Amit Sheth

Last updated
Amit Sheth
Born
Gujarat, India
Alma mater Ohio State University, Birla Institute of Technology and Science
Occupation Professor at University of South Carolina
TitleFounding Director of Artificial Intelligence Institute
Website Amit Sheth

Amit Sheth is a computer scientist at University of South Carolina in Columbia, South Carolina. He is the founding Director of the Artificial Intelligence Institute, and a professor of Computer Science and Engineering. [1] From 2007 to June 2019, he was the Lexis Nexis Ohio Eminent Scholar, director of the Ohio Center of Excellence in Knowledge-enabled Computing, [2] and a professor of Computer Science at Wright State University. Sheth's work has been cited by over 48,800 publications. [3] He has an h-index of 117, [3] which puts him among the top 100 computer scientists [4] with the highest h-index. [5] Prior to founding the Kno.e.sis Center, he served as the director of the Large Scale Distributed Information Systems Lab at the University of Georgia in Athens, Georgia.

Contents

Education

Sheth received his bachelor's in engineering from the Birla Institute of Technology and Science in computer science in 1981. He received his M.S. and Ph.D. in computer science from Ohio State University in 1983 and 1985, respectively. [6] [7]

Research

Semantic interoperability/integration and semantic web

Sheth has investigated, demonstrated, and advocated for the comprehensive use of metadata. He explored syntactical, structural, and semantic metadata; recently, he has pioneered ontology-driven approaches to metadata extraction and semantic analytics. He was among the first researchers to utilize description logic-based ontologies for schema and information integration (a decade before W3C adopted a DL-based ontology representation standard), and he was the first to deliver a keynote about Semantic Web applications in search. [8] [9] His work on multi-ontology query processing includes the most cited paper on the topic (over 930 citations [10] ). In 1996, he introduced the powerful concept of Metadata Reference Link (MREF) for associating metadata to hypertext that links documents on the Web and described an RDF-based realization in 1998, before RDF was adopted as a W3C recommendation. Part of his recent work has focussed on information extraction from text to generate semantic metadata in the form of RDF. In his work, semantic metadata extracted from biological text is made up of complex knowledge structures (complex entities and relationships) that reflect complex interactions in biomedical knowledge. [11] Sheth proposed a realization of Vannevar Bush's MEMEX vision as the Relationship Web, [12] based on the semantic metadata extracted from text. Sheth and his co-inventors were awarded the first known patent for commercial Semantic Web applications in browsing, searching, profiling, personalization, and advertising, [13] which led to his founding of the first Semantic Search company, Taalee.

In 1992, he gave an influential keynote titled "So far (schematically) yet so near (semantically)", which attested to the need for domain-specific semantics, the use of ontological representation for richer semantic modeling/knowledge representation, and the use of context when looking for similarity between objects. His work on using ontologies for information processing encompassed the approach for searching for an ontology-automated [14] reasoning for schema integration, semantic search, other applications, and semantic query processing. The latter involved query transformations using different ontologies for user queries and resources and federated queries—a concept with associated measures and techniques for computing information loss when traversing taxonomic relationships. [15] [16]

Workflow management and semantic web services

In the early 1990s, he initiated research in the formal modeling, scheduling, and correctness of workflows. His METEOR project demonstrated the value of research with real-world applications; its tools were used in graduate courses in several countries, and its technology was licensed to create a commercial product and was followed up by METEOR-S. He led the research (later joined by IBM) that resulted in the W3C submission of WSDL-S (Semantic Annotation of WSDL), the basis for SAWSDL, a W3C recommendation for adding semantics to WSDL and XML Schema.

