SMART Infrastructure Facility Associate Professor Rodney Clark, shared his work with the wider university community recently when he presented a SMART Seminar. Titled, ‘Tweets, Emergencies and Experience - New Theory and Methods in support of the PetaJakarta Project’, SMART's Co-Lab Manager presented this seminar on November 18th, 2014.
Student Achievement Review (initially presented during Inauguration Function of the Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)) - updated since
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
Where are all the Semantic Web agents? There are billions of "machine readable" open facts on the Semantic Web, i.e. Linked Open Data (LOD), isn't that enough? It looks like it's not. We're still far from seeing Lucy's and Pete's agents brilliantly solving their tasks with the help of other Semantic Web agents they can trust (Tim Berners Lee et al., The Semantic Web, Scientific American (2001) ). Despite its technological impact on many applications and areas, the Semantic Web promised to cause a breakthrough that we didn't yet experience. One issue is that LOD ontologies are not as linked as they should be. Another issue is that formalising only semi-structured Web pages or databases is not enough for making them able to operate. They also need to reason with commonsense knowledge, the encoding of which is a long-standing challenge in Artificial Intelligence. A third consideration is that most existing commonsense knowledge bases lack formal semantics and situational constraints. In this talk I will advocate the role of the Semantic Web as a provider of a knowledge graph of commonsense to Artificial Intelligence, and discuss ways and obstacles towards the achievement of this goal.
Full day lectures @International University, HCM City, Vietnam, May 2019. Review of AI in 2019; outlook into the future; empirical research in AI; introduction to AI research at Deakin University
Vahid Taslimitehrani's Dissertation Defense: Friday, February 19 2015.
Ph.D. Committee: Drs. Guozhu Dong, Advisor, T.K. Prasad, Amit Sheth, Keke Chen
and Jyotishman Pathak, Division of Health Informatics, Weill Cornell Medical College, Cornell University.
ABSTRACT:
Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy.
In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where a given baseline model has large modeling errors, characterizing such regions using patterns, and learning specialized models for those regions. Each PXR/PXC model contains several pairs of contrast patterns and local models, where a local classifier is applied only to data instances matching its associated pattern. We also propose a class of classification and regression techniques called Contrast Pattern Aided Regression (CPXR) and Contrast Pattern Aided Classification (CPXC) to build accurate and interpretable PXR and PXC models.
We have conducted a set of comprehensive performance studies to evaluate the performance of CPXR and CPXC. The results show that CPXR and CPXC outperform state-of-the-art regression and classification algorithms, often by significant margins. The results also show that CPXR and CPXC are especially effective for heterogeneous and high dimensional datasets. Besides being new types of modeling, PXR and PXC models can also provide insights into data heterogeneity and diverse predictor-response relationships.
We have also adapted CPXC to handle classifying imbalanced datasets and introduced a new algorithm called Contrast Pattern Aided Classification for Imbalanced Datasets (CPXCim). In CPXCim, we applied a weighting method to boost minority instances as well as a new filtering method to prune patterns with imbalanced matching datasets.
Finally, we applied our techniques on three real applications, two in the healthcare domain and one in the soil mechanic domain. PXR and PXC models are significantly more accurate than other learning algorithms in those three applications.
Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature, which influenced innovations in diagnosis, treatment, preventions and overall public health. However, much of the existing research on discovering hidden connections among concepts have used distributional statistics and graph-theoretic measures to capture implicit associations. Such metrics do not explicitly capture the semantics of hidden connections. ...
While effective in some situations, the practice of relying on domain expertise, structured background knowledge and heuristics to complement distributional and graph-theoretic approaches, has serious limitations. ..
This dissertation proposes an innovative context-driven, automatic subgraph creation method for finding hidden and complex associations among concepts, along multiple thematic dimensions. It outlines definitions for context and shared context, based on implicit and explicit (or formal) semantics, which compensate for deficiencies in statistical and graph-based metrics. It also eliminates the need for heuristics a priori. An evidence-based evaluation of the proposed framework showed that 8 out of 9 existing scientific discoveries could be recovered using this approach. Additionally, insights into the meaning of associations could be obtained using provenance provided by the system. In a statistical evaluation to determine the interestingness of the generated subgraphs, it was observed that an arbitrary association is mentioned in only approximately 4 articles in MEDLINE, on average. These results suggest that leveraging implicit and explicit context, as defined in this dissertation, is an advancement of the state-of-the-art in LBD research.
Ph.D. Committee: Drs. Amit Sheth (Advisor), TK Prasad, Michael Raymer,
Ramakanth Kavuluru (UKY), Thomas C. Rindflesch (NLM) and Varun Bhagwan (Yahoo! Labs)
Relevant Publications (more at: http://knoesis.wright.edu/students/delroy/)
D. Cameron, R. Kavuluru, T. C. Rindflesch, O. Bodenreider, A. P. Sheth, K. Thirunarayan. Leveraging Distributional Semantics for Domain Agnostic Literature-Based Discovery (under preparation)
D. Cameron, O. Bodenreider, H. Yalamanchili, T. Danh, S. Vallabhaneni, K. Thirunarayan, A. P. Sheth, T. C. Rindflesch. A Graph-based Recovery and Decomposition of Swanson’s Hypothesis using Semantic Predications. Journal of Biomedical Informatics (JBI13), 46(2): 238–251, 2013
D. Cameron, R. Kavuluru, O. Bodenreider, P. N. Mendes, A. P. Sheth, K. Thirunarayan. Semantic Predications for Complex Information Needs in Biomedical Literature International Bioinformatics and Biomedical Conference (BIBM11), pp. 512–519, 2011 (acceptance rate=19.4%)
D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan. Semantics-empowered Text Exploration for Knowledge Discovery. ACM Southeast Conference (ACMSE10), 14, 2010
Cory Henson defended his thesis on "A Semantics-based Approach to Machine Perception".
