On the functional origins of essentialism

This essay examines the proposal that psychological essentialism results from a history of natural selection acting on human representation and inference systems. It has been argued that the features that distinguish essentialist representational systems are especially well suited for representing natural kinds. If the evolved function of essentialism is to exploit the rich inductive potential of such kinds, then it must be subserved by cognitive mechanisms that carry out at least three distinct functions: identifying these kinds in the environment, constructing essentialized representations of them, and constraining inductive inferences about kinds. Moreover, there are different kinds of kinds, ranging from nonliving substances to biological taxa to within-species kinds such as sex, and the causal processes that render these categories coherent for the purposes of inductive generalization vary. If the evolved function of essentialism is to support inductive generalization under ignorance of true causes, and if kinds of kinds vary in the implicit assumptions that support valid inductive inferences about them, then we expect different, functionally incompatible modes of essentialist thinking for different kinds. In particular, there should be differences in how biological and nonbiological substances, biological taxa, and biological and social role kinds are essentialized. The functional differences between these kinds of essentialism are discussed

Knowledge Representation and WordNets

Knowledge itself is a representation of “real facts”. Knowledge is a logical model that presents facts from “the real world” witch can be expressed in a formal language. Representation means the construction of a model of some part of reality. Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence. Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge. Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams

280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification

We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach leverages the interlanguage links of Wikipedia followed by character-level classifiers to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages

Evolution and Devolution of Knowledge: A Tale of Two Biologies

Anthropological inquiry suggests that all societies classify animals and plants in similar ways. Paradoxically, in the same cultures that have seen large advances in biological science, citizenry's practical knowledge of nature has dramatically diminished. Here we describe historical, cross-cultural and developmental research on how people ordinarily conceptualize organic nature (folkbiology), concentrating on cognitive consequences associated with knowledge devolution. We show that results on psychological studies of categorization and reasoning from “standard populations” fail to generalize to humanity at large. Usual populations (Euro-American college students) have impoverished experience with nature, which yields misleading results about knowledge acquisition and the ontogenetic relationship between folkbiology and folkpsychology. We also show that groups living in the same habitat can manifest strikingly distinct behaviors, cognitions and social relations relative to it. This has novel implications for environmental decision making and management, including commons problems.

The role of zooarchaeology in the interpretation of socioeconomic status: a discussion with reference to Medieval Europe

Social inequality is ubiquitous in human society, and the concept of social standing has been of fundamental importance throughout time (Price and Feinman 1995). The inference of social status has encountered problems in many areas of archaeology (see Orser 1990; Grenville 1997), and the use of zooarchaeology as part of an integrated approach may contribute to our understanding of important issues (Crabtree 1990). This paper reviews the various criteria used to infer socioeconomic status from faunal assemblages, taking examples from a variety of contexts, but concentrating primarily on medieval Europe, and England in particular. The problems associated with the application of zooarchaeological methods to this sphere of research are discussed, and some possible solutions proposed. It is suggested that zooarchaeology can play an important role in answering questions relating to socioeconomic standing, provided that it forms part of a wider archaeological strategy

Guidelines to Study Differences in Expressiveness between Ontology Specification Languages: A Case Of Study

We focus on our experiences on translating ontologies between two ontology languages, FLogic and Ontolingua, in the framework of Methontology and ODE. Rather than building "ad hoc" translators between languages or using KIF, our option consists of translating through ODE intermediate representations. So, we have built direct translators from ODE intermediate representations to Ontolingua and FLogic, and we have also built reverse translators from these two languages to ODE intermediate representations. Expressiveness of the target languages is the main feature to analyse when automatically generating ontologies from ODE intermediate representations. Therefore, we analyse the expressiveness of Ontolingua and FLogic for creating classes, instances, relations, functions and axioms, which are the essential components in ontologies. The motivation for this analysis can be found in the (KA)² initiative and can be easily extended to any other domains and languages

Changes in co-existence mechanisms along a long-term soil chronosequence revealed by functional trait diversity

1. Functional trait diversity can reveal mechanisms of species co-existence in plant communities. Few studies have tested whether functional diversity for foliar traits related to resource use strategy increases or decreases with declining soil phosphorus (P) in forest communities. 2. We quantified tree basal area and four foliar functional traits (i.e. nitrogen (N), phosphorus (P), thickness and tissue density) for all woody species along the c. 120 kyr Franz Josef soil chronosequence in cool temperate rainforest, where strong shifts occur in light and soil nutrient availability (i.e. total soil P declines from 805 to 100 mg g–1). We combined the abundance and trait data in functional diversity indices to quantify trait convergence and divergence, in an effort to determine whether mechanisms of co-existence change with soil fertility. 3. Relationships between species trait means and total soil N and P were examined using multiple regression, with and without weighting of species abundances. We used Rao’s quadratic entropy to quantify functional diversity at the plot scale, then compared this with random expectation, using a null model that randomizes abundances across species within plots. Taxonomic diversity was measured using Simpson’s Diversity. Relationships between functional and taxonomic diversity and total soil P were examined using jackknife linear regression. 4. Leaf N and P declined and leaf thickness and density increased monotonically with declining total soil P along the sequence; these relationships were unaffected by abundance-weighting of species in the analyses. Inclusion of total soil N did not improve predictions of trait means. All measures of diversity calculated from presence/absence data were unrelated to total soil N and P. There was no evidence for a relationship between Rao values using quantitative abundances and total soil P. However, there was a strongly positive relationship between Rao, expressed relative to random expectation, and total soil P, indicating trait convergence of dominant species as soil P declined. 5. Synthesis: Our results demonstrate that at high fertility dominant species differ in resource use strategy, but as soil fertility declines over the long-term, dominant species increasingly converge on a resource-retentive strategy. This suggests that differentiation in resource use strategy is required for co-existence at high fertility but not in low fertility ecosystems

Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web