For both SAWSDL and SA-REST, he provided leadership in the community-based process followed by the W3C. He coauthored a 1995 paper in the Journal of Distributed and Parallel Databases, which is one of the most cited papers in the area of workflow management literature, with more than 2,330 citations, as well as the most cited among over 430 papers published in that journal. [17] His key technical areas of contribution in workflow management include adaptive workflow management, [18] exception handling, [19] authorization and access control, [20] security, optimization, and quality of service. [21]

Information integration, database interoperability/integration, and database federations

In the 1980s, large organizations wanted to couple multiple autonomous databases to accomplish certain tasks, but how this could be accomplished from a technical perspective wasn't understood. Starting in 1987, Sheth gave a number of tutorials at ICDE, VLDB, SIGMOD, and other major conferences in the area of distributed (federated) data management and developed scientific foundations and architectural principles to address these issues of database interoperability. He developed a clean reference architecture, covered in his most cited paper on federated databases. [22] It provides an architecture consisting of a range of tightly (i.e., global as a view) to loosely coupled (i.e., local as a view) alternatives for dealing with three dimensions: distribution, heterogeneity, and autonomy. Later, he led the development of a schema integration tool in the USA. [23]

Sheth analyzed the limitations resulting from the autonomy of the individual databases and worked towards deep integration by developing specification models for interdatabase dependencies, allowing for a limited degree of coupling to ensure global consistency for critical applications. [24] Together with Dimitrios Georgakopoulos and Marek Rusinkiewicz, he developed the ticketing method for concurrency control of global transactions that need to see and preserve a consistent state across multiple databases. [25] This work, which was recognized with a best paper award at the 1991 International Conference on Data Engineering Conference, was awarded a patent and resulted in progress on multidatabase transactions by other researchers.

His work continued in the areas of the integration and interoperability of networked databases in enterprises to Web-based database access. [26] [27] He has also helped to characterize metadata and develop the techniques that extract and use metadata for integrated access to a variety of content, ranging from databases to multimedia/multimodal data. [28] [29] [30]

Richer relationship identification on linked open data

Sheth has been a strong proponent of identifying a richer and broader set of relationships, such as meronomy and causality, on the Semantic Web. His idea of a "relationship web" [31] is inspired from the vision of memex given by Vannevar Bush. Since the inception of linked data he emphasized the utilization of schema knowledge and the information present on the Web and in linked data for this purpose. These ideas led to a system called BLOOMS [32] for the identification of schema-level relationships between datasets belonging to linked data. Another related system called PLATO allowed for the identification of partonomical relationship between entities on linked data.

Semantics and knowledge-empowered information extraction/NLP/ML, search, browsing, and analysis

In 1993, he initiated InfoHarness, a system that extracted metadata from diverse content (news, software code, and requirements documents) using a Mozilla browser-based faceted search. [33] This system transitioned into a product by Bellcore in 1995 and was followed by a metadata-based search engine for a personal, electronic program guide and Web-based videos for a cable set-top box. [34] He licensed this technology he developed at the University of Georgia for his company Taalee in the same year that Tim-Berners Lee coined the term Semantic Web. In the first keynote on Semantic Web given anywhere, [35] Sheth presented Taalee's commercial implementation of a semantic search engine, which is covered the patent "System and method for creating a semantic web and its applications in browsing, searching, profiling, personalization and advertising".

This 1999–2001 incarnation of semantic search (as described in the patent document) started with extensive tooling to create an ontology/WorldModel (today's knowledge graph) to design a schema and then automatically extract information (through knowledge extraction agents) and incorporate knowledge from multiple high-quality sources to populate the ontology and keep it fresh. This involves machinery for disambiguation to identify what is new and what has changed.

Then the data extraction agents which supported diverse content either pulled (crawled) or pushed (e.g., syndicated news in NewsML), called upon a nine-classifier committee (using bayesian, HMM, and knowledge-based classifiers) to determine the domains of the content, identify the relevant subset of the ontology to use, and perform semantic annotation. "Semantic Enhancement Engine: A Modular Document Enhancement Platform for Semantic Applications over Heterogeneous Content" is one of the earliest publications demonstrating the unusual effectiveness of knowledge-based classifiers compared with more traditional ML techniques. [36] The third component of the system utilized ontology and metadata (annotation) to support semantic search, browsing, profiling, personalization, and advertising.

