The Semantics and Typology of Yes/No Particles (A Cross-Linguistic Study)

Taking the three basic systems of Yes/No particles the group looked at the relative deep and surface structures, and asked what types of systems are present in the Georgian, Polish and Armenian languages. The choice of languages was of particular interest as the Caucasian and Indo-European languages usually have different question-answering systems, but Georgian (Caucasian) and Polish (Indo-European) in fact share the same system. The Armenian language is Indo-European, but the country is situated in the southern Caucasus, on Georgia's southern border, making it worth analysing Armenian in comparison with Georgian (from the point of view of language interference) and with Polish (as two relative languages). The group identified two different deep structures, tracing the occurrence of these in different languages, and showed that one is more natural in the majority of languages. They found no correspondence between relative languages and their question-answer systems and demonstrated that languages in the same typological class may show different systems, as with Georgian and the North Caucasian languages. It became clear that Georgian, Armenian and Polish all have an agree/disagree question-answering system defined by the same deep structure. From this they conclude that the lingual mentalities of Georgians, Armenians and Poles are more oriented to the communicative act. At the same time the Yes/No system, in which a positive particle stands for a positive answer and a negative particle for a negative answer, also functions in these languages, indicating that the second deep structure identified also functions alongside the first.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

Managing Things and Services with Semantics: A Survey

This paper presents a survey on the usage, opportunities and pitfalls of semantic technologies in the Internet of Things. The survey was conducted in the context of a semantic enterprise integration platform. In total we surveyed sixty-one individuals from industry and academia on their views and current usage of IoT technologies in general, and semantic technologies in particular. Our semantic enterprise integration platform aims for interoperability at a service level, as well as at a protocol level. Therefore, also questions regarding the use of application layer protocols, network layer protocols and management protocols were integrated into the survey. The survey suggests that there is still a lot of heterogeneity in IoT technologies, but first indications of the use of standardized protocols exist. Semantic technologies are being recognized as of potential use, mainly in the management of things and services. Nonetheless, the participants still see many obstacles which hinder the widespread use of semantic technologies: Firstly, a lack of training as traditional embedded programmers are not well aware of semantic technologies. Secondly, a lack of standardization in ontologies, which would enable interoperability and thirdly, a lack of good tooling support.

Collective Referential Intentionality in the Semantics of Dialogue

The concept of a dialogue is considered in general terms from the standpoint of its referential presuppositions. The semantics of dialogue implies that dialogue participants must generally have a collective intentionality of agreed-upon references that is minimally sufficient for them to be able to disagree about other things, and ideally for outstanding disagreements to become clearer at successive stages of the dialogue. These points are detailed and illustrated in a fictional dialogue, in which precisely these kinds of referential confusions impede progress in shared understanding. It is only through a continuous exchange of question and answer in this dialogue case study that the meanings of key terms and anaphorical references are disambiguated, and a relevantly complete collective intentionality of shared meaning between dialogue participants is achieved. The importance of a minimally shared referential semantics for the terms entering into reasoning and argument in dialogue contexts broadly construed cannot be over-estimated. Where to draw the line between referential agreement and disagreement within any chosen dialogue, as participants work toward better mutual understanding in clearing up referential incongruities, is sometimes among the dialogue’s main points of dispute.