Semiotic Analysis in the Study of Social Work

Research and professional practices have the joint aim of re-structuring the preconceived notions of reality. They both want to gain the understanding about social reality. Social workers use their professional competence in order to grasp the reality of their clients, while researchers’ pursuit is to open the secrecies of the research material. Development and research are now so intertwined and inherent in almost all professional practices that making distinctions between practising, developing and researching has become difficult and in many aspects irrelevant. Moving towards research-based practices is possible and it is easily applied within the framework of the qualitative research approach (Dominelli 2005, 235; Humphries 2005, 280). Social work can be understood as acts and speech acts crisscrossing between social workers and clients. When trying to catch the verbal and non-verbal hints of each others’ behaviour, the actors have to do a lot of interpretations in a more or less uncertain mental landscape. Our point of departure is the idea that the study of social work practices requires tools which effectively reveal the internal complexity of social work (see, for example, Adams & Dominelli & Payne 2005, 294 – 295). The boom of qualitative research methodologies in recent decades is associated with much profound the rupture in humanities, which is called the linguistic turn (Rorty 1967). The idea that language is not transparently mediating our perceptions and thoughts about reality, but on the contrary it constitutes it was new and even confusing to many social scientists. Nowadays we have got used to read research reports which have applied different branches of discursive analyses or narratologic or semiotic approaches. Although differences are sophisticated between those orientations they share the idea of the predominance of language. Despite the lively research work of today’s social work and the research-minded atmosphere of social work practice, semiotics has rarely applied in social work research. However, social work as a communicative practice concerns symbols, metaphors and all kinds of the representative structures of language. Those items are at the core of semiotics, the science of signs, and the science which examines people using signs in their mutual interaction and their endeavours to make the sense of the world they live in, their semiosis. When thinking of the practice of social work and doing the research of it, a number of interpretational levels ought to be passed before reaching the research phase in social work. First of all, social workers have to interpret their clients’ situations, which will be recorded in the files. In some very rare cases those past situations will be reflected in discussions or perhaps interviews or put under the scrutiny of some researcher in the future. Each and every new observation adds its own flavour to the mixture of meanings. Social workers have combined their observations with previous experience and professional knowledge, furthermore, the situation on hand also influences the reactions. In addition, the interpretations made by social workers over the course of their daily working routines are never limited to being part of the personal process of the social worker, but are also always inherently cultural. The work aiming at social change is defined by the presence of an initial situation, a specific goal, and the means and ways of achieving it, which are – or which should be – agreed upon by the social worker and the client in situation which is unique and at the same time socially-driven. Because of the inherent plot-based nature of social work, the practices related to it can be analysed as stories (see Dominelli 2005, 234), given, of course, that they are signifying and told by someone. The research of the practices is concentrating on impressions, perceptions, judgements, accounts, documents etc. All these multifarious elements can be scrutinized as textual corpora, but not whatever textual material. In semiotic analysis, the material studied is characterised as verbal or textual and loaded with meanings. We present a contribution of research methodology, semiotic analysis, which has to our mind at least implicitly references to the social work practices. Our examples of semiotic interpretation have been picked up from our dissertations (Laine 2005; Saurama 2002). The data are official documents from the archives of a child welfare agency and transcriptions of the interviews of shelter employees. These data can be defined as stories told by the social workers of what they have seen and felt. The official documents present only fragmentations and they are often written in passive form. (Saurama 2002, 70.) The interviews carried out in the shelters can be described as stories where the narrators are more familiar and known. The material is characterised by the interaction between the interviewer and interviewee. The levels of the story and the telling of the story become apparent when interviews or documents are examined with the use of semiotic tools. The roots of semiotic interpretation can be found in three different branches; the American pragmatism, Saussurean linguistics in Paris and the so called formalism in Moscow and Tartu; however in this paper we are engaged with the so called Parisian School of semiology which prominent figure was A. J. Greimas. The Finnish sociologists Pekka Sulkunen and Jukka Törrönen (1997a; 1997b) have further developed the ideas of Greimas in their studies on socio-semiotics, and we lean on their ideas. In semiotics social reality is conceived as a relationship between subjects, observations, and interpretations and it is seen mediated by natural language which is the most common sign system among human beings (Mounin 1985; de Saussure 2006; Sebeok 1986). Signification is an act of associating an abstract context (signified) to some physical instrument (signifier). These two elements together form the basic concept, the “sign”, which never constitutes any kind of meaning alone. The meaning will be comprised in a distinction process where signs are being related to other signs. In this chain of signs, the meaning becomes diverged from reality. (Greimas 1980, 28; Potter 1996, 70; de Saussure 2006, 46-48.) One interpretative tool is to think of speech as a surface under which deep structures – i.e. values and norms – exist (Greimas & Courtes 1982; Greimas 1987). To our mind semiotics is very much about playing with two different levels of text: the syntagmatic surface which is more or less faithful to the grammar, and the paradigmatic, semantic structure of values and norms hidden in the deeper meanings of interpretations. Semiotic analysis deals precisely with the level of meaning which exists under the surface, but the only way to reach those meanings is through the textual level, the written or spoken text. That is why the tools are needed. In our studies, we have used the semiotic square and the actant analysis. The former is based on the distinctions and the categorisations of meanings, and the latter on opening the plotting of narratives in order to reach the value structures.

