Associative and Spatial Relationships in Thesaurus-based Retrieval

The OASIS (Ontologically Augmented Spatial Information System) project explores terminology systems for thematic and spatial access in digital library applications. A prototype implementation uses data from the Royal Commission on the Ancient and Historical Monuments of Scotland, together with the Getty AAT and TGN thesauri. This paper describes its integrated spatial and thematic schema and discusses novel approaches to the application of thesauri in spatial and thematic semantic distance measures. Semantic distance measures can underpin interactive and automatic query expansion techniques by ranking lists of candidate terms. We first illustrate how hierarchical spatial relationships can be used to provide more flexible retrieval for queries incorporating place names in applications employing online gazetteers and geographical thesauri. We then employ a set of experimental scenarios to investigate key issues affecting use of the associative (RT) thesaurus relationships in semantic distance measures. Previous work has noted the potential of RTs in thesaurus search aids but the problem of increased noise in result sets has been emphasised. Specialising RTs allows the possibility of dynamically linking RT type to query context. Results presented in this paper demonstrate the potential for filtering on the context of the RT link and on subtypes of RT relationships

Initiating organizational memories using ontology-based network analysis as a bootstrapping tool

An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

Knowledge management support for enterprise distributed systems

Explosion of information and increasing demands on semantic processing web applications have software systems to their limits. To address the problem we propose a semantic based formal framework (ADP) that makes use of promising technologies to enable knowledge generation and retrieval. We argue that this approach is cost effective, as it reuses and builds on existing knowledge and structure. It is also a good starting point for creating an organisational memory and providing knowledge management functions

Initiating organizational memories using ontology network analysis

One of the important problems in organizational memories is their initial set-up. It is difficult to choose the right information to include in an organizational memory, and the right information is also a prerequisite for maximizing the uptake and relevance of the memory content. To tackle this problem, most developers adopt heavy-weight solutions and rely on a faithful continuous interaction with users to create and improve its content. In this paper, we explore the use of an automatic, light-weight solution, drawn from the underlying ingredients of an organizational memory: ontologies. We have developed an ontology-based network analysis method which we applied to tackle the problem of identifying communities of practice in an organization. We use ontology-based network analysis as a means to provide content automatically for the initial set up of an organizational memory

MetaNet: a metadata term thesaurus to enable semantic interoperability between metadata domains

Metadata interoperability is a fundamental requirement for access to information within networked knowledge organization systems. The Harmony International Digital Library Project [1] has developed a common underlying data model (the ABC model) to enable the scalable mapping of metadata descriptions across domains and media types. The ABC model, described in [2], provides a set of basic building blocks for metadata modeling and recognizes the importance of 'events' to unambiguously describe metadata for objects with a complex history. In order to test and evaluate the interoperability capabilities of this model, we applied it to some real multimedia examples and analysed the results of mapping from the ABC model to various different metadata domains using XSLT [3]. This work revealed serious limitations in XSLT's ability to support flexible dynamic semantic mapping. In order to overcome this, we developed MetaNet [4], a metadata term thesaurus which provides the additional semantic knowledge which is non-existent within declarative XML-encoded metadata descriptions. This paper describes MetaNet, its RDF Schema [5] representation and a hybrid mapping approach which combines the structural and syntactic mapping capabilities of XSLT with the semantic knowledge of MetaNet, to enable flexible and dynamic mapping among metadata standards

Semantic Search Using a Similarity Graph

Given a set of documents and an input query that is expressed in a natural language, the problem of document search is retrieving the most relevant documents. Unlike most existing systems that perform document search based on keywords matching, we propose a search method that considers the meaning of the words in the query and the document. As a result, our algorithm can return documents that have no words in common with the input query as long as the documents are relevant. For example, a document that contains the words “Ford”, “Chrysler” and “General Motors” multiple times is surely relevant for the query “car” even if the word “car” does not appear in the document. Our semantic search algorithm is based on a similarity graph that contains the degree of semantic similarity between terms, where a term can be a word or a phrase. We experimentally validate our algorithm on the Cranfield benchmark that contains 1400 documents and 225 natural language queries. The benchmark also contains the relevant documents for every query as determined by human judgment. We show that our semantic search algorithm produces a higher value for the mean average precision (MAP) score than a keywords matching algorithm. This shows that our approach can improve the quality of the result because the meaning of the words and phrases in the documents and the queries is taken into account

The expression of active structural network in intellectual environment design

[[abstract]]"This essay proposes a combination of commonsense knowledge and the spreading activation theory to strengthen the breadth of spreading activation of user’s behavioral information in the field of intelligent environment design. The ontology cited in traditional artificial intelligence is roughly used to express and share knowledge. It is described with concepts, the properties of concepts, and the relationships among concepts, and is mostly presented with the treestructured classification. However, there are often only vertical subordinate relationships between concepts with no horizontal and cross-layer interactions. Hence, we employ the constrained spreading activation model to perform a series of algorithms, further constructing the knowledge rules which can be commonly utilized to reinforce the breadth of activation process of user’s behavioral information in an intelligent environment. These rules can provide a reference to the network service of digitalized cities in the future, and can also support the simulation assessment of applicability of intelligent environment design by employing embedded interactive technology.

Semantic Document Clustering Using a Similarity Graph

Document clustering addresses the problem of identifying groups of similar documents without human supervision. Unlike most existing solutions that perform document clustering based on keywords matching, we propose an algorithm that considers the meaning of the terms in the documents. For example, a document that contains the words dog and cat multiple times may be placed in the same category as a document that contains the word pet even if the two documents share only noise words in common. Our semantic clustering algorithm is based on a similarity graph that stores the degree of semantic relationship between terms (extracted from WordNet), where a term can be a word or a phrase. We experimentally validate our algorithm on the Reuters-21578 benchmark, which contains 11,362 newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keywords matching and one that uses the similarity graph. We show that the second approach produces higher precision and recall, which means that this approach matches closer the results of the human study