Human concept cognition and semantic relations in the unified medical language system: A coherence analysis.

There is almost a universal agreement among scholars in information retrieval (IR) research that knowledge representation needs improvement. As core component of an IR system, improvement of the knowledge representation system has so far involved manipulation of this component based on principles such as vector space, probabilistic approach, inference network, and language modeling, yet the required improvement is still far from fruition. One promising approach that is highly touted to offer a potential solution exists in the cognitive paradigm, where knowledge representation practice should involve, or start from, modeling the human conceptual system. This study based on two related cognitive theories: the theory-based approach to concept representation and the psychological theory of semantic relations, ventured to explore the connection between the human conceptual model and the knowledge representation model (represented by samples of concepts and relations from the unified medical language system, UMLS). Guided by these cognitive theories and based on related and appropriate data-analytic tools, such as nonmetric multidimensional scaling, hierarchical clustering, and content analysis, this study aimed to conduct an exploratory investigation to answer four related questions. Divided into two groups, a total of 89 research participants took part in two sets of cognitive tasks. The first group (49 participants) sorted 60 food names into categories followed by simultaneous description of the derived categories to explain the rationale for category judgment. The second group (40 participants) performed sorting 47 semantic relations (the nonhierarchical associative types) into 5 categories known a priori. Three datasets resulted as a result of the cognitive tasks: food-sorting data, relation-sorting data, and free and unstructured text of category descriptions. Using the data analytic tools mentioned, data analysis was carried out and important results and findings were obtained that offer plausible explanations to the 4 research questions. Major results include the following: (a) through discriminant analysis category members were predicted consistently in 70% of the time; (b) the categorization bases are largely simplified rules, naïve explanations, and feature-based; (c) individuals theoretical explanation remains valid and stays stable across category members; (d) the human conceptual model can be fairly reconstructed in a low-dimensional space where 93% of the variance in the dimensional space is accounted for by the subjects performance; (e) participants consistently classify 29 of the 47 semantic relations; and, (f) individuals perform better in the functional and spatial dimensions of the semantic relations classification task and perform poorly in the conceptual dimension

Conceptual Spaces in Object-Oriented Framework

The aim of this paper is to show that the middle level of mental representations in a conceptual spaces framework is consistent with the OOP paradigm. We argue that conceptual spaces framework together with vague prototype theory of categorization appears to be the most suitable solution for modeling the cognitive apparatus of humans, and that the OOP paradigm can be easily and intuitively reconciled with this framework. First, we show that the prototypebased OOP approach is consistent with Gärdenfors’ model in terms of structural coherence. Second, we argue that the product of cloning process in a prototype-based model is in line with the structure of categories in Gärdenfors’ proposal. Finally, in order to make the fuzzy object-oriented model consistent with conceptual space, we demonstrate how to define membership function in a more cognitive manner, i.e. in terms of similarity to prototype

Attribute lattice: a graph-based conceptual modeling grammar for heterogeneous data