Phylogeny and prediction of genetic similarity of Cronobacter and related taxa by multilocus sequence analysis (MLSA)

Multilocus sequence analysis (MLSA) based on recN, rpoA and thdF genes was done on more than 30 species of the family Enterobacteriaceae with a focus on Cronobacter and the related genus Enterobacter. The sequences provide valuable data for phylogenetic, taxonomic and diagnostic purposes. Phylogenetic analysis showed that the genus Cronobacter forms a homogenous cluster related to recently described species of Enterobacter, but distant to other species of this genus. Combining sequence information on all three genes is highly representative for the species' %GC-content used as taxonomic marker. Sequence similarity of the three genes and even of recN alone can be used to extrapolate genetic similarities between species of Enterobacteriaceae. Finally, the rpoA gene sequence, which is the easiest one to determine, provides a powerful diagnostic tool to identify and differentiate species of this family. The comparative analysis gives important insights into the phylogeny and genetic relatedness of the family Enterobacteriaceae and will serve as a basis for further studies and clarifications on the taxonomy of this large and heterogeneous family.

Semantic Methods for Intelligent Distributed Design Environments

Continuous advancements in technology have led to increasingly comprehensive and distributed product development processes while in pursuit of improved products at reduced costs. Information associated with these products is ever changing, and structured frameworks have become integral to managing such fluid information. Ontologies and the Semantic Web have emerged as key alternatives for capturing product knowledge in both a human-readable and computable manner. The primary and conclusive focus of this research is to characterize relationships formed within methodically developed distributed design knowledge frameworks to ultimately provide a pervasive real-time awareness in distributed design processes. Utilizing formal logics in the form of the Semantic Web’s OWL and SWRL, causal relationships are expressed to guide and facilitate knowledge acquisition as well as identify contradictions between knowledge in a knowledge base. To improve the efficiency during both the development and operational phases of these “intelligent” frameworks, a semantic relatedness algorithm is designed specifically to identify and rank underlying relationships within product development processes. After reviewing several semantic relatedness measures, three techniques, including a novel meronomic technique, are combined to create AIERO, the Algorithm for Identifying Engineering Relationships in Ontologies. In determining its applicability and accuracy, AIERO was applied to three separate, independently developed ontologies. The results indicate AIERO is capable of consistently returning relatedness values one would intuitively expect. To assess the effectiveness of AIERO in exposing underlying causal relationships across product development platforms, a case study involving the development of an industry-inspired printed circuit board (PCB) is presented. After instantiating the PCB knowledge base and developing an initial set of rules, FIDOE, the Framework for Intelligent Distributed Ontologies in Engineering, was employed to identify additional causal relationships through extensional relatedness measurements. In a conclusive PCB redesign, the resulting “intelligent” framework demonstrates its ability to pass values between instances, identify inconsistencies amongst instantiated knowledge, and identify conflicting values within product development frameworks. The results highlight how the introduced semantic methods can enhance the current knowledge acquisition, knowledge management, and knowledge validation capabilities of traditional knowledge bases.

Enterprise Integration of Smart Objects using Semantic Service Descriptions

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

A fuzzy grassroots ontology for improving social semantic web search

The web is continuously evolving into a collection of many data, which results in the interest to collect and merge these data in a meaningful way. Based on that web data, this paper describes the building of an ontology resting on fuzzy clustering techniques. Through continual harvesting folksonomies by web agents, an entire automatic fuzzy grassroots ontology is built. This self-updating ontology can then be used for several practical applications in fields such as web structuring, web searching and web knowledge visualization.A potential application for online reputation analysis, added value and possible future studies are discussed in the conclusion.

Prometheus Framework for Fuzzy Information Retrieval in Semantic Spaces

This paper introduces a novel vision for further enhanced Internet of Things services. Based on a variety of data (such as location data, ontology-backed search queries, in- and outdoor conditions) the Prometheus framework is intended to support users with helpful recommendations and information preceding a search for context-aware data. Adapted from artificial intelligence concepts, Prometheus proposes user-readjusted answers on umpteen conditions. A number of potential Prometheus framework applications are illustrated. Added value and possible future studies are discussed in the conclusion.

Prometheus – Fuzzy Information Retrieval for Semantic Homes and Environments

This paper introduces a novel vision for further enhanced Internet of Things services. Based on a variety of data – such as location data, ontology-backed search queries, in- and outdoor conditions – the Prometheus framework is intended to support users with helpful recommendations and information preceding a search for context-aware data. Adapted from artificial intelligence concepts, Prometheus proposes user-readjusted answers on umpteen conditions. A number of potential Prometheus framework applications are illustrated. Added value and possible future studies are discussed in the conclusion.

Semantic Representation of Synaesthesia

Synaesthesia has multifaceted consequences for both subjective experience and cognitive performance. Here, I broach the issue of how synaesthesia is represented in semantic memory. I hypothesize that, for example, in grapheme colour synaesthesia, colour is represented as an additional feature in the semantic network that enables the formation of associations that are not present in non-synaesthetes. Thus, synaesthesia provokes richer memory representations which enable learning opportunities that are not present in non-synaesthetes, provides additional memory cues, and may trigger creative ideas.