The utility of formal theories of commonsense reasoning will depend both on their competency in solving problems and on their conceptual coverage. We argue that the problems of coverage and competency can be decoupled and solved with different methods for a given commonsense domain. We describe a methodology for identifying the coverage requirements of theories through the large-scale analysis of planning strategies, with further refinements made by collecting and categorizing instances of natural language expressions pertaining to the domain. We demonstrate the effectiveness of this methodology in identifying the representational coverage requirements of theories of the commonsense psychology of human memory. We then apply traditional methods of formalization to produce a formal first-order theory of commonsense memory with a high degree of competency and coverage.

Abstract — We discuss the challenges involved in adapting the OntoSem natural language processing system to the Web. One set of tasks involves processing Web documents, translating their computed meaning representations from the OntoSem’s native KR language into the Semantic Web language OWL, and publishing the results as Web pages and RSS feeds. Another set of tasks works in reverse – querying the Web for facts needed by OntoSem, translating them from OWL into OntoSem’s native KR language and importing the results. A central problem underlying both sets of tasks is that of translating knowledge between OntoSem’s KR language and ontologies and those of the Semantic Web. OntoSem2OWL has been developed as a translation system to support these translations. We describe SemNews, an implemented prototype application that demonstrates the process. It monitors RSS feeds of news stories, applies OntoSem to understand the text, and exports the computed facts back to the Web in OWL. I.

Abstract. Sensor Web is a revolutionary concept towards achieving a collaborative, coherent, consistent, and consolidated sensor data collection, fusion and distribution system. Sensor Webs can perform as an extensive monitoring and sensing system that provides timely, comprehensive, continuous and multi-mode observations. This new earth-observation system opens up a new avenue to fast assimilation of data from various sensors (both in situ and remote) and to accurate analysis and informed decision makings. So far, most of the sensor networks work in isolated environments rather than being integrated into a higher system of environmental systems. Our vision of the Sensor Web is an open infrastructure that allows end users to automatically access and extract and use appropriate information from multiple sensor sources over the Internet. This paper proposes an architectural framework, the Sensor Web Agent Platform (SWAP) that makes use of two of the most promising distributed architectural paradigms i.e. Web Services and Multi Agent Systems. SWAP allows for integrating arbitrary sensors or sensor networks into a loosely coupled higher level environment that facilitates developing and deploying end user applications across multiple application domains. An ontology framework and an abstract hierarchy are proposed for fusing and integrating data and storing and re-using generic information extraction techniques within the system. 1

An analysis of human planning strategies reveals that much of the knowledge that underlies intelligent planning involves commonsense psychology, the way that people think that they think. In this paper we describe our continuing effort to formalize a large-scale theory of commonsense psychology as 30 interrelated content theories in first-order logic. This paper discusses key aspects of the 16 content theories that we have completed, focusing on those that provide an account of how knowledge and intention lead to action, namely, memory, knowledge management, envisionment, goals, planning, and execution. Some of these areas present challenges to many of the simplifying assumptions that have traditionally been made in formal knowledge representation research; others are areas of commonsense knowledge where few formal treatments have previously been attempted. 1

Abstract. The design of TimeML as an expressive language for temporal information brings promises, and challenges; in particular, its representational properties raise the bar for traditional information extraction methods applied to the task of text-to-TimeML analysis. A reference corpus, such as TimeBank, is an invaluable asset in this situation; however, certain characteristics of TimeBank—size and consistency, primarily—present challenges of their own. We discuss the design, implementation, and performance of an automatic TimeML-compliant annotator, trained on TimeBank, and deploying a hybrid analytical strategy of mixing aggressive finitestate processing over linguistic annotations with a state-of-the-art machine learning technique capable of leveraging large amounts of unannotated data. The results we report are encouraging in the light of a close analysis of TimeBank; at the same time they are indicative of the need for more infrastructure work, especially in the direction of creating a larger and more robust reference corpus. 1

