Spatial groundings for meaningful symbols

The increasing availability of ontologies raises the need to establish relationships and make inferences across heterogeneous knowledge models. The approach proposed and supported by knowledge representation standards consists in establishing formal symbolic descriptions of a conceptualisation, which, it has been argued, lack grounding and are not expressive enough to allow to identify relations across separate ontologies. Ontology mapping approaches address this issue by exploiting structural or linguistic similarities between symbolic entities, which is costly, error-prone, and in most cases lack cognitive soundness. We argue that knowledge representation paradigms should have a better support for similarity and propose two distinct approaches to achieve it. We first present a representational approach which allows to ground symbolic ontologies by using Conceptual Spaces (CS), allowing for automated computation of similarities between instances across ontologies. An alternative approach is presented, which considers symbolic entities as contextual interpretations of processes in spacetime or Differences. By becoming a process of interpretation, symbols acquire the same status as other processes in the world and can be described (tagged) as well, which allows the bottom-up production of meaning

A formal model for fuzzy ontologies.

Au Yeung Ching Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 97-110).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Semantic Web and Ontologies --- p.3Chapter 1.2 --- Motivations --- p.5Chapter 1.2.1 --- Fuzziness of Concepts --- p.6Chapter 1.2.2 --- Typicality of Objects --- p.6Chapter 1.2.3 --- Context and Its Effect on Reasoning --- p.8Chapter 1.3 --- Objectives --- p.9Chapter 1.4 --- Contributions --- p.10Chapter 1.5 --- Structure of the Thesis --- p.11Chapter 2 --- Background Study --- p.13Chapter 2.1 --- The Semantic Web --- p.14Chapter 2.2 --- Ontologies --- p.16Chapter 2.3 --- Description Logics --- p.20Chapter 2.4 --- Fuzzy Set Theory --- p.23Chapter 2.5 --- Concepts and Categorization in Cognitive Psychology --- p.25Chapter 2.5.1 --- Theory of Concepts --- p.26Chapter 2.5.2 --- Goodness of Example versus Degree of Typicality --- p.28Chapter 2.5.3 --- Similarity between Concepts --- p.29Chapter 2.5.4 --- Context and Context Effects --- p.31Chapter 2.6 --- Handling of Uncertainty in Ontologies and Description Logics --- p.33Chapter 2.7 --- Typicality in Models for Knowledge Representation --- p.35Chapter 2.8 --- Semantic Similarity in Ontologies and the Semantic Web --- p.39Chapter 2.9 --- Contextual Reasoning --- p.41Chapter 3 --- A Formal Model of Ontology --- p.44Chapter 3.1 --- Rationale --- p.45Chapter 3.2 --- Concepts --- p.47Chapter 3.3 --- Characteristic Vector and Property Vector --- p.47Chapter 3.4 --- Subsumption of Concepts --- p.49Chapter 3.5 --- Likeliness of an Individual in a Concept --- p.51Chapter 3.6 --- Prototype Vector and Typicality --- p.54Chapter 3.7 --- An Example --- p.59Chapter 3.8 --- Similarity between Concepts --- p.61Chapter 3.9 --- Context and Contextualization of Ontology --- p.65Chapter 3.9.1 --- Formal Definitions --- p.67Chapter 3.9.2 --- Contextualization of an Ontology --- p.69Chapter 3.9.3 --- "Contextualized Subsumption Relations, Likeliness, Typicality and Similarity" --- p.71Chapter 4 --- Discussions and Analysis --- p.73Chapter 4.1 --- Properties of the Formal Model for Fuzzy Ontologies --- p.73Chapter 4.2 --- Likeliness and Typicality --- p.78Chapter 4.3 --- Comparison between the Proposed Model and Related Works --- p.81Chapter 4.3.1 --- Comparison with Traditional Ontology Models --- p.81Chapter 4.3.2 --- Comparison with Fuzzy Ontologies and DLs --- p.82Chapter 4.3.3 --- Comparison with Ontologies modeling Typicality of Objects --- p.83Chapter 4.3.4 --- Comparison with Ontologies modeling Context --- p.84Chapter 4.3.5 --- Limitations of the Proposed Model --- p.85Chapter 4.4 --- "Significance of Modeling Likeliness, Typicality and Context in Ontologies" --- p.86Chapter 4.5 --- Potential Application of the Model --- p.88Chapter 4.5.1 --- Searching in the Semantic Web --- p.88Chapter 4.5.2 --- Benefits of the Formal Model of Ontology --- p.90Chapter 5 --- Conclusions and Future Work --- p.91Chapter 5.1 --- Conclusions --- p.91Chapter 5.2 --- Future Research Directions --- p.93Publications --- p.96Bibliography --- p.9

On the similarity relation within fuzzy ontology components

Ontology reuse is an important research issue. Ontology merging, integration, mapping, alignment and versioning are some of its subprocesses. A considerable research work has been conducted on them. One common issue to these subprocesses is the problem of defining similarity relations among ontologies components. Crisp ontologies become less suitable in all domains in which the concepts to be represented have vague, uncertain and imprecise definitions. Fuzzy ontologies are developed to cope with these aspects. They are equally concerned with the problem of ontology reuse. Defining similarity relations within fuzzy context may be realized basing on the linguistic similarity among ontologies components or may be deduced from their intentional definitions. The latter approach needs to be dealt with differently in crisp and fuzzy ontologies. This is the scope of this paper.ou

Time Aware Knowledge Extraction for Microblog Summarization on Twitter

Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.Comment: 33 pages, 10 figure