Video can be found at: http://www.youtube.com/watch?v=L8M7eoGKtSE
Video: https://www.youtube.com/watch?v=ZCToaDgxnAs
Abstract:
People's emotions can be gleaned from their text using machine learning techniques to build models that exploit large self-labeled emotion data from social media. Further, the self-labeled emotion data can be effectively adapted to train emotion classifiers in different target domains where training data are sparse.
Emotions are both prevalent in and essential to most aspects of our lives. They influence our decision-making, affect our social relationships and shape our daily behavior. With the rapid growth of emotion-rich textual content, such as microblog posts, blog posts, and forum discussions, there is a growing need to develop algorithms and techniques for identifying people's emotions expressed in text. It has valuable implications for the studies of suicide prevention, employee productivity, well-being of people, customer relationship management, etc. However, emotion identification is quite challenging partly due to the following reasons: i) It is a multi-class classification problem that usually involves at least six basic emotions. Text describing an event or situation that causes the emotion can be devoid of explicit emotion-bearing words, thus the distinction between different emotions can be very subtle, which makes it difficult to glean emotions purely by keywords. ii) Manual annotation of emotion data by human experts is very labor-intensive and error-prone. iii) Existing labeled emotion datasets are relatively small, which fails to provide a comprehensive coverage of emotion-triggering events and situations.
Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries.
First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases.
While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from 100+ million search queries from smarts devices (smartphones/tablets) and personal computers (desktops/laptops)
Video of the talk: https://www.youtube.com/watch?v=7k-u_TUew3o
Abstract: Social media has experienced immense growth in recent times. These platforms are becoming increasingly common for information seeking and consumption, and as part of its growing popularity, information overload pose a significant challenge to users. For instance, Twitter alone generates around 500 million tweets per day and it is impractical for users to have to parse through such an enormous stream to find information that are interesting to them. This situation necessitates efficient personalized filtering mechanisms for users to consume relevant, interesting information from social media.
Building a personalized filtering system involves understanding users interests and utilizing these interests to deliver relevant information to users. These tasks primarily include analyzing and processing social media text which is challenging due to its shortness in length, and the real-time nature of the medium. The challenges include: (1) Lack of semantic context: Social Media posts are on an average short in length, which provides limited semantic context to perform textual analysis. This is particularly detrimental for topic identification which is a necessary task for mining users interests; (2) Dynamically changing vocabulary: Most social media websites such as Twitter and Facebook generate posts that are of current (timely) interests to the users. Due to this real-time nature, information relevant to dynamic topics of interest evolve reflecting the changes in the real world. This in turn changes the vocabulary associated with these dynamic topics of interest making it harder to filter relevant information; (3) Scalability: The number of users on social media platforms are significantly large, which is difficult for centralized systems to scale to deliver relevant information to users. This dissertation is devoted to exploring semantic techniques and Semantic Web technologies to address the above mentioned challenges in building a personalized information filtering system for social media. Particularly, the necessary semantics (knowledge) is derived from crowd sourced knowledge bases such as Wikipedia to improve context for understanding short-text and dynamic topics on social media.
Description - Ajith defended his thesis on application and data portability in cloud
computing. More details on Ajith's research and publications can be
found at http://knoesis.wright.edu/researchers/ajith/
Video can be found at : http://www.youtube.com/watch?v=oDBeBIIFmHc&list=UUORqXk1ZV44MOwpCorAROyQ&index=1&feature=plpp_video
Sujan Perera's Dissertation Defense: Friday, August 12, 2016
Ph.D. Committee: Drs. Amit Sheth, Advisor; T.K. Prasad, Michael Raymer, and Pablo Mendes (IBM Research)
Video: https://youtu.be/pbjJ1zb8ayY
ABSTRACT:
Natural language is a powerful tool developed by humans over hundreds of thousands of years. The extensive usage, flexibility of the language, creativity of the human beings, and social, cultural, and economic changes that have taken place in daily life have added new constructs, styles, and features to the language. One such feature of the language is its ability to express ideas, opinions, and facts in an implicit manner. This is a feature that is used extensively in day to day communications in situations such as: 1) expressing sarcasm, 2) when trying to recall forgotten things, 3) when required to convey descriptive information, 4) when emphasizing the features of an entity, and 5) when communicating a common understanding.
Consider the tweet 'New Sandra Bullock astronaut lost in space movie looks absolutely terrifying' and the text snippet extracted from a clinical narrative 'He is suffering from nausea and severe headaches. Dolasteron was prescribed.' The tweet has an implicit mention of the entity Gravity and the clinical text snippet has implicit mention of the relationship between medication Dolasteron and clinical condition nausea. Such implicit references of the entities and the relationships are common occurrences in daily communication and they add unique value to conversations. However, extracting implicit constructs has not received enough attention. This dissertation focuses on extracting implicit entities and relationships from clinical narratives and extracting implicit entities from Tweets.