One key characteristic of big data is variety. With massive and growing amounts of data existing in independent and heterogeneous (structured and unstructured) sources, assigning consistent and interoperable data semantics, which is essential for meaningful use of data, is an increasingly important challenge. I argue, conceptual models, in contrast to their traditional roles in the Information System development, can be used to represent data semantics as perceived by the user of data. In this thesis, I use principles from philosophical ontology, human cognition (i.e., classification theory), and graph theory to offer a theory-based conceptual modeling grammar for this purpose. This grammar reflects data from users of data perspective and independent from data source schema. I formally define the concept of attribute lattice as a graph-based, schema-free conceptual modeling grammar that represents attributes of instances in the domain of interest and precedence relations among them. Each node in an attribute lattice represents an attribute - a true statement (predicate) about some instances in the domain. Each directed arc represents a precedence relation indicating that possessing one attribute implies possessing another attribute. In this thesis, based on the premise that inherent classification is a barrier that hinders semantic interoperation of heterogeneous data sources, a human cognition based conceptual modeling grammar is introduced as an effective way to resolve semantic heterogeneity. This grammar represents the precedence relationship among attributes as perceived by human user and provides a mechanism to infer classes based on the pattern of precedences. Hence, a key contribution of attribute lattice is semantic relativism – that is, the classification in this grammar relies on the pattern of precedence relationship among attributes rather than fixed classes. This modeling grammar uses the immediate and semantic neighbourhoods of an attribute to designate an attribute as either a category, a class or a property and to specify the expansion of an attribute – attributes which are semantically equal to the given attribute. The introduced conceptual modeling grammar is implemented as an artifact to store and manage attribute lattices, to graphically represent them, and integrate lattices from various heterogeneous sources. With the ever-increasing amount of unstructured data (mostly text data) from various data sources such as social media, integrating text data with other data sources has gained considerable attention. This massive amount of data, however, makes finding the data relevant to a topic of interest a new challenge. I argue that the attribute lattice provides a robust semantic foundation to address this information retrieval challenge from unstructured data sources. Hence, a topic modeling approach based on the attribute lattice is proposed for Twitter. This topic model conceptualizes topic structure of tweets related to the domain of interest and enhances information retrieval by improving the semantic interpretability of hashtags

Ontology-driven conceptual modeling: A'systematic literature mapping and review

All rights reserved. Ontology-driven conceptual modeling (ODCM) is still a relatively new research domain in the field of information systems and there is still much discussion on how the research in ODCM should be performed and what the focus of this research should be. Therefore, this article aims to critically survey the existing literature in order to assess the kind of research that has been performed over the years, analyze the nature of the research contributions and establish its current state of the art by positioning, evaluating and interpreting relevant research to date that is related to ODCM. To understand and identify any gaps and research opportunities, our literature study is composed of both a systematic mapping study and a systematic review study. The mapping study aims at structuring and classifying the area that is being investigated in order to give a general overview of the research that has been performed in the field. A review study on the other hand is a more thorough and rigorous inquiry and provides recommendations based on the strength of the found evidence. Our results indicate that there are several research gaps that should be addressed and we further composed several research opportunities that are possible areas for future research

A formal theory of conceptual modeling universals

Conceptual Modeling is a discipline of great relevance to several areas in Computer Science. In a series of papers [1,2,3] we have been using the General Ontological Language (GOL) and its underlying upper level ontology, proposed in [4,5], to evaluate the ontological correctness of conceptual models and to develop guidelines for how the constructs of a modeling language (UML) should be used in conceptual modeling. In this paper, we focus on the modeling metaconcepts of classifiers and objects from an ontological point of view. We use a philosophically and psychologically well-founded theory of universals to propose a UML profile for Ontology Representation and Conceptual Modeling. The formal semantics of the proposed modeling elements is presented in a language of modal logics with quantification restricted to Sortal universals

Conceptual modelling: Towards detecting modelling errors in engineering applications

Rapid advancements of modern technologies put high demands on mathematical modelling of engineering systems. Typically, systems are no longer “simple” objects, but rather coupled systems involving multiphysics phenomena, the modelling of which involves coupling of models that describe different phenomena. After constructing a mathematical model, it is essential to analyse the correctness of the coupled models and to detect modelling errors compromising the final modelling result. Broadly, there are two classes of modelling errors: (a) errors related to abstract modelling, eg, conceptual errors concerning the coherence of a model as a whole and (b) errors related to concrete modelling or instance modelling, eg, questions of approximation quality and implementation. Instance modelling errors, on the one hand, are relatively well understood. Abstract modelling errors, on the other, are not appropriately addressed by modern modelling methodologies. The aim of this paper is to initiate a discussion on abstract approaches and their usability for mathematical modelling of engineering systems with the goal of making it possible to catch conceptual modelling errors early and automatically by computer assistant tools. To that end, we argue that it is necessary to identify and employ suitable mathematical abstractions to capture an accurate conceptual description of the process of modelling engineering systems

What does semantic tiling of the cortex tell us about semantics?

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) features and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions