31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Meaning versus Grammar

This volume investigates the complicated relationship between grammar, computation, and meaning in natural languages. It details conditions under which meaning-driven processing of natural language is feasible, discusses an operational and accessible implementation of the grammatical cycle for Dutch, and offers analyses of a number of further conjectures about constituency and entailment in natural language

Respecting Relations: Memory Access and Antecedent Retrieval in Incremental Sentence Processing

This dissertation uses the processing of anaphoric relations to probe how linguistic information is encoded in and retrieved from memory during real-time sentence comprehension. More specifically, the dissertation attempts to resolve a tension between the demands of a linguistic processor implemented in a general-purpose cognitive architecture and the demands of abstract grammatical constraints that govern language use. The source of the tension is the role that abstract configurational relations (such as c-command, Reinhart 1983) play in constraining computations. Anaphoric dependencies are governed by formal grammatical constraints stated in terms of relations. For example, Binding Principle A (Chomsky 1981) requires that antecedents for local anaphors (like the English reciprocal each other) bear the c-command relation to those anaphors. In incremental sentence processing, antecedents of anaphors must be retrieved from memory. Recent research has motivated a model of processing that exploits a cue-based, associative retrieval process in content-addressable memory (e.g. Lewis, Vasishth & Van Dyke 2006) in which relations such as c-command are difficult to use as cues for retrieval. As such, the c-command constraints of formal grammars are predicted to be poorly implemented by the retrieval mechanism. I examine retrieval's sensitivity to three constraints on anaphoric dependencies: Principle A (via Hindi local reciprocal licensing), the Scope Constraint on bound-variable pronoun licensing (often stated as a c-command constraint, though see Barker 2012), and Crossover constraints on pronominal binding (Postal 1971, Wasow 1972). The data suggest that retrieval exhibits fidelity to the constraints: structurally inaccessible NPs that match an anaphoric element in morphological features do not interfere with the retrieval of an antecedent in most cases considered. In spite of this alignment, I argue that retrieval's apparent sensitivity to c-command constraints need not motivate a memory access procedure that makes direct reference to c-command relations. Instead, proxy features and general parsing operations conspire to mimic the extension of a system that respects c-command constraints. These strategies provide a robust approximation of grammatical performance while remaining within the confines of a independently- motivated general-purpose cognitive architecture

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

Ancient ancestors for modern practices: An evolutionary concept analysis of digital marginalia

Marginalia, the notes readers write in the blank spaces of their books, are significant objects of study in bibliography and book history, among other fields. Due to factors including findability and fragile book materials, marginalia from the nineteenth and twentieth centuries are difficult to study. The same does not necessarily have to be true for similar objects from the twenty-first century. This thesis uses Rodger’s evolutionary concept analysis to analyze the usage of digital marginalia in the scholarly literature from 1991 to 2020. Beginning with an overview of bibliography and the history of marginalia, this thesis situates digital marginalia in a bibliographic context. Digital marginalia’s definitions, characteristic attributes, events related to the creation of digital marginalia, and concepts related to the practice are then examined. Bringing in connections to bibliographic concepts, this thesis argues that digital marginalia and bibliography provide each other reciprocal value. Like their physical counterparts, digital marginalia provide evidence of users’ interactions with media, their social interactions through that media, and their sociocultural contexts

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

Profiling large-scale lazy functional programs

The LOLITA natural language processing system is an example of one of the ever increasing number of large-scale systems written entirely in a functional programming language. The system consists of over 50,000 lines of Haskell code and is able to perform a number of tasks such as semantic and pragmatic analysis of text, context scanning and query analysis. Such a system is more useful if the results are calculated in real-time, therefore the efficiency of such a system is paramount. For the past three years we have used profiling tools supplied with the Haskell compilers GHC and HBC to analyse and reason about our programming solutions and have achieved good results; however, our experience has shown that the profiling life-cycle is often too long to make a detailed analysis of a large system possible, and the profiling results are often misleading. A profiling system is developed which allows three types of functionality not previously found in a profiler for lazy functional programs. Firstly, the profiler is able to produce results based on an accurate method of cost inheritance. We have found that this reduces the possibility of the programmer obtaining misleading profiling results. Secondly, the programmer is able to explore the results after the execution of the program. This is done by selecting and deselecting parts of the program using a post-processor. This greatly reduces the analysis time as no further compilation, execution or profiling of the program is needed. Finally, the new profiling system allows the user to examine aspects of the run-time call structure of the program. This is useful in the analysis of the run-time behaviour of the program. Previous attempts at extending the results produced by a profiler in such a way have failed due to the exceptionally high overheads. Exploration of the overheads produced by the new profiling scheme show that typical overheads in profiling the LOLITA system are: a 10% increase in compilation time; a 7% increase in executable size and a 70% run-time overhead. These overheads mean a considerable saving in time in the detailed analysis of profiling a large, lazy functional program

Concepts, Frames and Cascades in Semantics, Cognition and Ontology

This open access book presents novel theoretical, empirical and experimental work exploring the nature of mental representations that support natural language production and understanding, and other manifestations of cognition. One fundamental question raised in the text is whether requisite knowledge structures can be adequately modeled by means of a uniform representational format, and if so, what exactly is its nature. Frames are a key topic covered which have had a strong impact on the exploration of knowledge representations in artificial intelligence, psychology and linguistics; cascades are a novel development in frame theory. Other key subject areas explored are: concepts and categorization, the experimental investigation of mental representation, as well as cognitive analysis in semantics. This book is of interest to students, researchers, and professionals working on cognition in the fields of linguistics, philosophy, and psychology