Viewpoints on emergent semantics

Authors include:Philippe Cudr´e-Mauroux, and Karl Aberer (editors), Alia I. Abdelmoty, Tiziana Catarci, Ernesto Damiani, Arantxa Illaramendi, Robert Meersman, Erich J. Neuhold, Christine Parent, Kai-Uwe Sattler, Monica Scannapieco, Stefano Spaccapietra, Peter Spyns, and Guy De Tr´eWe introduce a novel view on how to deal with the problems of semantic interoperability in distributed systems. This view is based on the concept of emergent semantics, which sees both the representation of semantics and the discovery of the proper interpretation of symbols as the result of a self-organizing process performed by distributed agents exchanging symbols and having utilities dependent on the proper interpretation of the symbols. This is a complex systems perspective on the problem of dealing with semantics. We highlight some of the distinctive features of our vision and point out preliminary examples of its applicatio

Semantic component selection - SemaCS

In component based software development, project success or failure largely depends on correct software component evaluation. All available evaluation methods require time to analyse components. Due to the black box nature of components, preliminary judgments are made based on vendor descriptions. As there is no standard way of describing components, descriptions have to be interpreted using semantics and domain knowledge. This paper presents a semi-automated generic method for component identification and classification based on generic domain taxonomy and user generated semantic input. Every query is semantically tailored to what is being looked for, arriving at better results then it is currently possible using available automated categorisation systems

Endogenously Emergent Information Systems

This paper analyzes the concept of “emergence” in the context of information systems and discusses its implications to the IS research. The analysis shows that this literature as¬sumes emergence to be an outcome of exogenous, although, complex design agency, largely omitting endogenous emergence, rising from the complexity of the information system and its operational interaction with its environment. Reflecting the IS perspective, the paper reviews research on endogenous emergence conducted especially in Computer Science and Software Engineering

Query Processing in a P2P Network of Taxonomy-based Information Sources

In this study we address the problem of answering queries over a peer-to-peer system of taxonomy-based sources. A taxonomy states subsumption relationships between negation-free DNF formulas on terms and negation-free conjunctions of terms. To the end of laying the foundations of our study, we first consider the centralized case, deriving the complexity of the decision problem and of query evaluation. We conclude by presenting an algorithm that is efficient in data complexity and is based on hypergraphs. We then move to the distributed case, and introduce a logical model of a network of taxonomy-based sources. On such network, a distributed version of the centralized algorithm is then presented, based on a message passing paradigm, and its correctness is proved. We finally discuss optimization issues, and relate our work to the literature

Fault-tolerant Semantic Mappings Among Heterogeneous and Distributed Local Ontologies

ABSTRACT Overcoming semantic mapping faults, i.e. semantic incompatibility, is a vital issue for the success of semantic-based peer-to-peer systems. There are various research efforts which address the classification and the resolution of the semantic mapping fault problem, i.e. translation errors. All of the precedent research related to semantic mapping faults demonstrates one significant shortcoming. This flaw is the inability to discriminate between non-permanent and permanent semantic mapping faults, i.e. how long do semantic incompatibilities stay effective and are the semantic incompatibilities permanent or temporary? The current research examines the destructive effect of semantic mapping faults on the Emerging Semantics, i.e. bottom-up construction of ontology and proposes a solution to detect temporal semantic mapping faults. The current research also demonstrates that fault-tolerant semantic mapping will result in Emerging Semantics which are more complete and agreeable than those domain ontologies that are built without consideration for fault-tolerant semantic mapping

Semantic component selection

The means of locating information quickly and efficiently is a growing area of research. However the real challenge is not related to locating bits of information, but finding those that are relevant. Relevant information resides within unstructured ‘natural’ text. However, understanding natural text and judging information relevancy is a challenge. The challenge is partially addressed by use of semantic models and reasoning approaches that allow categorisation and (within limited fashion) provide understanding of this information. Nevertheless, many such methods are dependent on expert input and, consequently, are expensive to produce and do not scale. Although automated solutions exist, thus far, these have not been able to approach accuracy levels achievable through use of expert input. This thesis presents SemaCS - a novel nondomain specific automated framework of categorising and searching natural text. SemaCS does not rely on expert input; it is based on actual data being searched and statistical semantic distances between words. These semantic distances are used to perform basic reasoning and semantic query interpretation. The approach was tested through a feasibility study and two case studies. Based on reasoning and analyses of data collected through these studies, it can be concluded that SemaCS provides a domain independent approach of semantic model generation and query interpretation without expert input. Moreover, SemaCS can be further extended to provide a scalable solution applicable to large datasets (i.e. World Wide Web). This thesis contributes to the current body of knowledge by establishing, adapting, and using novel techniques to define a generic selection/categorisation framework. Implementing the framework outlined in the thesis improves an existing algorithm of semantic distance acquisition. Finally, as a novel approach to the extraction of semantic information is proposed, there exists a positive impact on Information Retrieval domain and, specifically, on Natural Language Processing, word disambiguation and Web/Intranet search

Semantic component selection

The means of locating information quickly and efficiently is a growing area of research. However the real challenge is not related to locating bits of information, but finding those that are relevant. Relevant information resides within unstructured ‘natural’ text. However, understanding natural text and judging information relevancy is a challenge. The challenge is partially addressed by use of semantic models and reasoning approaches that allow categorisation and (within limited fashion) provide understanding of this information. Nevertheless, many such methods are dependent on expert input and, consequently, are expensive to produce and do not scale. Although automated solutions exist, thus far, these have not been able to approach accuracy levels achievable through use of expert input. This thesis presents SemaCS - a novel nondomain specific automated framework of categorising and searching natural text. SemaCS does not rely on expert input; it is based on actual data being searched and statistical semantic distances between words. These semantic distances are used to perform basic reasoning and semantic query interpretation. The approach was tested through a feasibility study and two case studies. Based on reasoning and analyses of data collected through these studies, it can be concluded that SemaCS provides a domain independent approach of semantic model generation and query interpretation without expert input. Moreover, SemaCS can be further extended to provide a scalable solution applicable to large datasets (i.e. World Wide Web). This thesis contributes to the current body of knowledge by establishing, adapting, and using novel techniques to define a generic selection/categorisation framework. Implementing the framework outlined in the thesis improves an existing algorithm of semantic distance acquisition. Finally, as a novel approach to the extraction of semantic information is proposed, there exists a positive impact on Information Retrieval domain and, specifically, on Natural Language Processing, word disambiguation and Web/Intranet search.EThOS - Electronic Theses Online ServiceGBUnited Kingdo