This system also supported a dynamically generated "Rich Media Reference" (a.k.a. Google's Infobox) which not only displayed metadata about the searched entity pulled from the ontology and metabase but also provided what was termed "blended semantic browsing and querying". [37] He also led efforts in other forms/modality of data, including social and sensor data. He coined the term "Semantic Sensor Web" and initiated and chaired [38] [39] the W3C effort on Semantic Sensor Networking that resulted in a de facto standard. [40] He introduced the concept of semantic perception to reflect the process of converting massive amounts of IoT data into higher level abstractions to support human cognition and perception in decision making, which involves an IntellegO ontology-enabled abductive and deductive reasoning framework for iterative hypothesis refinement and validation. [41]

Real-time scalable social media analytics

In early 2009 he initiated and framed the issue of social media analysis in a broad set of semantic dimensions he called "Spatio-Temporal-Thematic" (STT). He emphasised the analysis of social data from the perspective of people, content, sentiment analysis and emotions. This idea led to a system called Twitris, [42] which employs dynamically evolving semantic models [43] produced by the Semantic Web project Doozer [44] for this purpose. Twitris system can identify people's emotions (such as: joy, sadness, anger, fear, etc.) from their social media posts [45] by applying machine learning techniques with millions of self-labeled emotion tweets. [46]

Entrepreneurship

Sheth founded Infocosm, Inc. in 1997, which licensed and commercialized the METEOR technology from the research he led at the University of Georgia, resulting in distributed workflow management products, WebWork [47] and ORBWork. [48] He founded Taalee, Inc. in 1999 based on licensing VideoAnywhere technology [49] based on the research he led at the University of Georgia. The first product from Taalee was a semantic search engine. [50] [51] [52] Taalee became Voquette [53] after merger in 2002, and then Semagix in 2004. [54] In 2016, Cognovi Labs was founded based on the Twirtis technology [55] resulting from the research he led at the Kno.e.sis Center of the Wright State University. [56] He also served as its chief innovator and serves on the board. The technology was successfully used to predict Brexit [57] and the US 2016 presidential election. [58]

Awards

Related Research Articles

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.

The semantic spectrum, sometimes referred to as the ontology spectrum, the smart data continuum, or semantic precision, is a series of increasingly precise or rather semantically expressive definitions for data elements in knowledge representations, especially for machine use.

<span class="mw-page-title-main">Semantic technology</span> Technology to help machines understand data

The ultimate goal of semantic technology is to help machines understand data. To enable the encoding of semantics with the data, well-known technologies are RDF and OWL. These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources.

A semantic web service, like conventional web services, is the server end of a client–server system for machine-to-machine interaction via the World Wide Web. Semantic services are a component of the semantic web because they use markup which makes data machine-readable in a detailed and sophisticated way.

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<span class="mw-page-title-main">Linked data</span> Structured data and method for its publication

In computing, linked data is structured data which is interlinked with other data so it becomes more useful through semantic queries. It builds upon standard Web technologies such as HTTP, RDF and URIs, but rather than using them to serve web pages only for human readers, it extends them to share information in a way that can be read automatically by computers. Part of the vision of linked data is for the Internet to become a global database.

Ontology-based data integration involves the use of one or more ontologies to effectively combine data or information from multiple heterogeneous sources. It is one of the multiple data integration approaches and may be classified as Global-As-View (GAV). The effectiveness of ontology‑based data integration is closely tied to the consistency and expressivity of the ontology used in the integration process.

The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related while mapping refers to the transformations between the objects. For example, in the two schemas DB1.Student and DB2.Grad-Student ; possible matches would be: DB1.Student ≈ DB2.Grad-Student; DB1.SSN = DB2.ID etc. and possible transformations or mappings would be: DB1.Marks to DB2.Grades.