This dissertation demonstrates manifestations of implicit constructs in text, studies their characteristics, and develops a solution that is capable of extracting implicit factual information from text. The developed solution starts by acquiring relevant knowledge to solve the implicit information extraction problem. The relevant knowledge includes domain knowledge, contextual knowledge, and linguistic knowledge. The acquired knowledge can take different syntactic forms such as a text snippet, structured knowledge represented in standard knowledge representation languages like Resource Description Framework (RDF) or custom formats. Hence, the acquired knowledge is processed to create models that can be understood by machines. Such models provide the infrastructure to perform implicit information extraction of interest.
This dissertation focuses on three different use cases of implicit information and demonstrates the applicability of the developed solution in these use cases. They are:
- implicit entity linking in clinical narratives,
- implicit entity linking in Twitter,
- implicit relationship extraction from clinical narratives.
Dissertation Defense:
" Mining and Analyzing Subjective Experiences in User Generated Content "
By Lu Chen
Tuesday, April 9, 2016
Dissertation Committee: Dr. Amit Sheth, Advisor, Dr. T. K. Prasad, Dr. Keke Chen, Dr. Ingmar Weber, and Dr. Justin Martineau,
Pictures: https://www.facebook.com/Kno.e.sis/photos/?tab=album&album_id=1225911137443732
Video: https://youtu.be/tzLEUB-hggQ
Lu's Home page: http://knoesis.wright.edu/researchers/luchen/
ABSTRACT
Web 2.0 and social media enable people to create, share and discover information instantly anywhere, anytime. A great amount of this information is subjective information -- the information about people's subjective experiences, ranging from feelings of what is happening in our daily lives to opinions on a wide variety of topics. Subjective information is useful to individuals, businesses, and government agencies to support decision making in areas such as product purchase, marketing strategy, and policy making. However, much useful subjective information is buried in ever-growing user generated data on social media platforms, it is still difficult to extract high quality subjective information and make full use of it with current technologies.
Current subjectivity and sentiment analysis research has largely focused on classifying the text polarity -- whether the expressed opinion regarding a specific topic in a given text is positive, negative, or neutral. This narrow definition does not take into account the other types of subjective information such as emotion, intent, and preference, which may prevent their exploitation from reaching its full potential. This dissertation extends the definition and introduces a unified framework for mining and analyzing diverse types of subjective information. We have identified four components of a subjective experience: an individual who holds it, a target that elicits it (e.g., a movie, or an event), a set of expressions that describe it (e.g., "excellent", "exciting"), and a classification or assessment that characterize it (e.g., positive vs. negative). Accordingly, this dissertation makes contributions in developing novel and general techniques for the tasks of identifying and extracting these components.
We first explore the task of extracting sentiment expressions from social media posts. We propose an optimization-based approach that extracts a diverse set of sentiment-bearing expressions, including formal and slang words/phrases, for a given target from an unlabeled corpus. Instead of associating the overall sentiment with a given text, this method assesses the more fine-grained target-dependent polarity of each sentiment expression. Unlike pattern-based approaches which often fail to capture the diversity of sentiment expressions due to the informal nature of language usage and writing style in social media posts, the proposed approach is capable of identifying sentiment phrase
The recent emergence of the “Linked Data” approach for publishing data represents a major step forward in realizing the original vision of a web that can "understand and satisfy the requests of people and machines to use the web content" – i.e. the Semantic Web. This new approach has resulted in the Linked Open Data (LOD) Cloud, which includes more than 70 large datasets contributed by experts belonging to diverse communities such as geography, entertainment, and life sciences. However, the current interlinks between datasets in the LOD Cloud – as we will illustrate – are too shallow to realize much of the benefits promised. If this limitation is left unaddressed, then the LOD Cloud will merely be more data that suffers from the same kinds of problems, which plague the Web of Documents, and hence the vision of the Semantic Web will fall short.
This thesis presents a comprehensive solution to address the issue of alignment and relationship identification using a bootstrapping based approach. By alignment we mean the process of determining correspondences between classes and properties of ontologies. We identify subsumption, equivalence and part-of relationship between classes. The work identifies part-of relationship between instances. Between properties we will establish subsumption and equivalence relationship. By bootstrapping we mean the process of being able to utilize the information which is contained within the datasets for improving the data within them. The work showcases use of bootstrapping based methods to identify and create richer relationships between LOD datasets. The BLOOMS project (http://wiki.knoesis.org/index.php/BLOOMS) and the PLATO project, both built as part of this research, have provided evidence to the feasibility and the applicability of the solution.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
Smart Data - How you and I will exploit Big Data for personalized digital hea...Amit Sheth
Amit Sheth's keynote at IEEE BigData 2014, Oct 29, 2014.
Abstract from:
http://cci.drexel.edu/bigdata/bigdata2014/keynotespeech.htm
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is personalized digital health that related to taking better decisions about our health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (e.g., information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will describe Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If my child is an asthma patient, for all the data relevant to my child with the four V-challenges, what I care about is simply, “How is her current health, and what are the risk of having an asthma attack in her current situation (now and today), especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP. I will motivate the need for a synergistic combination of techniques similar to the close interworking of the top brain and the bottom brain in the cognitive models.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city.
An overview of a social psychological approach to the design of social technologies, with design principles and a brief review of how I applied these principles to several R&D projects in the past few years.
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"Understanding Broadband from the Outside"
Ricardo Ramírez
Freelance researcher and consultant, adjunct professor at the University of Guelph, Ontario, Canada
http://arnic.info/ramirezseminar.php
THE SURVEY OF SENTIMENT AND OPINION MINING FOR BEHAVIOR ANALYSIS OF SOCIAL MEDIAIJCSES Journal
Nowadays, internet has changed the world into a global village. Social Media has reduced the gaps among
the individuals. Previously communication was a time consuming and expensive task between the people.
