A knowledge acquisition tool to assist case authoring from texts.

Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without even thinking about it. For example, an occupational therapist (OT) that assesses the needs of a new disabled person may be reminded of a previous person in terms of their disabilities. He may or may not decide to recommend the same devices based on the outcome of an earlier (disabled) person. Case-based reasoning makes use of a collection of past problem-solving experiences thus enabling users to exploit the information of others successes and failures to solve their own problem(s). This project has developed a CBR tool to assist in matching SmartHouse technology to the needs of the elderly and people with disabilities. The tool makes suggestions of SmartHouse devices that could assist with given impairments. SmartHouse past problem-solving textual reports have been used to obtain knowledge for the CBR system. Creating a case-based reasoning system from textual sources is challenging because it requires that the text be interpreted in a meaningful way in order to create cases that are effective in problem-solving and to be able to reasonably interpret queries. Effective case retrieval and query interpretation is only possible if a domain-specific conceptual model is available and if the different meanings that a word can take can be recognised in the text. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Furthermore, hierarchically structured case representations are preferred to flat-structured ones for problem-solving because they allow for comparison at different levels of specificity thus resulting in more effective retrieval than flat structured cases. This project has developed SmartCAT-T, a tool that creates knowledge-rich hierarchically structured cases from semi-structured textual reports. SmartCAT-T highlights important phrases in the textual SmartHouse problem-solving reports and uses the phrases to create a conceptual model of the domain. The model then becomes a standard structure onto which each semi-structured SmartHouse report is mapped in order to obtain the correspondingly structured case. SmartCAT-T also relies on an unsupervised methodology that recognises word synonyms in text. The methodology is used to create a uniform vocabulary for the textual reports and the resulting harmonised text is used to create the standard conceptual model of the domain. The technique is also employed in query interpretation during problem solving. SmartCAT-T does not require large sets of tagged data for learning, and the concepts in the conceptual model are interpretable, allowing for expert refinement of knowledge. Evaluation results show that the created cases contain knowledge that is useful for problem solving. An improvement in results is also observed when the text and queries are harmonised. A further evaluation highlights a high potential for the techniques developed in this research to be useful in domains other than SmartHouse. All this has been implemented in the Smarter case-based reasoning system

Modeling Meaning for Description and Interaction

Language is a powerful tool for communication and coordination, allowing us to share thoughts, ideas, and instructions with others. Accordingly, enabling people to communicate linguistically with digital agents has been among the longest-standing goals in artificial intelligence (AI). However, unlike humans, machines do not naturally acquire the ability to extract meaning from language. One natural solution to this problem is to represent meaning in a structured format and then develop models for processing language into such structures. Unlike natural language, these structured representations can be directly processed and interpreted by existing algorithms. Indeed, much of the digital infrastructure we have built is mediated by structured representations (e.g. programs and APIs). Furthermore, unlike the internal representations of current neural models, structured representations are built to be used and interpreted by people. I focus on methods for parsing language into these dually-interpretable representations of meaning. I introduce models that learn to predict structure from language and apply them to a variety of tasks, ranging from linguistic description to interaction with robots and digital assistants. I address three thematic challenges in modeling meaning: abstraction, sensitivity, and ambiguity. In order to be useful, meaning representations must abstract away from the linguistic input. Abstractions differ for each representation used, and must be learned by the model. The process of abstraction entails a kind of invariance: different linguistic inputs mapping to the same meaning. At the same time, meaning is sensitive to slight changes in the linguistic input; here, similar inputs might map to very different meanings. Finally, language is often ambiguous, and many utterances have multiple meanings. In cases of ambiguity, models of meaning must learn that the same input can map to different meanings

On the Concept of Power

This dissertation provides a definition of “power”, and examines the implication of said definition for the study of politics. The exam of extant debates, from Dahl to Lukes and Morriss, shows that a proper definition of power is still to be had; those so far proposed are either circular and/or so detached from the concept’s meaning in ordinary language that they cease to be about power. Re-connecting power to its meaning in natural languages is the necessary premise to any study of politics, be it empirical, theoretical, or normative: without the concept of power, we could not conceive of politics as a specific domain, either to be scientifically inquired or acted into. To formulate a viable definition, we have to recognize that “power” is not a “thing” or phenomenon, but rather refers to a state, or a modality, within which persons may be situated. Power is a modal concept, and the relevant modality, or category, is that of possibility. More specifically, “power” denotes the status, or condition, of having possibilities available and representing them as such. If and only if both conditions are present, then power can meaningfully be attributed. The implications of this definition are far reaching, as they show how, by adopting perspectives that variously eschew the category of possibility, significant portions of political science and political philosophy are, by their own design, incapable of properly conceptualizing power, and therefore politics. The main problem with political science is the attempt to methodologically reduce politics to causal and probabilistic regularities, which entails the disappearance of possibility in favor of necessity. Political philosophy, on the other hand, tends to prioritize various forms of a teleologically oriented normativity, which results in discarding possibility in favor of necessity, again losing sight of politics