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References

  1. ↑ University of South Carolina. "USC to open "smart data" artificial intelligence institute" . Retrieved 2020-12-20.
  2. ↑ Wright State University. "Amit Sheth, Ph.D. is the Director of Kno.e.sis" . Retrieved 2020-12-21.
  3. 1 2 "Amit Sheth - Google Scholar Citations" . Retrieved 10 May 2024.
  4. ↑ "Buide2Research - Top Scientists by H-Index - Computer Science and Electronics" (PDF). Retrieved 28 March 2018.
  5. ↑ "The h Index for Computer Science" . Retrieved 22 September 2015.
  6. ↑ Alumni US. "Ohio State University Alumni, 1985" . Retrieved 2021-01-17.
  7. ↑ OhioLINK - Electronic Theses & Dissertations Center (1985). Amit Sheth, Dissertation Record, OhioLINK (Thesis). The Ohio State University. Retrieved 2021-01-17.
  8. ↑ Amit P. Sheth (2009-11-26). "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges" . Retrieved 2015-01-04.
  9. ↑ Amit P. Sheth (2010-04-29). "Content Management, Metadata and Semantic Web" . Retrieved 2015-01-04.
  10. ↑ Mena, Eduardo; Illarramendi, Arantza; Kashyap, Vipul; Sheth, Amit P. (2000). "Google Scholar Citations - OBSERVER: An approach for query processing in global information systems based on interoperation across pre-existing ontologies". Distributed and Parallel Databases. 8 (2): 223–271. doi:10.1023/A:1008741824956 . Retrieved 20 June 2017.
  11. ↑ Cartic Ramakrishnan; K. J. Kochut; Amit P. Sheth (2006). "A Framework for Schema-Driven Relationship Discovery from Unstructured Text". The Semantic Web - ISWC 2006: 5th International Semantic Web Conference, ISWC 2006, Athens, GA, USA, November 5-9, 2006, Proceedings. Lecture Notes in Computer Science (LNCS). Vol. 4273. pp. 583–596. doi:10.1007/11926078_42. ISBN   978-3-540-49029-6.
  12. ↑ Amit P. Sheth; Cartic Ramakrishnan (2007). "Relationship Web: Blazing Semantic Trails between Web Resources". IEEE Internet Computing. 11 (4): 77–81. doi:10.1109/MIC.2007.91. S2CID   3008622.
  13. ↑ Amit P. Sheth; David Avant; Clemens Bertram. "System and method for creating a semantic web and its applications in browsing, searching, profiling, personalization and advertising, US 6311194 B1" . Retrieved 2015-01-04.
  14. ↑ Arumugam, Madhan; Sheth, Amit P.; Arpinar, I. Budak (2002). "Towards peer-to-peer semantic web: A distributed environment for sharing semantic knowledge on the web". Kno.e.sis Publications.
  15. ↑ Kashyap, Vipul; Sheth, Amit (1994). "Semantics-based information brokering". Proceedings of the third international conference on Information and knowledge management - CIKM '94. pp. 363–370. CiteSeerX   10.1.1.54.9548 . doi:10.1145/191246.191309. ISBN   978-0897916745. S2CID   296653.
  16. ↑ Mena, Eduardo; Kashyap, Vipul; Sheth, Amit; Illarramendi, Arantza (1996). "Observer: An approach for query processing in global information systems based on interoperation across pre-existing ontologies". Proceedings First IFCIS International Conference on Cooperative Information Systems. pp. 14–25. CiteSeerX   10.1.1.35.2779 . doi:10.1109/COOPIS.1996.554955. ISBN   978-0-8186-7505-8. S2CID   52809082.
  17. ↑ Georgakopoulos, Diimitrios; Hornick, Mark; Sheth, Amit (1995-04-01). "An overview of workflow management: From process modeling to workflow automation infrastructure". Distributed and Parallel Databases. 3 (2): 119–153. CiteSeerX   10.1.1.101.5199 . doi:10.1007/BF01277643. ISSN   0926-8782. S2CID   446699.