Social Media has earned fame because it is a cheaper and faster communication provider. Besides, social
media has allowed us to reduce the gaps of physical distance, it also generates and preserves huge amount
of data. The data are very valuable and it presents association degree between people and their opinions.The comprehensive analysis of the methods which are used on user behavior prediction is presented in this paper. This comparison will provide a detailed information, pros and cons in the domain of sentiment and
opinion mining.
Graph-based Analysis and Opinion Mining in Social NetworkKhan Mostafa
This is the final report for Networks & Data Mining Techniques project focusing on mining social network to estimate public opinion about entities and associated keywords. This project mines Twitter for recent feeds and analyzes them to estimate sentiment score, discussed entity and describing keywords in each tweet. This data is then exploited to elicit overall sentiment associated with each entity. Entities and keywords extracted is also used to form an entity-keyword bigraph. This graph is further used to detect entity communities and keywords found within those communities. Presented implementation works in linear time.
Sentiment Mining of Community Development Program Evaluation Based on Social ...TELKOMNIKA JOURNAL
It is crucial to support community-oriented services for youth awareness in the social media with knowledge extraction, which would be useful for both government agencies and community group of interest for program evaluation. This work provided to formulate effective evaluation on community development program and addressing them to a correct action. By using classification based SVM, evaluation of the achievement level conducted in both quantitative and qualitative analysis, particularly to conclude which activities has high success rate. By using social media based activities, this study searched the sentiment analysis from every activities comments based on their tweet. First, we kicked off preprocessing stage, reducing feature space by using principle of component analysis and estimate parameters for classification purposes. Second, we modeled activity classification by using support vector machine. At last, set term score by calculating term frequency, which combined with term sentiment scores based on lexicon.The result shows that models provided sentiment summarization that point out the success level of positive sentiment.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...csandit
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to high-profile news. Such volatility, can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information.
Such mechanisms include statistical data only, without considering the collective feeling. This article, is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...cscpconf
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to the high-profile news. Such volatility can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information. Such mechanisms include statistical data only, without considering the collective feeling. This article is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an
attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem.
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
A large-scale sentiment analysis using political tweetsIJECEIAES
Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC.
Need Response 1The subcomponent of crowdsourcing ICT platform.docxvannagoforth
Need Response 1:
The subcomponent of crowdsourcing ICT platform technological architecture I would like to discuss is that gives additionally created an examination concerning public text information (for instance blog postings, comments, appraisals, etc.) and setting up this information sources using sentiment mining tools. The Web has changed the way wherein people express their emotions, offering them the capacity to post comments and reviews on business things and express their points of view on a a huge amount of issues in parties, talk get-togethers, visit rooms, long-broaden agreeable correspondence get-togethers and web diaries. This customer passed on the substance has been seen as a tremendous wellspring of business and political information. Notwithstanding, the tremendous the degree of this information and its normal language structure makes it difficult to remove the consistent areas, for instance, the general inclination/assessment (for instance positive, negative or sensible) on the particular subject (for instance a thing/affiliation or another methodology proposition) and the specific issues raised about it by the customers/visitors of these objectives. It is hidden motivation has been to enable firms to research online overviews and comments entered by customers of their things in various review districts, web diaries, social affairs, etc., in order to arrive at general judgments as for whether customers adored the thing or not (supposition assessment), and moreover continuously express finishes concerning features (traits) of the thing that has been commented on insistently or conflictingly (features extraction and examination).
This subcomponent performs three tasks, firstly it classifies the opinion text, a document which includes various declarations like a dialogue or a blog spot conveying a positive, negative or unprejudiced end. This is suggested as the record level evaluation examination. Secondly further focusing on sentence-level which deals with the gathering of a sentence as objective or passionate, it organizes each sentence in such a structure, that atmosphere it is a unique or targets (demonstrating whether it can express the inclination or not). For each sentence that is a conceptual (infers conveying an inclination) further, the portrayal is done as imparting an appositive, negative or unprejudiced supposition. Lastly extracting the most commented features of the commented articles, and for each commented feature further classification of relevant opinion is executed as positive, negative or unprejudiced.
References
Janssen, M., Wimmer, M. A., & Deljoo, A. (Eds.). (2015). Policy practice and digital science: Integrating complex systems, social simulation and public administration in policy research (Vol. 10). Springer
Need response 2:
nformation and communication technology platform has an important role to play in active crowdsourcing. A policy maker of a government agency initiates ...
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Hemant Purohit PhD Defense: Mining Citizen Sensor Communities for Cooperation with Organizations
1. Mining Citizen Sensor Communities to Improve
Cooperation with Organizational Actors
June 23 2015
PhD Defense
Hemant Purohit (Advisor: Prof. Amit Sheth)
Kno.e.sis, Dept. of CSE, Wright State University, USA
2. @hemant_pt
Outline
— Citizen Sensor Communities & Organizations
— Cooperative System Design Challenges
— Contributions
— Problem 1. Conversation Classification using Offline Theories
— Problem 2. Intent Classification
— Problem 3. Engagement Modeling
— Applications
— Limitations & Future Work
2
3. @hemant_pt
Citizen Sensors: Access to Human
Observations & Interactions
Uni-directional communication
(TO people)
Unstructured, Unconstrained Language Data
• Ambiguity
• Sparsity
• Diversity
• Scalability
Bi-directional
(BY people, TO people)
Web 2.0
media
3
4. @hemant_pt
Goal: Data to Decision Making
Organizational Decision Making
Noisy Citizen Sensor data
4
SOCIAL SCIENCE
• Experts on Organizations
• Small-scale Data
COMPUTER SCIENCE
• Experts on Mining
• Large-scale data
Scope of My
Research
5. @hemant_pt
1. No Structured Roles
2. No Defined Tasks
ü But “GENERATE”
Massive Data
1. Structured Roles
2. Defined Tasks
ü COLLECT Data
ü Process, & Make Decisions
ORGANIZATIONS
Sure!
