A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Personalised ontology learning and mining for web information gathering

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.

Knowledge-Based Task Structure Planning for an Information Gathering Agent

An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed.

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment.

Unsupervised multi-label text classification using a world knowledge ontology

The development of text classification techniques has been largely promoted in the past decade due to the increasing availability and widespread use of digital documents. Usually, the performance of text classification relies on the quality of categories and the accuracy of classifiers learned from samples. When training samples are unavailable or categories are unqualified, text classification performance would be degraded. In this paper, we propose an unsupervised multi-label text classification method to classify documents using a large set of categories stored in a world ontology. The approach has been promisingly evaluated by compared with typical text classification methods, using a real-world document collection and based on the ground truth encoded by human experts.

A System for Representing and Using Real-World Knowledge

This report describes a knowledge-base system in which the information is stored in a network of small parallel processing elements ??de and link units ??ich are controlled by an external serial computer. This network is similar to the semantic network system of Quillian, but is much more tightly controlled. Such a network can perform certain critical deductions and searches very quickly; it avoids many of the problems of current systems, which must use complex heuristics to limit and guided their searches. It is argued (with examples) that the key operation in a knowledge-base system is the intersection of large explicit and semi-explicit sets. The parallel network system does this in a small, essentially constant number of cycles; a serial machine takes time proportional to the size of the sets, except in special cases.

A revision of Evaniscus (Hymenoptera, Evaniidae) using ontology-based semantic phenotype annotation

The Neotropical evaniid genus Evaniscus Szepligeti currently includes six species. Two new species are described, Evaniscus lansdownei Mullins, sp. n. from Colombia and Brazil and E. rafaeli Kawada, sp. n. from Brazil. Evaniscus sulcigenis Roman, syn. n., is synonymized under E. rufithorax Enderlein. An identification key to species of Evaniscus is provided. Thirty-five parsimony informative morphological characters are analyzed for six ingroup and four outgroup taxa. A topology resulting in a monophyletic Evaniscus is presented with E. tibialis and E. rafaeli as sister to the remaining Evaniscus species. The Hymenoptera Anatomy Ontology and other relevant biomedical ontologies are employed to create semantic phenotype statements in Entity-Quality (EQ) format for species descriptions. This approach is an early effort to formalize species descriptions and to make descriptive data available to other domains.

Dynamics of Traditional Ecological Knowledge, Local Use and Conservation of Native Trees and Shrubs in the Bolivian Andes

Native trees and shrubs are essential components of rural landscapes in the semi-arid inner-Andean valleys of Bolivia. They can be found as hedges and bushes in various agroecosystems such as terrace walls, slopes, field boundaries and fallow land. Their distribution and floristic composition are the result of dynamic spatial and temporal interactions between local farmers and the environment. Local uses of natural resources and biodiversity reflect the constantly evolving Andean culture, which can be generally characterised as an intertwining of the human, natural, and spiritual worlds. The aim of the present ethnobotanical study was to analyse the dynamics of traditional ecological knowledge, to ascertain local farmers’ perceptions and uses of native woody species in Andean communities and to associate the results with local conservation activities for the trees and shrubs concerned. Our case study was carried out within two communities of the Tunari National Park (Dept. Cochabamba) in Bolivia. For data collection, research methods from social science (semi-structured interviews, participative observation, participatory mapping) as well as vegetation surveys were combined. Local actors included women and men of all ages as well as families from different social categories and altitudinal levels of permanent residence. Our study indicates that, due to a multitude of socio-economic pressures (e.g. migration of young people) as well as changes in use of biodiversity (e.g. replacement of native by exotic introduced species), the traditional ecological knowledge base of native trees and shrubs and their respective uses has become diminished over time. In many cases it has led to a decline in people’s awareness of native species and as a consequence their practical, emotional and spiritual relationships with them have been lost. However, results also show that applied traditional ecological knowledge has led to local conservation strategies, which have succeeded in protecting those tree and shrub species which are most widely regarded for their multifunctional, constant and exclusive uses (e.g. Schinus molle, Prosopis laevigata, Baccharis dracunculifolia). The presentation will discuss the question if and how applied traditional ecological knowledge positively contributes to local initiatives of sustainable use and conservation of biodiversity in rural areas.

World knowledge in the process of translation

The paper illustrates the role of world knowledge in comprehending and translating texts. A short news item, which displays world knowledge fairly implicitly in condensed lexical forms, was translated by students from English into German. It is shown that their translation strategies changed from a first draft which was rather close to the surface structure of the source text to a final version which took situational aspects, texttypological conventions and the different background knowledge of the respective addressees into account. Decisions on how much world knowledge has to be made explicit in the target text, however, must be based on the relevance principle. Consequences for teaching and for the notions of semantic knowledge and world knowledge are discussed.

Semantic annotation for knowledge management:requirements and a survey of the state of the art

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.

Automatic Discovery of Word Semantic Relations

In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal di®ers from previous research as it tries to take the best of two different methodologies i.e. semantic space models and information extraction models. It can be applied to extract close semantic relations, it limits the search space and it is unsupervised.

Semantic relations: Modelling issues, proposals and possible applications

Semantic relations are an important element in the construction of ontology-based linguistic resources and models of problem domains. Nevertheless, they remain under-specified. This is a pervasive problem in both Software Engineering and Artificial Intelligence. Thus, we find semantic links that can have multiple interpretations, abstractions that are not enough to represent the relation richness of problem domains, and even poorly structured taxonomies. However, if provided with precise semantics, some of these problems can be avoided, and meaningful operations can be performed on them that can be an aid in the ontology construction process. In this paper we present some insightful issues about the representation of relations. Moreover, the initiatives aiming to provide relations with clear semantics are explained and the inclusion of their core ideas as part of a methodology for the development of ontology-based linguistic resources is proposed.