  18. ↑ Han, Yanbo; Sheth, Amit; Bussler, Christoph (1998). "A taxonomy of adaptive workflow management". Workshop of the 1998 ACM Conference on Computer Supported Cooperative Work: 1–11.
  19. ↑ Luo, Zongwei; Sheth, Amit; Kochut, Krys; Miller, John (2000). "Exception handling in workflow systems". Applied Intelligence. 13 (2): 125–147. doi:10.1023/a:1008388412284. S2CID   6477503.
  20. ↑ Wu, Shengli; Sheth, Amit; Miller, John; Luo, Zongwei (2002). "Authorization and access control of application data in workflow systems" (PDF). Journal of Intelligent Information Systems. 18 (1): 71–94. doi:10.1023/a:1012972608697. S2CID   7959746.
  21. ↑ Cardoso, Jorge; Sheth, Amit; Miller, John (2003). "Workflow Quality of Service". Enterprise Inter- and Intra-Organizational Integration. IFIP Advances in Information and Communication Technology. Vol. 108. Springer US. pp. 303–311. doi:10.1007/978-0-387-35621-1_31. ISBN   978-1-4757-5151-2.
  22. ↑ Sheth, Amit P.; Larson, James A. (1990). "Federated database systems for managing distributed, heterogeneous, and autonomous databases". ACM Computing Surveys. 22 (3): 183–236. CiteSeerX   10.1.1.381.9176 . doi:10.1145/96602.96604. S2CID   47120670.
  23. ↑ Sheth, Amit P.; Larson, James A.; Cornelio, Aloysius; Navathe, Shamkant B. (1988). "A tool for integrating conceptual schemas and user views". Proceedings. Fourth International Conference on Data Engineering. pp. 176–183. doi:10.1109/ICDE.1988.105459. ISBN   978-0-8186-0827-8. S2CID   291677.
  24. ↑ Rusinkiewicz, Marek; Sheth, Amit; Karabatis, George (1991). "Specifying interdatabase dependencies in a multidatabase environment". Computer. 24 (12): 46–53. doi:10.1109/2.116888. S2CID   17988570.
  25. ↑ Georgakopoulos, Dimitrios; Rusinkiewicz, Marek; Sheth, Amit (1991). "On serializability of multidatabase transactions through forced local conflicts". [1991] Proceedings. Seventh International Conference on Data Engineering. pp. 314–323. doi:10.1109/ICDE.1991.131479. ISBN   978-0-8186-2138-3. S2CID   12690413.
  26. ↑ Sheth, Amit P. (1999). Interoperating geographic information systems. Vol. 495. Springer Netherlands. pp. 5–30. ISBN   9780792384366.
  27. ↑ Ouksel, Aris M.; Sheth, Amit (1999). "Semantic interoperability in global information systems". ACM SIGMOD Record. 28 (1): 5–12. doi:10.1145/309844.309849. S2CID   3351799.
  28. ↑ Klas, Wolfgang; Sheth, Amit (1998). Multimedia Data Management: Using Metadata to Integrate and Aplly Digital Media.
  29. ↑ Kashyap, Vipul; Shah, Kshitij; Sheth, Amit P. (1996). Metadata for Building the MultiMedia Patch Quilt.
  30. ↑ Kashyap, Vipul; Sheth, Amit P. (2006). Information brokering across heterogeneous digital data: a metadata-based approach. Springer Science & Business Media. ISBN   9780306470288.
  31. ↑ Sheth, Amit (2007-09-28). "Relationship web". Workshop on multimedia information retrieval on the many faces of multimedia semantics - MS '07. Dl.acm.org. pp. 1–2. doi:10.1145/1290067.1290068. ISBN   9781595937827. S2CID   33880601.
  32. ↑ "BLOOMS". Wiki.knoesis.org. Retrieved 2013-10-17.
  33. ↑ Shklar, Leon; Sheth, Amit; Kashyap, Vipul; Shah, Kshitij (1995). InfoHarness: Use of automatically generated metadata for search and retrieval of heterogeneous information. Notes on Numerical Fluid Mechanics and Multidisciplinary Design. Vol. 141. pp. 217–230. CiteSeerX   10.1.1.697.7942 . doi:10.1007/3-540-59498-1_248. ISBN   978-3-319-98176-5.{{cite book}}: |journal= ignored (help)