How to help?
CITIZEN
SENSOR
COMMUNITIES
5
COOPERATIVE
SYSTEM
Can you
help us?
7. @hemant_pt
Articulation
Challenges
(Malone & Crowston 1990;
Schmidt & Bannon 1992)
ENGAGEMENT MODELING INTENT MINING
COOPERATIVE
SYSTEM
DATA
PROBLEM
DESIGN
PROBLEM
7
ORGANIZATIONS
CITIZEN
SENSOR
COMMUNITIES
Awareness
Q1. Who to
engage
first?
Org. Actor
Q2. What are
resource needs &
availabilities?
Org. Actor
8. @hemant_pt
Research Questions
— Can general theories of offline conversation be
applied in the online context?
— Can we model intentions to inform organizational
tasks using knowledge-guided features?
— Can we find reliable groups to engage by modeling
collective group divergence using content-based
measure?
8
9. @hemant_pt
Thesis: Statement
Prior knowledge, and
interplay of features of users, their content, and network
efficiently model
Intent & Engagement
for cooperation of citizen sensor communities.
Scope of Concepts
• Intent: aim of action, e.g., offering help
• Engagement: involvement in activity, e.g., participating in discussion
9
10. @hemant_pt
Contributions
1. Operationalized computing in cooperative system design
— by accommodating articulation in Intent Mining, and
— enriching awareness by Engagement Modeling
2. Improved computation of online social data
— by incorporating features from offline social theoretical knowledge
3. Improved performance of intent classification
— by fusing top-down & bottom-up data representations
4. Improved explanation of group engagement
— by modeling content divergence to complement existing structural measures
10
12. @hemant_pt
Outline
— Citizen Sensor Communities & Organizations
— Cooperative System Design Challenges
— Awareness: tackle via Engagement Modeling
— Articulation: tackle via Intent Mining
— Contributions
— Problem 1. Conversation Classification using Offline Theories
— Problem 2. Intent Classification
— Problem 3. Engagement Modeling
— Applications
— Limitations & Future Work
12
13. @hemant_pt
User1. Analyzing #Conversations on Twitter. Using platform provided
functions #REPLY, #RT, and #Mention.
..
…
……..
User2. I kinda feel one might need more than just the platform fn -- @User1 u
can think #Psycholinguistics, dude!
Problem 1. Conversation Classification
— Function of Reply, Retweet, Mention reflect conversation
13
R1. Can general theories of conversation be applied in the online context?
14. @hemant_pt
Problem 1. Conversation Classification
— Function of Reply, Retweet, Mention reflect conversation
— Task: Given a set S of messages mi, Classify a sample {mi}
for {RP, None}, {RT, None}, {MN, None} , where
— Ground-truth corpuses
— RP = { mi | has_Reply_function (mi) = True }
— RT = { mi | has_Retweet_function (mi) = True }
— MN = { mi | has_Mention_function (mi) = True }
— None = S – {RP, RT, MN}
— Sample {mi} size = 3, based on average Reply conversation size
14
15. @hemant_pt
Conversation Classification: Offline
Theories
— Psycholinguistics Indicators [Clark & Gibbs, 1986, Chafe 1987, etc.]
— Determiners (‘the’ vs. ‘a/an’)
— Dialogue Management (e.g., ‘thanks’, ’anyway’), etc.
— Drawback
— Offline analysis focused on positive conversation instances
— Hypotheses
— Offline theoretic features are discriminative
— Such features correlate with information density
15
16. @hemant_pt
Conversation Classification: Feature
Examples
16
CATEGORY Hj Hj SET
H1 - Determiners (the)
H3 - Subject pronouns (she, he, we, they)
H9 - Dialogue management indicators (thanks, yes, ok, sorry, hi, hello, bye,
anyway, how about, so, what do you
mean, please, {could, would, should,
can, will} followed by pronoun)
H11 - Hedge words (kinda, sorta)
• Feature_Hj (mi) = term-frequency ( Hj-set, mi )
• Normalized
• Total 14 feature categories
17. @hemant_pt
Conversation Classification: Results
— Dataset
— Tweets from 3 Disasters, and 3 Non-Disaster events
— Varying set size (3.8K – 609K), time periods
— Classifier:
— Decision Tree
— Evaluation: 10-fold Cross Validation
— Accuracy: 62% - 78% [Lowest for {Mention,None} ]
— AUC range: 0.63 - 0.84
17
Purohit,
Hampton,
Shalin,
Sheth
&
Flach.
In
Journal
of
Computers
in
Human
Behavior,
2013
18. @hemant_pt
Conversation Classification:
Discriminative Features
— Consistent top features across classifiers
— Pronouns (e.g., you, he)
— Dialogue management (e.g., thanks)
— Determiners (e.g., the)
— Word counts
— Positively correlated with RP, RT, MN
— Correlation Coefficient up to 0.69
18
19. @hemant_pt
Conversation Classification:
Psycholinguistic Analysis
— LIWC: Tool for deeper content analysis [Pennebaker, 2001]
— Gives a measure per psychological category
— Categories of interest
— Social Interaction
— Sensed Experience
— Communication
— Analyzed output sets in confusion matrices
Ø Higher values for positive classified conversation
Ø suggests higher information for cooperative intent
19
Purohit,
Hampton,
Shalin,
Sheth
&
Flach.
