Victoria S. Uren

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

While much of a companys 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.

SemSearch: a search engine for the semantic web

Existing semantic search tools have been primarily designed to enhance the performance of traditional search technologies but with little support for ordinary end users who are not necessarily familiar with domain specific semantic data, ontologies, or SQL-like query languages. This paper presents SemSearch, a search engine, which pays special attention to this issue by providing several means to hide the complexity of semantic search from end users and thus make it easy to use and effective.

AquaLog: An ontology-driven question answering system for organizational semantic intranets

The semantic web vision is one in which rich, ontology-based semantic markup will become widely available. The availability of semantic markup on the web opens the way to novel, sophisticated forms of question answering. AquaLog is a portable question-answering system which takes queries expressed in natural language and an ontology as input, and returns answers drawn from one or more knowledge bases (KBs). We say that AquaLog is portable because the configuration time required to customize the system for a particular ontology is negligible. AquaLog presents an elegant solution in which different strategies are combined together in a novel way. It makes use of the GATE NLP platform, string metric algorithms, WordNet and a novel ontology-based relation similarity service to make sense of user queries with respect to the target KB. Moreover it also includes a learning component, which ensures that the performance of the system improves over the time, in response to the particular community jargon used by end users.

Is question answering fit for the semantic web?: a survey

Abstract. With the recent rapid growth of the Semantic Web (SW), the processes of searching and querying content that is both massive in scale and heterogeneous have become increasingly challenging. User-friendly interfaces, which can support end users in querying and exploring this novel and diverse, structured information space, are needed to make the vision of the SW a reality. We present a survey on ontology-based Question Answering (QA), which has emerged in recent years to exploit the opportunities offered by structured semantic information on the Web. First, we provide a comprehensive perspective by analyzing the general background and history of the QA research field, from influential works from the artificial intelligence and database communities developed in the 70s and later decades, through open domain QA stimulated by the QA track in TREC since 1999, to the latest commercial semantic QA solutions, before tacking the current state of the art in open userfriendly interfaces for the SW. Second, we examine the potential of this technology to go beyond the current state of the art to support end-users in reusing and querying the SW content. We conclude our review with an outlook for this novel research area, focusing in particular on the R&D directions that need to be pursued to realize the goal of efficient and competent retrieval and integration of answers from large scale, heterogeneous, and continuously evolving semantic sources.

Sensemaking tools for understanding research literatures: Design, implementation and user evaluation

This paper describes the work undertaken in the Scholarly Ontologies Project. The aim of the project has been to develop a computational approach to support scholarly sensemaking, through interpretation and argumentation, enabling researchers to make claims: to describe and debate their view of a documents key contributions and relationships to the literature. The project has investigated the technicalities and practicalities of capturing conceptual relations, within and between conventional documents in terms of abstract ontological structures. In this way, we have developed a new kind of index to distributed digital library systems. This paper reports a case study undertaken to test the sensemaking tools developed by the Scholarly Ontologies project. The tools used were ClaiMapper, which allows the user to sketch argument maps of individual papers and their connections, ClaiMaker, a server on which such models can be stored and saved, which provides interpretative services to assist the querying of argument maps across multiple papers and ClaimFinder, a novice interface to the search services in ClaiMaker.

Comparing dissimilarity measures for content-based image retrieval

Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measures retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection. From our experimental results, we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval, which will lay a foundation for developing more effective image search technologies.

Semantic Search Meets the Web

While semantic search technologies have been proven to work well in specific domains, they still have to confront two main challenges to scale up to the Web in its entirety. In this work we address this issue with a novel semantic search system that a) provides the user with the capability to query Semantic Web information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance keyword search technologies.

Building and applying a concept hierarchy representation of a user profile

Term dependence is a natural consequence of language use. Its successful representation has been a long standing goal for Information Retrieval research. We present a methodology for the construction of a concept hierarchy that takes into account the three basic dimensions of term dependence. We also introduce a document evaluation function that allows the use of the concept hierarchy as a user profile for Information Filtering. Initial experimental results indicate that this is a promising approach for incorporating term dependence in the way documents are filtered.

Cross ontology query answering on the semantic web: an initial evaluation

PowerAqua is a Question Answering system, which takes as input a natural language query and is able to return answers drawn from relevant semantic resources found anywhere on the Semantic Web. In this paper we provide two novel contributions: First, we detail a new component of the system, the Triple Similarity Service, which is able to match queries effectively to triples found in different ontologies on the Semantic Web. Second, we provide a first evaluation of the system, which in addition to providing data about PowerAquas competence, also gives us important insights into the issues related to using the Semantic Web as the target answer set in Question Answering. In particular, we show that, despite the problems related to the noisy and incomplete conceptualizations, which can be found on the Semantic Web, good results can already be obtained.

PowerAqua: fishing the semantic web

The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources.