Events for May 03, 2013
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AI Seminar-Rudi Studer:
Fri, May 03, 2013 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Rudi Studer, Institutes AIFB/KSRI, Karlsruhe Institute of Technology & FZI Research Center for Information Technology, Karlsruhe, Germany
Talk Title: A Declarative Language for Interoperation between Web Data and Services
Abstract: In recent years the amount of data that have been published according to Linked Data principles as well as the number of Web APIs that expose data in various formats has been growing rapidly. In order to integrate these two worlds the notion of Linked APIs will be presented that combines principles from Linked Data and Representational State Transfer (REST). The combination provides a uniform resource-centric abstraction, which includes the RDF data format and manipulation mechanisms for the data.
For declaratively specifying interactions with web resources in the context of Linked APIs, Data-Fu is introduced. Data-Fu is a lightweight declarative rule language with state transition systems as formal grounding. An execution engine that supports the parallel execution of the declarative Data-Fu programs is outlined as well. Application examples show the advantages of the developed approach.
Biography: Rudi Studer is Full Professor in Applied Informatics at the Karlsruhe Institute of Technology (KIT), Institute AIFB. In addition, he is director at the Karlsruhe Service Research Institute (KSRI) as well as at the FZI Research Center for Information Technology. His research interests include knowledge management, semantic web technologies and applications, data and text mining, big data and services.
He obtained a Diploma in Computer Science at the University of Stuttgart in 1975. In 1982 he was awarded a Doctor's degree in Mathematics and Informatics at the University of Stuttgart, and in 1985 he obtained his Habilitation in Informatics at the University of Stuttgart. From 1985 to 1989 he was project leader and manager at the Scientific Center of IBM Germany.
He is involved in various national and international cooperation projects, among others the DFG Graduate School Information Management and Market Engineering (IME), the EU Network of Excellence on Large-Scale Data Management (PlanetData) as well as the EU projects XLike (Cross-lingual Knowledge Extraction) and Render (Reflecting Knowledge Diversity). He is former president of the Semantic Web Science Association (SWSA) and former Editor-in-chief of the Journal Web Semantics: Science, Services, and Agents on the World Wide Web.
Host: Craig Knoblock, USC/ISI
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=02b81710252d4e6cb0ac2fe9726e525b1dLocation: Information Science Institute (ISI) - Marina del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=02b81710252d4e6cb0ac2fe9726e525b1d
Audiences: Everyone Is Invited
Contact: Alma Nava / Information Sciences Institute
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar- Dirk Hovy: "Learning Semantic Types and Relations from Text" (Defense Practice Talk)
Fri, May 03, 2013 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Dirk Hovy, USC/ISI
Talk Title: Learning Semantic Types and Relations from Text (Defense Practice Talk)
Series: Natural Language Seminar
Abstract: NLP applications such as Question Answering (QA), Information Extraction (IE), or Machine Translation (MT) are incorporating increasing amounts of semantic information. A fundamental building block of semantic information is the relation between a predicate and its arguments, e.g. eat(John,burger). In order to reason at higher levels of abstraction, it is useful to group relation instances according to the types of their predicates and the types of their arguments. For example, while eat(Mary,burger) and devour(John,tofu) are two distinct relation instances, they share the underlying predicate and argument types INGEST(PERSON,FOOD).
A central question is: where do the types and relations come from?
The subfield of NLP concerned with this is relation extraction, which comprises two main tasks: 1. identifying and extracting relation instances from text 2. determining the types of their predicates and arguments The first task is difficult for several reasons. Relations can express their predicate explicitly or implicitly. Furthermore, their elements can be far part, with unrelated words intervening. In this thesis, we restrict ourselves to relations that are explicitly expressed between syntactically related words. We harvest the relation instances from dependency parses. The second task is the central focus of this thesis. Specifically, we will address these three problems: 1) determining argument types 2) determining predicate types 3) determining argument and predicate types. For each task, we model predicate and argument types as latent variables in a hidden Markov models. Depending on the type system available for each of these tasks, our approaches range from unsupervised to semi-supervised to fully supervised training methods.
The central contributions of this thesis are as follows: 1. Learning argument types (unsupervised): We present a novel approach that learns the type system along with the relation candidates when neither is given. In contrast to previous work on unsupervised relation extraction, it produces human-interpretable types rather than clusters. We also investigate its applicability to downstream tasks such as knowledge base population and construction of ontological structures. An auxiliary contribution, born from the necessity to evaluate the quality of human subjects, is MACE (Multi-Annotator Competence Estimation), a tool that helps estimate both annotator competence and the most likely answer. 2. Learning predicate types (unsupervised and supervised): Relations are ubiquitous in language, and many problems can be modeled as relation problems. We demonstrate this on a common NLP task, word sense disambiguation (WSD) for prepositions (PSD). We use selectional constraints between the preposition and its argument in order to determine the sense of the preposition. In contrast, previous approaches to PSD used n-gram context windows that do not capture the relation structure. We improve supervised state-of-the-art for two type systems. 3. Argument types and predicates types (semi-supervised): Previously, there was no work in jointly learning argument and predicate types because (as with many joint learning tasks) there is no jointly annotated data available. Instead, we have two partially annotated data sets, using two disjoint type systems: one with type annotations for the predicates, and one with type annotations for the arguments. We present a semisupervised approach to jointly learn argument types and predicate types, and demonstrate it for jointly solving PSD and supersense-tagging of their arguments. To the best of our knowledge, we are the first to address this joint learning task. Our work opens up interesting avenues for both the typing of existing large collections of triple stores, using all available information, and for WSD of various word classes.
Biography: Home Page:
http://www.dirkhovy.com/
Host: Qing Dou
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - Marina Del Rey-11th Flr Conf Rm # 1135
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
