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Panel: Semantic Search Beyond RDF – SemTech 2009 Audio

By   /  July 6, 2009  /  No Comments

Wen Ruan, TextWise
Ronald M Kaplan, Powerset division of bing
Christian F. Hempelmann, RiverGlass, Inc.
Riza Berkan, hakia

Semantic Search technology in the Semantic Web community is often understood as retrieval of knowledge from tagged data such as RDF sources, which require substantial formatting and markup to realize. Understanding unstructured query and document text and conducting searches according to their meaning is another approach, exemplified by linguistically rooted semantic matching, ontological knowledge-based semantic interpretation, and statistically based semantic similarity search.

Wen Ruan, TextWise
Ronald M Kaplan, Powerset division of bing
Christian F. Hempelmann, RiverGlass, Inc.
Riza Berkan, hakia

Semantic Search technology in the Semantic Web community is often understood as retrieval of knowledge from tagged data such as RDF sources, which require substantial formatting and markup to realize. Understanding unstructured query and document text and conducting searches according to their meaning is another approach, exemplified by linguistically rooted semantic matching, ontological knowledge-based semantic interpretation, and statistically based semantic similarity search.

This panel will look at different ways to tackle semantic search as a problem of text understanding. Powerset division of bing’s natural-language processing engine does deep syntactical analysis to determine the meaning of a query or a sentence. Hakia relies on a language-independent ontology model and an ontology-based English lexicon to translate text into a representation of its meaning. RiverGlass has developed an ontological semantic approach to search and text analytics, emphasizing the in-context, linguistic meaning of textual content in order to return truly relevant results in response to information requests. TextWise’s Semantic Signature matching looks for similarities between a query and text at the topic level. Each presenter will introduce their respective semantic search technologies and then lead discussions on how these can benefit

Attachment: Panel – Semantic Search Beyond RDF.mp3 (56.8 MB)

Presenters:

Wen Ruan
Wen Ruan
TextWise

Wen Ruan is Chief Science Officer at Textwise, LLC. She has a B.S. in Biomedical Engineering and Instrumentation and a M.S. in Electrical Engineering from Xi’an Jiaotong University (Xi’an, China). She received her Ph.D. in Medical Informatics in 1999 from University of Giessen (Germany). Ruan joined TextWise as the principal researcher on the NIH-funded MEDLINK project, using natural language processing and information extraction techniques to improve search accuracy.

Ruan’s extensive research background in pattern recognition, signal and image processing, standardized medical vocabularies, knowledge representation, information retrieval, natural language processing, machine learning, and statistical analysis has made her a key contributor to the Semantic Signature® technology. She performed a key role in porting the Trainable Semantic Vector technology from its original patent application to the Web for the TextWise online contextual advertising business.

Under Ruan’s direction, the Trainable Semantic Vector patent application space has been extended to search, clustering and classification. She also designed and developed a hybrid search technology offering significant improvements in accuracy and coverage through both semantic and keyword matches. Ruan is currently leading various R&D initiatives to apply Semantic Signature® technology to various vertical business applications.

Riza Berkan
Riza Berkan
hakia

Founder of hakia, Dr. Berkan is a nuclear scientist by training with a specialization in artificial intelligence and fuzzy logic. He is the author of several articles in this area, including the book Fuzzy Systems Design Principles published by IEEE in 1997. Between 1990 and 2000, Dr. Berkan worked as a subcontractor to the US Government and handled multiple Lockheed Martin Energy Systems and Westinghouse projects involving the Y-12 Plant, Savannah River Plant, and Los Alamos National Laboratory. Dr. Berkan holds a B.S. in Physics from Hacettepe University, Turkey and an M.S. and Ph.D. in Nuclear Engineering from the University of Tennessee with a specialization in Artificial Intelligence.

Christian Hempelmann
Christian Hempelmann
RiverGlass, Inc.

Ph.D. in Linguistics from Purdue University most recently, Kiki was Chief Scientific Officer for hakia.com, where he led the development of the semantic core technology and the lexical and ontological resources. His research has been in computational linguist with an interest in semantically informed applications including an emphasis on descriptive methods necessary to capture cognitive processing based on knowledge representation. Another field of applications for Kiki is the semantics of humor, a field in which he published numerous articles, reviews, book chapters, and has given invited talks. He is an adjunct assistant professor of linguistics at Purdue University and his previous appointments include an assistant professorship (tenure-track) for linguistics at Georgia Southern University and a postdoctoral researcher position in computational linguistics and cognitive psychology at the University of Memphis. Currently, Kiki is leading the Ontological-Semantic initiative at RiverGlass.

Ronald Kaplan
Ronald Kaplan
Powerset division of bing

Ronald M. Kaplan is Chief Scientist of Powerset, a natural language search company recently acquired by Microsoft. Prior to joining Powerset, he was a Research Fellow at the (Xerox) Palo Alto Research Center where he created and directed the Natural Language Theory and Technology research group. He is also a Consulting Professor in the Linguistics Department at Stanford University and a Principal of Stanford’s Center for the Study of Language and Information.

He received his Ph.D. in Social Psychology in 1975 from Harvard University, where he investigated how explicit computational models of grammar could be embedded in models of human language performance. He has made many contributions to computational linguistics and linguistic theory. These include the notions of consumer-producer and active-chart parsing, the design of the formal theory of Lexical Functional Grammar and its initial computational implementation, and the mathematical, linguistic, and computational concepts that underlie the use of finite-state phonological and morphological descriptions.

He has provided linguistic technologies for commercial applications other than Powerset. He was Chief Scientist of Microlytics, Inc. a PARC spin-off, and delivered software that was incorporated into products sold by Microsoft, Apple, Hewlett-Packard, Sony and other companies. He served on the Technical Advisory Board of Inxight Software, another PARC spin-off that commercialized his technology.

Kaplan is a past President of the Association for Computational Linguistics, a co-recipient of the 1992 Software System Award of the Association for Computing Machinery, and a Fellow of the ACM. He has also been a Fellow-in-Residence at the Netherlands Institute for Advanced Study in the Humanities and Social Sciences. He holds over 30 patents in computational linguistics and related areas.

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