In
Journal
of
Computers
in
Human
Behavior,
2013
True
Positive
False
Negative
False
Positive
True
Negative
20. @hemant_pt
Conversation Classification:
Lessons
1. Offline theoretic features of conversations exist in the
online environment
Ø Can be applied for computing social data
2. Such features correlate with information density in content
- Reflection of conversation for an intent
20
21. @hemant_pt
Outline
— Citizen Sensor Communities & Organizations
— Cooperative System Design Challenges
— Awareness: tackle via Engagement Modeling
— Articulation: tackle via Intent Mining
— Contributions
— Problem 1. Conversation Classification using Offline Theories
— Problem 2. Intent Classification
— Problem 3. Engagement Modeling
— Applications
— Limitations & Future Work
21
22. @hemant_pt
Thesis: Statement
Prior knowledge, and
interplay of features of users, their content, and network
efficiently model
Intent & Engagement
for cooperation of citizen sensor communities.
22
23. @hemant_pt
Short-text Document Intent
— Intent: Aim of action
DOCUMENT
INTENT
Text
REDCROSS
to
90999
to
donate
10$
to
help
the
victims
of
hurricane
sandy
SEEKING HELP
Anyone know where the nearest #RedCross is? I wanna
give blood today to help the victims of hurricane Sandy
OFFERING HELP
Would like to urge all citizens to make the proper
preparations for Hurricane #Sandy - prep is key - http://
t.co/LyCSprbk has valuable info!
ADVISING
23
24. @hemant_pt
Short-text Document Intent
— Intent: Aim of action
DOCUMENT
INTENT
Text
REDCROSS
to
90999
to
donate
10$
to
help
the
victims
of
hurricane
sandy
SEEKING HELP
Anyone know where the nearest #RedCross is? I wanna
give blood today to help the victims of hurricane Sandy
OFFERING HELP
Would like to urge all citizens to make the proper
preparations for Hurricane #Sandy - prep is key - http://
t.co/LyCSprbk has valuable info!
ADVISING
24
How to identify relevant intent from ambiguous, unconstrained
natural language text?
Relevant intent è Articulation of organizational tasks
(e.g., Seeking vs. Offering resources)
25. @hemant_pt
Intent Classification: Problem
Formulation
— Given a set of user-generated text documents, identify
existing intents
— Variety of interpretations
— Problem statement: a multi-class classification task
approximate f: S ! C , where
C = {c1, c2 … cK}
is a set of predefined K intent classes, and
S = {m1, m2 … mN}
is a set of N short text documents
Focus - Cooperation-assistive intent classes, C= {Seeking, Offering, None}
25
26. @hemant_pt
Intent Classification: Related Work
TEXT CLASSIFICATION
TYPE
FOCUS EXAMPLE
Topic predominant
subject matter
sports or entertainment
Sentiment/Emotion/
Opinion
focus on present state
of emotional affairs
negative or positive;
happy emotion
Intent Focus on action, hence,
future state of affairs
offer to help after floods
e.g., I am going to watch the awesome Fast and Furious movie!! #Excited
26
27. @hemant_pt
Intent Classification: Related Work
DATA TYPE APPROACH FOCUS LIMITED APPLICABILITY
27
Formal text on
Webpages/blogs
(Kröll and Strohmaier 2009, -15;
Raslan et al. 2013, -14)
Knowledge
Acquisition:
via Rules, Clustering
• Lack of large corpora with
proper grammatical structure
• Poor quality text hard to parse
for dependencies
Commercial Reviews,
marketplace
(Hollerit et al. 2013, Wu et al. 2011,
Ramanand et al. 2010, Carlos &
Yalamanchi 2012, Nagarajan et al.
2009)
Classification:
via Rules, Lexical
template based,
Pattern
• More generalized intents
(e.g., ‘help’ broader than ‘sell’)
• Patterns implicit to capture than
for buying/selling
Search Queries
(Broder 2002, Downey et al. 2008,,
Case 2012, Wu et al. 2010,
Strohmaier & Kröll 2012)
User Profiling:
Query Classification
• Lack of large query logs, click
graphs
• Existence of social conversation
28. @hemant_pt
Intent Classification: Challenges
— Unconstrained Natural Language in small space
— Ambiguity in interpretation
— Sparsity of low ‘signal-to-noise’: Imbalanced classes
— 1% signals (Seeking/Offering) in 4.9 million tweets #Sandy
— Hard-to-predict problem:
— commercial intent, F-1 score 65% on Twitter [Hollerit et al. 2013]
@Zuora wants to help @Network4Good with Hurricane Relief. Text SANDY to
80888 & donate $10 to @redcross @AmeriCares & @SalvationArmyUS #help
*Blue: offering intent, *Red: seeking intent
28
29. @hemant_pt
Intent Classification: Types & Features
29
Intent
Binary
Crisis Domain:
- [Varga et al. 2013] Problem vs. Aid (Japanese)
- Features: Syntactic, Noun-Verb templates, etc.
Commercial Domain:
- [Hollerit et al. 2013] Buy vs. Sell intent
- Features: N-grams, Part-of-Speech
Multiclass
Commercial Domain:
- Not on Twitter
31. @hemant_pt
Intent Classification Top-Down:
Binary Classifier - Prior Knowledge
— Conceptual Dependency Theory [Schank, 1972]
— Make meaning independent from the actual words in input
— e.g., Class in an Ontology abstracts similar instances
— Verb Lexicon [Hollerit et al. 2013]
— Relevant Levin’s Verb categories [Levin, 1993]
— e.g., give, send, etc.
