Ontologies have been criticized because they are not sufficiently flexible, and thus cannot capture the dynamism and complexity of reality. However, they have increasingly come into focus because of the need for knowledge management in both general and specialized knowledge domains. EcoLexicon is a frame-based visual thesaurus on the environment that is gradually evolving towards the status of a formal ontology. For this purpose, the information in its relational database is in the process of being linked to the ontological system of FunGramKB, a multipurpose knowledge base that has been specifically designed for natural language understanding with modules for lexical, grammatical, and conceptual knowledge. This enables the explicitation of specialized knowledge as an extension of general knowledge through its representation in the domain-specific satellite ontology of a main general ontology.

This paper describes preliminary research in the use of ontological knowledge for the task of automatically classifying classical Chinese poetry (CCCP) according to authorship. Based on a collection of poems written by Liu Yong (987–1053 AD) and Su Shi (1037– 1101 AD), which have been analyzed according to a taxonomy of ontological entities at the lexical level, the research looks into the issue of whether characteristic features can be automatically extracted as important stylistic differences between the two poets. This paper examines the efficiency of different ontological concepts as features in CCCP using Support Vector Machine (SVMs). The experiment shows that an integration of ontological knowledge and bags-of-words (BoW) produces a higher precision for CCCP than BoW only with an overall increase of 2.1% and 2.2% in terms of precision and F-score.

In this paper we introduce a system that is designed to automatically populate a knowledge base from both structured and unstructured text given an ontology. Our system is designed as a modular end-to-end system that takes structured or unstructured data as input, extracts information, maps relevant information to an ontology, and finally disambiguates entities in the knowledge base. The novelty of our approach is that it is domain independent and can easily be adapted to new ontologies and domains. Unlike most knowledge base population systems, ours includes entity detection. This feature allows one to employ very complex ontologies that include events and the entities that are involved in the events.

Linguistic and semantic annotations are important features for text-based applications. However, achieving and maintaining a good quality of a set of annotations is known to be a complex task. Many ad hoc approaches have been developed to produce various types of annotations, while comparing those annotations to improve their quality is still rare. In this paper, we propose a framework in which both linguistic and domain information can cooperate to reason with annotations. The underlying knowledge representation issues are carefully analyzed and solved by studying a higher order logic, which accounts for the cooperation of different sorts of knowledge. Our prototype implements this logic based on a reduction to classical description logics by preserving the semantics, allowing us to benefit from cutting-edge Semantic Web reasoners. An application scenario shows interesting merits of this framework on reasoning with annotations of texts.

In this paper we propose an ontological framework for tools facilitating creative writing and story reading. It is based on an ontology implemented as a topic map and employs linguistic analysis methods for discovering conceptual entities in the text.

One of the key challenges in electronic government (e-government) is the development of systems that can be easily integrated and interoperated to provide seamless services delivery to citizens. In recent years, Semantic Web technologies based on ontology have emerged as promising solutions to the above engineering problems. However, current research practicing semantic development in e-government does not focus on the application of available methodologies and platforms for developing government domain ontologies. Furthermore, only a few of these researches provide detailed guidelines for developing semantic ontology models from a government service domain. This research presents a case study combining an ontology building methodology and two state-of-the-art Semantic Web platforms namely Protege and Java Jena ontology API for semantic ontology development in e-government. Firstly, a framework adopted from the Uschold and King ontology building methodology is employed to build a domain ontology describing the semantic content of a government service domain. Thereafter, UML is used to semi-formally represent the domain ontology. Finally, Protege and Jena API are employed to create the Web Ontology Language (OWL) and Resource Description Framework (RDF) representations of the domain ontology respectively to enable its computer processing. The study aims at: (1) providing e-government developers, particularly those from the developing world with detailed guidelines for practicing semantic content development in their e-government projects and (2), strengthening the adoption of semantic technologies in e-government. The study would also be of interest to novice Semantic Web developers who might used it as a starting point for further investigations.

Users who need several queries before finding what they need can benefit from an automatic search assistant that provides feedback on their query modification strategies. We present a method to learn from a search log which types of query modifications have and have not been effective in the past. The method analyses query modifications along two dimensions: a traditional term-based dimension and a semantic dimension, for which queries are enriches with linked data entities. Applying the method to the search logs of two search engines, we identify six opportunities for a query modification assistant to improve search: modification strategies that are commonly used, but that often do not lead to satisfactory results.

With the growing popularity of Social Web applications, more and more user data is published on the Web everyday. Our research focuses on investigating ways of mining data from such platforms that can be used for modeling users and for semantically augmenting user profiles. This process can enhance adaptation and personalization in various adaptive Web-based systems. In this paper, we present the U-Sem people modeling service, a framework for the semantic enrichment and mining of people's profiles from usage data on the Social Web. We explain the architecture of our people modeling service and describe its application in an adult e-learning context as an example. Versions: Mar 21, 10:10, Mar 25, 09:37

Temporal encoding schemes using RDF and OWL are often plagued by a massive proliferation of useless "container" objects. Reasoning and querying with such representations is extremely complex, expensive, and error-prone. We present a temporal extension of the Hayes and ter Horst entailment rules for RDFS/OWL. The extension is realized by extending RDF triples with further temporal arguments and requires only some lightweight forms of reasoning. The approach has been implemented in the forward chaining engine HFC.

We present an approach to integrating rules and ontologies on the basis of the first-order stable model semantics proposed by Ferraris, Lee and Lifschitz. We show that some existing integration proposals can be uniformly reformulated in terms of the first-order stable model semantics. The reformulations are simpler than the original proposals in the sense that they do not refer to grounding.