Srini Narayanan

DAML-S: Web Service Description for the Semantic Web

In this paper we present DAML-S, a DAML+OIL ontology for describing the properties and capabilities of Web Services. Web Services - Web-accessible programs and devices - are garnering a great deal of interest from industry, and standards are emerging for low-level descriptions of Web Services. DAML-S complements this effort by providing Web Service descriptions at the application layer, describing what a service can do, and not just how it does it. In this paper we describe three aspects of our ontology: the service profile, the process model, and the service grounding. The paper focuses on the grounding, which connects our ontology with low-level XML-based descriptions of Web Services.

Simulation, verification and automated composition of web services

Web services -- Web-accessible programs and devices - are a key application area for the Semantic Web. With the proliferation of Web services and the evolution towards the Semantic Web comes the opportunity to automate various Web services tasks. Our objective is to enable markup and automated reasoning technology to describe, simulate, compose, test, and verify compositions of Web services. We take as our starting point the DAML-S DAML+OIL ontology for describing the capabilities of Web services. We define the semantics for a relevant subset of DAML-S in terms of a first-order logical language. With the semantics in hand, we encode our service descriptions in a Petri Net formalism and provide decision procedures for Web service simulation, verification and composition. We also provide an analysis of the complexity of these tasks under different restrictions to the DAML-S composite services we can describe. Finally, we present an implementation of our analysis techniques. This implementation takes as input a DAML-S description of a Web service, automatically generates a Petri Net and performs the desired analysis. Such a tool has broad applicability both as a back end to existing manual Web service composition tools, and as a stand-alone tool for Web service developers.

Question answering based on semantic structures

The ability to answer complex questions posed in Natural Language depends on (1) the depth of the available semantic representations and (2) the inferential mechanisms they support. In this paper we describe a QA architecture where questions are analyzed and candidate answers generated by 1) identifying predicate argument structures and semantic frames from the input and 2) performing structured probabilistic inference using the extracted relations in the context of a domain and scenario model. A novel aspect of our system is a scalable and expressive representation of actions and events based on Coordinated Probabilistic Relational Models (CPRM). In this paper we report on the ability of the implemented system to perform several forms of probabilistic and temporal inferences to extract answers to complex questions. The results indicate enhanced accuracy over current state-of-the-art Q/A systems.

Spatial and Linguistic Aspects of Visual Imagery in Sentence Comprehension.

There is mounting evidence that language comprehension involves the activation of mental imagery of the content of utterances (Barsalou, 1999; Bergen, Chang, & Narayan, 2004; Bergen, Narayan, & Feldman, 2003; Narayan, Bergen, & Weinberg, 2004; Richardson, Spivey, McRae, & Barsalou, 2003; Stanfield & Zwaan, 2001; Zwaan, Stanfield, & Yaxley, 2002). This imagery can have motor or perceptual content. Three main questions about the process remain under-explored, however. First, are lexical associations with perception or motion sufficient to yield mental simulation, or is the integration of lexical semantics into larger structures, like sentences, necessary? Second, what linguistic elements (e.g., verbs, nouns, etc.) trigger mental simulations? Third, how detailed are the visual simulations that are performed? A series of behavioral experiments address these questions, using a visual object categorization task to investigate whether up- or down-related language selectively interferes with visual processing in the same part of the visual field (following Richardson et al., 2003). The results demonstrate that either subject nouns or main verbs can trigger visual imagery, but only when used in literal sentences about real space-metaphorical language does not yield significant effects-which implies that it is the comprehension of the sentence as a whole and not simply lexical associations that yields imagery effects. These studies also show that the evoked imagery contains detail as to the part of the visual field where the described scene would take place.

Analysis and simulation of Web services

Web services--Web-accessible programs and devices--are a key application area for the Semantic Web. With the proliferation of Web services and the evolution towards the Semantic Web comes the opportunity to automate various Web services tasks. Our objective is to enable markup and automated reasoning technology to describe, simulate, compose, test, and verify compositions of Web services. We take as our starting point the DAML-S DAML + OIL ontology for describing the capabilities of Web services. We define the semantics for a relevant subset of DAML-S in terms of a first-order logical language. With the semantics in hand, we encode our service descriptions in a Petri Net formalism and provide decision procedures for Web service simulation, verification and composition. We also provide an analysis of the complexity of these tasks under different restrictions to the DAML-S composite services we can describe. Finally, we present an implementation of our analysis techniques. This implementation takes as input a DAML-S description of a Web service, automatically generates a Petri Net and performs the desired analysis. Such a tool has broad applicability both as a back end to existing manual Web service composition tools, and as a stand-alone tool for Web service developers.

Learning all optimal policies with multiple criteria

We describe an algorithm for learning in the presence of multiple criteria. Our technique generalizes previous approaches in that it can learn optimal policies for all linear preference assignments over the multiple reward criteria at once. The algorithm can be viewed as an extension to standard reinforcement learning for MDPs where instead of repeatedly backing up maximal expected rewards, we back up the set of expected rewards that are maximal for some set of linear preferences (given by a weight vector, w). We present the algorithm along with a proof of correctness showing that our solution gives the optimal policy for any linear preference function. The solution reduces to the standard value iteration algorithm for a specific weight vector, w.

L 0 —the first five years of an automated language acquisition project

The L0 project at ICSI and UC Berkeley attempts to combine not only vision and natural language modelling, but also learning. The original task was put forward in (Feldman et al. 1990a) as a touchstone task for AI and cognitive science. The task is to build a system that can learn the appropriate fragment of any natural language from sentence-picture pairs. We have not succeeded in building such a system, but we have made considerable progress on component subtasks and this has led in a number of productive and surprising directions.

Putting frames in perspective

This paper attempts to bridge the gap between FrameNet frames and inference. We describe a computational formalism that captures structural relationships among participants in a dynamic scenario. This representation is used to describe the internal structure of FrameNet frames in terms of parameters for event simulations. We apply our formalism to the commerce domain and show how it provides a flexible means of accounting for linguistic perspective and other inferential effects.

Semantic extraction with wide-coverage lexical resources

We report on results of combining graphical modeling techniques with Information Extraction resources (Pattern Dictionary and Lexicon) for both frame and semantic role assignment. Our approach demonstrates the use of two human built knowledge bases (WordNet and FrameNet) for the task of semantic extraction.

Framenet meets the semantic web: lexical semantics for the web

This paper describes FrameNet [9,1,3], an online lexical resource for English based on the principles of frame semantics [5,7,2]. We provide a data category specification for frame semantics and FrameNet annotations in an RDF-based language. More specifically, we provide an RDF markup for lexical units, defined as a relation between a lemma and a semantic frame, and frame-to-frame relations, namely Inheritance and Subframes. The paper includes simple examples of FrameNet annotated sentences in an XML/RDF format that references the project-specific data category specification.