— Syntactic Pattern
— Auxiliary & modals: e.g., ‘be’, ‘do’, ‘could’, etc. [Ramanand et al. 2010]
— Word order: Verb-Subject positions, etc.
Purohit,
Hampton,
Bhatt,
Shalin,
Sheth
&
Flach.
In
Journal
of
CSCW,
2014
31
41. @hemant_pt
Intent Classification Hybrid:
Multiclass Classifier – Feature Creation
1. (T) Bag of Tokens -
2. (DK) Declarative Knowledge Patterns
— Domain expert guidance
— Psycholinguistics syntactic & semantic rules
— Expand by WordNet and Levin Verbs
e.g.,
3. (SK) Social Knowledge Indicators
— Offline conversation indicators studied in Problem 1
e.g., Hj = Dialogue Management, Hj-set = {Thanks, anyway,..}
41
(how = yes) ^ (Modal-Set 'can' = yes) ^ (Pronouns except 'you' = yes) ^ (Levin Verb-Set 'give' = yes)
Feature_Hj (mi) = term-frequency ( Hj-set, mi )
Pj = Feature_Pj (mi) = 1 if Pj exists in mi , else 0
TOKENIZER(mi , min, max)
42. @hemant_pt
Intent Classification Hybrid:
Multiclass Classifier - Feature Creation
4. (CTK) Contrast Knowledge Patterns
INPUT: corpus {mi} cleaned and abstracted, min. support, X
For each class Cj
— Find contrasting pattern using sequential pattern mining
OUTPUT: contrast patterns set {P} for each class Cj
5. (CPK) Contrast Patterns: on Part-of-Speech tags of {mi}
42
e.g., unique sequential patterns:
SEEKING: help .* victim .* _url_ .*
OFFERING: anyon .* know .* cloth .*
43. @hemant_pt
Intent Classification Hybrid:
Multiclass Classifier - Feature Creation
Finding CTK: Contrast Knowledge Patterns
For each class Cj
1. Tokenize the cleaned, abstracted text of {mi }
2. Mine Sequential Patterns: SPADE Algorithm
— - Output: sequences of token sets, {P’}
3. Reduce to minimal sequences {P}
4. Compute growth rate & contrast strength for P with all other Ck
5. Top-K ranked {P} by contrast strength
OUTPUT: contrast patterns set {P} for each class Cj
43
gr(P,Cj,Ck) = support (P,Cj) / support (P,Ck) .. (1)
Contrast-Growth (P,Cj,Ck) = 1/(|Cj| -1) ΣCk, k=/=j gr(P,Cj,Ck)/ (1 + gr(P,Cj,Ck)) ..(2)
Contrast-Strength(P,Cj) = support(P,Cj)*Contrast-Growth(P,Cj,Ck) .. (3)
44. @hemant_pt
CORPUS
Set of
short text
documents,
S
FEATURES
Knowledge-driven
features
XT
, y
M_1
M_2
M_K
.
.
.
Subset Xj
T ⊂ S such that, Xj
T includes
all the labeled instances of class Cj for
model M_j
Binarization Frameworks for
Multiclass Classifier: 1 vs. All
P(c2)
P(c1)
X1
T, y1
X2
T, y2
XK
T, yK
P(cK)
44(In 1 vs. 1 framework: K*(K-1)/2 classifiers, for each Cj,Ck pair)
45. @hemant_pt
Intent Classification Hybrid:
Multiclass Classifier - Experiments
— Datasets
— Dataset-1: Hurricane Sandy, Oct 27 – Nov 7, 2012
— Dataset-2: Philippines Typhoon, Nov 7 – Nov 17, 2013
— Parameters
— Base Learner M_j: Random Forest, 10 trees with 100 features
— bi-, tri-gram for (T)
— K=100% & min. support 10% for CTK, 50% for CPK
45
48. @hemant_pt
Lessons
1. Top-down & Bottom-up hybrid approach improves data
representation for learning (complementary) intent classes
— Top 1% discriminative features contained 50% knowledge driven
2. Offline theoretic social conversation (SK) features (the, thanks,
etc.), often removed for text classification are valuable for
intent.
3. There is a varying effect of knowledge types (SK vs. DK vs.
CTK/CPK) in different types of real world event datasets
Ø Culturally-sensitive psycholinguistics knowledge in future
48
49. @hemant_pt
Outline
— Citizen Sensor Communities & Organizations
— Cooperative System Design Challenges
— Awareness: tackle via Engagement Modeling
— Articulation: tackle via Intent Mining
— Contributions
— Problem 1. Conversation Classification using Offline Theories
— Problem 2. Intent Classification
— Problem 3. Engagement Modeling
— Applications
— Limitations & Future Work
49
50. @hemant_pt
Thesis: Statement
Prior knowledge, and
interplay of features of users, their content, and network
efficiently model
Intent & Engagement
for cooperation of citizen sensor communities.
50
51. @hemant_pt
— Engagement: degree of involvement in discussion
— Reliable groups: stay focused and collectively behave to diverge on
topics
Problem 3. Group Engagement Model
51Purohit, Ruan, Fuhry, Parthasarathy, & Sheth. ICWSM 2014
How can organizations find reliable groups to engage for action?