  34. ↑ Sheth, Amit; Bertram, Clemens; Shah, Kshitij (1999). "Video anywhere: a system for searching and managing distributed heterogeneous video assets". ACM SIGMOD Record. 28 (1): 104–114. doi:10.1145/309844.310067. S2CID   14275737.
  35. ↑ Sheth, Amit P. (2000). "Semantic web and information brokering: Opportunities, commercialization, and challenges". Kno.e.sis Publications.
  36. ↑ Hammond, Brian; Sheth, Amit; Kochut, Krzysztof (2002). "Semantic Enhancement Engine: A Modular Document Enhancement Platform for Semantic Applications over Heterogeneous Content". Real World Semantic Web Applications. 92: 29–49. S2CID   5705718.
  37. ↑ Sheth, Amit; Bertram, Clemens; Avant, David; Hammond, Brian; Kochut, Krys; Warke, Yashodhan (2002). "Managing semantic content for the Web". IEEE Internet Computing. 6 (4): 80–87. doi:10.1109/mic.2002.1020330.
  38. ↑ Semantic Sensor Network Incubator Group, World Wide Web Consortium. "Semantic Sensor Network Incubator Group Charter" . Retrieved 2021-01-17.
  39. ↑ Semantic Sensor Network Incubator Group, World Wide Web Consortium. "Semantic Sensor Network XG Final Report, W3C Incubator Group Report 28 June 2011" . Retrieved 2021-01-17.
  40. ↑ Compton, Michael; Barnaghi, Payam; Bermudez, Luis; GarcíA-Castro, RaúL; Corcho, Oscar; Cox, Simon; Graybeal, John; Hauswirth, Manfred; Henson, Cory (2012). "The SSN ontology of the W3C semantic sensor network incubator group". Web Semantics: Science, Services and Agents on the World Wide Web. 17: 25–32. doi: 10.1016/j.websem.2012.05.003 .
  41. ↑ Henson, Cory; Sheth, Amit; Thirunarayan, Krishnaprasad (2012). "Semantic perception: Converting sensory observations to abstractions". IEEE Internet Computing. 16 (2): 26–34. doi:10.1109/mic.2012.20. S2CID   18868397.
  42. ↑ "Twitris". Twitris.knoesis.org. Retrieved 2013-10-17.
  43. ↑ Amit Sheth; Christopher Thomas; Pankaj Mehra (2010). "Continuous Semantics to Analyze Real-Time Data". IEEE Internet Computing. 14 (6): 84–89. doi:10.1109/MIC.2010.137. S2CID   5392093 . Retrieved 2012-09-04.
  44. ↑ "Doozer". Knoesis.org. 2001-05-17. Archived from the original on 2013-10-17. Retrieved 2013-10-17.
  45. ↑ Sheth, Amit; Jadhav, Ashutosh; Kapanipathi, Pavan; Chen, Lu; Purohit, Hemant; Smith, Gary; Wang, Wenbo (2014). Encyclopedia of Social Network Analysis and Minine (PDF). Springer Verlag. ISBN   978-1-4614-6169-2 . Retrieved 21 September 2015.
  46. ↑ Wang, Wenbo; Chen, Lu; Thirunarayan, Krishnaprasad; Sheth, Amit P. (2012). "Harnessing Twitter "Big Data" for Automatic Emotion Identification". 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing. pp. 587–592. doi:10.1109/SocialCom-PASSAT.2012.119. ISBN   978-1-4673-5638-1. S2CID   17120085.
  47. ↑ Miller, John A.; Palaniswami, Devanand; Sheth, Amit P.; Kochut, Krys J.; Singh, Harvinder (1998-03-01). "WebWork: METEOR2's Web-Based Workflow Management System". Journal of Intelligent Information Systems. 10 (2): 185–215. doi:10.1023/A:1008660827609. ISSN   0925-9902. S2CID   20955969.
  48. ↑ Das, S.; Kochut, K.; Miller, John; Sheth, Amit; Worah, D. (1996). "ORBWork: A Reliable Distributed CORBA-based Workflow Enactment System for METEOR2".{{cite journal}}: Cite journal requires |journal= (help)
  49. ↑ Sheth, Amit; Bertram, Clemens; Shah, Kshitij (March 1999). "Video anywhere: a system for searching and managing distributed heterogeneous video assets". ACM SIGMOD Record. 28 (1): 104–114. doi:10.1145/309844.310067. ISSN   0163-5808. S2CID   14275737.