Answering questions that ask about temporal information involves several forms of inference. First, relations between events and temporal expressions need to be inferred, either in the question or in the answer. Second, semantic inference between events, entities and their definitions needs to be performed. Sometimes, semantic inference employs aspectual information to relate events expressed by different lexical units. Third, temporal inference is preconditioned by relations between events, or between events, their arguments and their time anchors. In this paper we present a Question Answering (QA) methodology for handling temporal inference by combining all these forms of inference. The Problem The AQUAINT 1 program is a multi-project effort aimed at creating capabilities of processing complex questions and finding their answers from heterogeneous collections of texts. An important component of this effort deals with the recognition and processing of temporal information for Question Answering (QA). When asking a question that refers directly or indirectly to a temporal expression, the answer is expected to validate the temporal constraints. To achieve such functionality, QA systems need to (1) incorporate relations between temporal expressions and events or entities mentioned in the question; and (2) to rely on temporal inference for justifying the answer. Answer justification was presented in (Harabagiu et al. 2001) as a multifeedback methodology, but this mechanism did not include any temporal constraints. Whenever the answer to a question needs to be justified, if temporal expressions are involved, the justification must contain some form of temporal inference. For example, the expected answer type of question Q 1 is a DATE: Q 1: “When did Iraq invade Kuwait?” The expected answer type is an argument of the event E1=“invade ” which has two more arguments: “Iraq ” and

Integrated Asset Management (IAM) is the vision of ITenabled transformation of oilfield operations where information integration from a variety of tools for reservoir modeling, simulation, and performance prediction will lead to rapid decision making for continuous production optimization. This paper describes the design of a model-based IAM system for production forecasting. Domain knowledge is captured through a formal modeling language that forms the basis for an intuitive user interface to the system. An IAM metacatalog captures domain knowledge as well as metadata about computational resources and data sets in a single ontological framework, thereby providing a unified mechanism for application, data, and workflow integration. The framework is designed to be portable across oilfield assets, to allow different classes of end users to interact with the integrated system, and to accomodate new domain knowledge, software applications, data sets, and workflows for IAM. 1.

Abstract. In the context of the eTen project, WINS, a web-based business intelligence service to public and private financial institutions has been designed and implemented. One of the goals of the project was to provide new financial knowledge on companies from information gathered through interoperable information services. The services were implemented under the new emerging standard XBRL used for financial reporting. We sketch how relevant financial information was extracted from annual financial reportings. We also show at the same time the limitations we encountererd with the XBRL schema, due to the lack of reasoning support over XML-based data and information extracted from documents. To overcome these difficulties, we describe the “ontologization” of XBRL, which we assume to be a necessary requisite for large intelligent web-based financial information and decision support systems. 1 General Background In the context of the eTen project, WINS, a web-based business intelligence

Representation and reasoning about time and events is a fundamental aspect of our cognitive abilities and intrinsic to our construal of the structure of our personal and historical lives and recall of past experiences. This talk describes an abstract device called a Chronoscope, that allows a temporal representation (a set of events and their temporal relations) to be viewed based on temporal abstractions. The temporal representation is augmented with abstract events called episodes that stand for discourse segments. The temporal abstractions allow one to collapse temporal relations, or view the representation at different time granularities (hour, day, month, year, etc.), with corresponding changes in event characterization and temporal relations at those granularities. A temporal representation can also be filtered to specify temporal trajectories of particular participants. Trajectories, in turn, can be intersected at various levels of granularity. Chronoscopes can be used to compare temporal representations (e.g., for aggregation, summarization, or evaluation purposes), as well as help in the visualization of temporal narratives. 1.

The interpretation of temporal expressions in text is an important constituent task for many practical natural language processing tasks, including question-answering, information extraction and text summarisation. Although temporal expressions have long been studied in the research literature, it is only more recently, with the impetus provided by exercises like the ACE Program, that attention has been directed to broad-coverage, implemented systems. In this paper, we describe our approach to intermediate semantic representations in the interpretation of temporal expressions. 1