Symbols as Self-emergent Entities in an Optimization Process of Feature Extraction and Predictions

In the mammalian cortex the early sensory processing can be characterized as feature extraction resulting in local and analogue low-level representations. As a direct consequence, these map directly to the environment, but interpretation under natural conditions is ambiguous. In contrast, high-level representations for cognitive processing, e.g. language, require symbolic representations characterized by expression and syntax. The representations are binary, structured and disambiguated. However, do these fundamental functional distinctions translate into a fundamental distinction of the respective brain areas and their anatomical and physiological properties? Here we argue that the distinction between early sensory processing and higher cognitive functions may not be based on structural differences of cortical areas; instead similar learning principles acting on input signals with different statistics give rise to the observed variations of function. Firstly, we give an account of present research describing neuronal properties at early stages of sensory systems as a consequence of an optimization process over the set of natural stimuli. Secondly, addressing a stage following early visual processing we suggest to extend the unsupervised learning scheme by including predictive processes. These contain the widely used objective of temporal coherence as a special case and are a powerful approach to resolve ambiguities. Furthermore, in combination with a prior on the bandwidth of information exchange between units it leads to a condensation of information. Thirdly, as a crucial step, not only are predictive units optimized, but the selectivity of the feature extractors are adapted to allow optimal predictability. Thus, over and beyond making useful predictions, we propose that the predictability of a stimulus be in itself a selection criterion for further processing. In a hierarchical system the combined optimization process leads to entities that represent condensed pieces of knowledge and that are not analogue anymore. Instead, these entities work as arguments in a framework of transformations that realize predictions. Thus, the criteria of predictability and condensation in an optimization of sensory representations relate directly to the two defining properties of symbols of expression and syntax. In this paper, we sketch an unsupervised learning process that gradually transforms analogue local representations into discrete binary representations by means of four hypotheses. We propose that in this optimization process acting in a hierarchical system, entities emerge at, higher levels that fulfil the criteria defining symbols, instantiating qualitatively different representations at similarly structured low and high levels.     

Towards a processual microbial ontology

Standard microbial evolutionary ontology is organized according to a nested hierarchy of entities at various levels of biological organization. It typically detects and defines these entities in relation to the most stable aspects of evolutionary processes, by identifying lineages evolving by a process of vertical inheritance from an ancestral entity. However, recent advances in microbiology indicate that such an ontology has important limitations. The various dynamics detected within microbiological systems reveal that a focus on the most stable entities (or features of entities) over time inevitably underestimates the extent and nature of microbial diversity. These dynamics are not the outcome of the process of vertical descent alone. Other processes, often involving causal interactions between entities from distinct levels of biological organisation, or operating at different time scales, are responsible not only for the destabilisation of pre-existing entities, but also for the emergence and stabilisation of novel entities in the microbial world. In this article we consider microbial entities as more or less stabilised functional wholes, and sketch a network-based ontology that can represent a diverse set of processes including, for example, as well as phylogenetic relations, interactions that stabilise or destabilise the interacting entities, spatial relations, ecological connections, and genetic exchanges. We use this pluralistic framework for evaluating (i) the existing ontological assumptions in evolution (e.g. whether currently recognized entities are adequate for understanding the causes of change and stabilisation in the microbial world), and (ii) for identifying hidden ontological kinds, essentially invisible from within a more limited perspective. We propose to recognize additional classes of entities that provide new insights into the structure of the microbial world, namely “processually equivalent” entities, “processually versatile” entities, and “stabilized” entities.     

Turning text into research networks: information retrieval and computational ontologies in the creation of scientific databases

Background

Web-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive.

Method

We focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology.

Results

We present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances.

Conclusion

We have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels.     

土壤动物知识图谱构建理论、方法与技术——以浙江天目山土壤螨类为例

土壤动物学面临以全新知识体系为科学研究框架的变革时期,其核心内容是以数据驱动为主要特征的人工智能技术方法。目前广泛应用的基于数据库的数据处理分析方法,面临着数据多源异构、快速增长和处理能力不足之间的矛盾。基于快速发展的大数据科学和人工智能技术的数据挖掘方法在解决前述矛盾中有突出优势,但需要依赖一个强大的领域知识库,然而土壤动物领域知识图谱的研究十分匮乏。土壤动物知识图谱是一个具有有向图结构的知识库,其中图的节点代表与土壤动物相关的实体或概念,图的边代表实体或概念之间的各种语义关系。提出了土壤动物知识图谱的定义、内涵、理论模型和构建方法,以浙江天目山土壤螨类多样性为例,分析了构建山地土壤动物知识图谱的技术方法;以土壤动物多样性研究关注的物种分布、物种共存、环境条件对物种的影响作用为例,探讨了基于山地土壤动物知识图谱可以解决的相关科学问题。研究表明,土壤动物知识图谱在解决生物多样性重要科学问题方面具有独特的潜力和优势,有力推动了土壤动物学、信息科学和数据科学交叉的土壤动物信息学的发展。     