  50. ↑ Townley, John (August 10, 2000). "The Streaming Search Engine That Reads Your Mind". Archived from the original on December 6, 2008. Alt URL
  51. ↑ US 6311194,Sheth, Amit; Avant, David& Bertram, Clemens,"System and method for creating a semantic web and its applications in browsing, searching, profiling, personalization and advertising",published Oct 30, 2001
  52. ↑ Amit Sheth (2009-11-26). "Semantic Web & Information Brokering: Opportunities, Commercializatio…".
  53. ↑ Sheth, A.; Bertram, C.; Avant, D.; Hammond, B.; Kochut, K.; Warke, Y. (July 2002). "Managing semantic content for the Web". IEEE Internet Computing. 6 (4): 80–87. doi:10.1109/MIC.2002.1020330. ISSN   1089-7801.
  54. ↑ Sheth, Amit (August 25, 2005). "Enterprise Applications of Semantic Web: The Sweet Spot of Risk and Compliance". Industrial Applications of Semantic Web. IFIP — the International Federation for Information Processing. Vol. 188. pp. 47–62. doi:10.1007/0-387-29248-9_3. ISBN   0-387-28568-7. S2CID   7941137.{{cite book}}: |website= ignored (help)
  55. ↑ Sheth, A.; Purohit, H.; Smith, G. A.; Brunn, J.; Jadhav, A.; Kapanipathi, P.; Lu, C.; Wang, W. (May 2017). "Twitris: A System for Collective Social Intelligence". Encyclopedia of Social Network Analysis and Mining. Vol. 2nd Edition. pp. 1–23. doi:10.1007/978-1-4614-7163-9_345-1. ISBN   978-1-4614-7163-9.
  56. ↑ "Local start-up hails big investments". daytondailynews. Retrieved 2017-07-06.
  57. ↑ Donovan, Jay (29 June 2016). "The Twitris sentiment analysis tool by Cognovi Labs predicted the Brexit hours earlier than polls | TechCrunch" . Retrieved 2017-07-07.
  58. ↑ "Cognovi Labs: Twitter Analytics Startup Predicts Trump Upset in Real-Time". Archived from the original on 2017-10-17. Retrieved 2017-07-06.
  59. ↑ University of South Carolina. "Amith Sheth Recipient of IEEE Computer Society W. Wallace McDowell Award" . Retrieved 2023-06-13.
  60. ↑ IEEE Computer Society (26 January 2023). "Prof. Amit Sheth Award Receipient" . Retrieved 2023-06-13.
  61. ↑ ACM. "ACM Fellow Award" . Retrieved 2020-12-21.
  62. ↑ University of South Carolina. "All Award Winners" . Retrieved 2021-01-16.
  63. ↑ IEEE. "IEEE TCSVC Awards" . Retrieved 2020-12-21.
  64. ↑ University of South Carolina. "Dr. Sheth wins IEEE TCSVC Research Innovation Award 2020" . Retrieved 2020-12-21.
  65. ↑ Ohio State University (4 October 2019). "College recognizes 22 elite alumni for achievement, service" . Retrieved 2020-12-21.
  66. ↑ AAAS. "AAAS Honors Accomplished Scientists as 2018 Elected Fellows" . Retrieved 2018-11-28.
  67. ↑ Wright State University Newsroom. "Wright State professor Amit Sheth elected a Fellow of American Association for the Advancement of Sciences" . Retrieved 2018-11-28.{{cite web}}: |author= has generic name (help)
  68. ↑ AAAI. "Elected AAAI Fellows - 2018" . Retrieved 2018-10-28.
  69. ↑ Ohio Faculty Council. "Ohio Faculty Council - Technology Commercialization Award" . Retrieved 2018-10-28.
  70. ↑ Wright State University Newsroom. "Citizen Sensing to Insights, Predictions and Decisions" . Retrieved 2018-10-28.{{cite web}}: |author= has generic name (help)
  71. ↑ IEEE. "Fellow Class of 2006". Institute of Electrical and Electronics Engineers (IEEE). Archived from the original on December 14, 2007. Retrieved 2008-05-28.
  72. ↑ WSU. "Faculty Award Winners" . Retrieved 2012-08-06.
  73. ↑ "Amit Sheth homepage" . Retrieved 17 January 2021.
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