Concept-based Text Clustering

Thematic organization of text is a natural practice of humans and a crucial task for today's vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping like ones together and separating those with different topics. Clusters provide a comprehensive logical structure that facilitates exploration, search and interpretation of current texts, as well as organization of future ones. Automatic clustering is usually based on words. Text is represented by the words it mentions, and thematic similarity is based on the proportion of words that texts have in common. The resulting bag-of-words model is semantically ambiguous and undesirably orthogonal|it ignores the connections between words. This thesis claims that using concepts as the basis of clustering can significantly improve effectiveness. Concepts are defined as units of knowledge. When organized according to the relations among them, they form a concept system. Two concept systems are used here: WordNet, which focuses on word knowledge, and Wikipedia, which encompasses world knowledge. We investigate a clustering procedure with three components: using concepts to represent text; taking the semantic relations among them into account during clustering; and learning a text similarity measure from concepts and their relations. First, we demonstrate that concepts provide a succinct and informative representation of the themes in text, exemplifying this with the two concept systems. Second, we define methods for utilizing concept relations to enhance clustering by making the representation models more discriminative and extending thematic similarity beyond surface overlap. Third, we present a similarity measure based on concepts and their relations that is learned from a small number of examples, and show that it both predicts similarity consistently with human judgement and improves clustering. The thesis provides strong support for the use of concept-based representations instead of the classic bag-of-words model

Semiotics 101: Taking the Printed Matter Doctrine Seriously

The printed matter doctrine is a branch of the section 101 doctrine of patent eligibility that, among other things, prevents the patenting of technical texts and diagrams. The contemporary formulation of the doctrine is highly problematic. It borders on incoherency in many of its applications, and it lacks any recognized grounding in the Patent Act. Yet, despite its shortcomings, courts have not abandoned the printed matter doctrine, likely because the core applications of the doctrine place limits on the reach of the patent regime that are widely viewed as both intuitively \u27correct and normatively desirable. Instead of abandoning the doctrine, courts have marginalized it. They have retained the substantive effects of the printed matter doctrine but avoided analyzing it whenever possible. This Article adopts a different approach: it takes the printed matter doctrine seriously. It reinterprets the printed matter doctrine as the sign doctrine, revealing both the conceptual coherence hidden in the doctrine\u27s historical applications and the doctrine\u27s as-of-yet unnoticed statutory grounding. The key to this reconceptualization is recognizing that the printed matter doctrine is in effect already based on semiotic principles. The printed matter doctrine purports to be about information, but it is actually about signs. It purports to curtail the patenting of worldly artifacts, but it actually curbs the reach ofpatent protection into mental representations in the human mind. To support these arguments, this Article offers a course in Semiotics 101 : a semiotics primer strategically targeted on the principles that prove to be relevant to the section 101 doctrine ofpatent eligibility. This Article also examines one unexpected consequence of taking the printed matter doctrine seriously and adopting a semiotic framework. It reconsiders the patentability of a class of software inventions which are defined here as computer models. As a matter of semiotic logic, the routine patentability of newly invented computer models under the contemporary patent eligibility doctrine cannot be reconciled with the categorical unpatentability of mechanical measuring devices with new labels under the printed matter doctrine

Innovations for Requirements Analysis, From Stakeholders' Needs to Formal Designs

14th MontereyWorkshop 2007 Monterey, CA, USA, September 10-13, 2007 Revised Selected PapersWe are pleased to present the proceedings of the 14thMontereyWorkshop, which took place September 10–13, 2007 in Monterey, CA, USA. In this preface, we give the reader an overview of what took place at the workshop and introduce the contributions in this Lecture Notes in Computer Science volume. A complete introduction to the theme of the workshop, as well as to the history of the Monterey Workshop series, can be found in Luqi and Kordon’s “Advances in Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and Formal Designs” in this volume. This paper also contains the case study that many participants used as a problem to frame their analyses, and a summary of the workshop’s results

Genre, schema, and the academic writing process : an enquiry into the generalisability of generic structure and its relationship to schematic knowledge.

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