Genomic imprinting and the units of adaptation

Two guiding principles identify which biological entities are able to evolve adaptations. Williams'' principle holds that, in order for an entity to evolve adaptations, there must be selection between such entities. Maynard Smith''s principle holds that, in order for an entity to evolve adaptations, selection within such entities must be absent or negligible. However, although the kinship theory of genomic imprinting suggests that parent-of-origin-specific gene expression evolves as a consequence of natural selection acting between—rather than within—individuals, it evades adaptive interpretation at the individual level and is instead viewed as an outcome of an intragenomic conflict of interest between an individual''s genes. Here, I formalize the idea that natural selection drives intragenomic conflicts of interest between genes originating from different parents. Specifically, I establish mathematical links between the dynamics of natural selection and the idea of the gene as an intentional, inclusive-fitness-maximizing agent, and I clarify the role that information about parent of origin plays in mediating conflicts of interest between genes residing in the same genome. These results highlight that the suppression of divisive information may be as important as the suppression of lower levels of selection in maintaining the integrity of units of adaptation.     

Extraction of semantic biomedical relations from text using conditional random fields

Background  

The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.     

Distance learning techniques for ontology similarity measuring and ontology mapping

Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.     

Species: kinds of individuals or individuals of a kind

The “species‐as‐individuals” thesis takes species, or taxa, to be individuals. On grounds of spatiotemporal boundedness, any biological entity at any level of complexity subject to evolutionary processes is an individual. From evolutionary theory flows an ontology that does not countenance universal properties shared by evolving entities. If austere nominalism were applied to evolving entities, however, nature would be reduced to a mere flow of passing events, each one a blob in space–time and hence of passing interest only. Yet if there is genuine biodiversity in nature, if nature is genuinely carved into species, and taxa, then these evolutionary entities will be genuinely differentiated into specific kinds, each species being one of its kind. Given the fact that evolving entities have un‐sharp boundaries, an appropriately weak, “non‐essentialist” concept of natural kind has to be invoked that does not allow for strong identity conditions. The thesis of this paper is that species are not either individuals, or natural kinds. Instead, species are complex wholes (particulars, individuals) that instantiate a specific natural kind. © The Willi Hennig Society 2007.     

Assessing the Impact of Case Sensitivity and Term Information Gain on Biomedical Concept Recognition

Concept recognition (CR) is a foundational task in the biomedical domain. It supports the important process of transforming unstructured resources into structured knowledge. To date, several CR approaches have been proposed, most of which focus on a particular set of biomedical ontologies. Their underlying mechanisms vary from shallow natural language processing and dictionary lookup to specialized machine learning modules. However, no prior approach considers the case sensitivity characteristics and the term distribution of the underlying ontology on the CR process. This article proposes a framework that models the CR process as an information retrieval task in which both case sensitivity and the information gain associated with tokens in lexical representations (e.g., term labels, synonyms) are central components of a strategy for generating term variants. The case sensitivity of a given ontology is assessed based on the distribution of so-called case sensitive tokens in its terms, while information gain is modelled using a combination of divergence from randomness and mutual information. An extensive evaluation has been carried out using the CRAFT corpus. Experimental results show that case sensitivity awareness leads to an increase of up to 0.07 F1 against a non-case sensitive baseline on the Protein Ontology and GO Cellular Component. Similarly, the use of information gain leads to an increase of up to 0.06 F1 against a standard baseline in the case of GO Biological Process and Molecular Function and GO Cellular Component. Overall, subject to the underlying token distribution, these methods lead to valid complementary strategies for augmenting term label sets to improve concept recognition.     

BioCaster: detecting public health rumors with a Web-based text mining system

SUMMARY: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. AVAILABILITY: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org.     

BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology

Background

Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis.

Results

We describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology.

Conclusions

BiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0486-3) contains supplementary material, which is available to authorized users.