52. @hemant_pt
— Engagement: degree of involvement in discussion
— Reliable groups: stay focused and collectively behave to diverge on topics
— Why & How do groups collectively evolve over time?
1. Define a group from interaction network, g
2. Define Divergence of g: content based in contrast to structure
3. Predict change in the divergence between time slices
— Features of g based on theories of social identity, & cohesion
Problem 3. Group Engagement Model
52Purohit, Ruan, Fuhry, Parthasarathy, & Sheth. ICWSM 2014
53. @hemant_pt
Group Engagement Model:
Integrated Approach Unlike Prior Work
People (User): Participant
of the discussion
Content (Text): Topic of
Interest
Network (Community):
Group around topic
AND
AND
Sources: tupper-lake.com/.../uploads/Community.jpg
http://www.iconarchive.com/show/people-icons-by-aha-soft/user-icon.html
KEY POINT: capture
User Node Diversity
53
54. @hemant_pt
— Candidate Group: Detect in interaction network
— Group Discussion Divergence: Jenson-Shannon Divergence of topic
distribution on group members’ tweets
Group Engagement Model: Discussion
Divergence
where, H(*) = Shannon Entropy
Bt = Latent topic distribution of each tweet t in all members’ tweets |Tg| ,
Bg = mean topic distribution of group g, such that:
54
55. @hemant_pt
Lessons
1. Content Divergence based measure helps explanation of
why groups collectively diverge
— Less diverging group write more social & future action related
content
2. Emerging events such as disasters have higher correlation
with social identity-driven features
Ø Role of social context
55
56. @hemant_pt
Outline
— Citizen Sensor Communities & Organizations
— Cooperative System Design Challenges
— Awareness: tackle via Engagement Modeling
— Articulation: tackle via Intent Mining
— Contributions
— Problem 1. Conversation Classification using Offline Theories
— Problem 2. Intent Classification
— Problem 3. Engagement Modeling
— Applications
— Limitations & Future Work
56
57. @hemant_pt
DISASTER Event
Application-1: Filter Content for
Disaster Response
CITIZEN
Sensors
RESPONSE
Organizations
Me
and
@CeceVancePR
are
coordinating
a
clothing/
food
drive
for
families
affected
by
Hurricane
Sandy.
If
you
would
like
to
donate,
DM
us
Does
anyone
know
how
to
donate
clothes
to
hurricane
#Sandy
victims?
[SEEKING
[OFFERING
Intent-Classifiers
as a Service
57
61. @hemant_pt
Articulation
ENGAGEMENT MODELING INTENT MINING
COOPERATIVE
SYSTEM
61
ORGANIZATIONS
CITIZEN
SENSOR
COMMUNITIES
Awareness
Q1. Who to
engage
first?
Org. Actor
Q2. What are
Resource needs &
availabilities?
Org. Actor
62. @hemant_pt
Limitations & Future Work
— Cooperative System
— CSCW Application specific to domain of crisis
Ø How to create a full What-Where-When-Who knowledge base
— Intent Mining
— Non-cooperation assistive intent classes not considered, as well as
the temporal drift of intent not considered
Ø How to mine actor-level intent beyond document level
— Group Engagement
— Reliable prioritized groups based on Correlation, not Causality
— Interplay of Offline and Online interactions beyond the scope
Ø How to incorporate intent in the group divergence
— Bipartite Intent Graph Matching
— Reducing time complexity of Seeking vs. Offering matching
62
63. @hemant_pt
Conclusion
Prior knowledge, and
interplay of features of users, their content, and network
efficiently model
Intent & Engagement
for cooperation between citizen sensors and organizations in
the online social communities.
63
64. @hemant_pt
Thanks to the Committee Members
64
[Left to Right] Prof. Amit Sheth, (advisor, WSU), Prof. Guozhu Dong (WSU), Prof. Srinivasan
Parthasarathy (OSU), Prof. TK Prasad (WSU), Dr. Patrick Meier (QCRI), Prof. Valerie Shalin (WSU)
Computer Science Social Science
65. @hemant_pt
Acknowledgement,
Thanks and Questions J
— NSF SoCS grant IIS-1111182 to support this work
— Interdisciplinary Mentors especially Prof. John Flach (WSU), Drs. Carlos
Castillo (QCRI), Fernando Diaz (Microsoft), Meena Nagarajan (IBM)
— Kno.e.sis team especially Andrew Hampton from Psychology dept. and
Shreyansh and Tanvi from CSE at Wright State, as well as Yiye Ruan (now
Google) & David Fuhry at the Data Mining Lab, Ohio State University
— Colleagues: Digital Volunteers from the CrisisMappers network, StandBy Task
Force, InCrisisRelief.org, info4Disasters, Humanity Road, Ushahidi, etc. and
the subject matter experts at UN FPA
65
66. @hemant_pt
Ambiguity
Sparsity
Diversity
Scalability
• Mutual Influence in Sparse
Friendship Network
[AAAI ICWSM’12]
• User Summarization with
Sparse Profile Metadata
[ASE SocialInfo’12]
• Matching intent as task of
Information Retrieval [FM’14]
• Knowledge-aware Bi-partite
Matching [In preparation]
• Short-Text Document Intent
Mining [FM’14, JCSCW’14]
• Actor-Intent Mining
Complexity [In preparation]
• Modeling Group Using
Diverse Social Identity &
Cohesion [AAAI ICWSM’14]
• Modeling Diverse User-
Engagement [SOME WWW’11,
ACM WebSci’12]
(Interpretation)
(users)
(behaviors)
66
Other
works