Proceedings of the 12th European Conference on Knowledge ...

Taha Osman, Dhavalkumar Thakker and Matt Nathan
on <strong>the</strong> analysis <strong>of</strong> <strong>the</strong>se requirements we can, for instance, make a critical decision that proactive
intelligent browsing or faceted search (Suominen, 2007) is a more appropriate method for semantic
retrieval as opposed to explicit query-based search, and we can also strike a balance between
automated bootstrapping <strong>of</strong> our knowledgebase with information from public semantic datasets and
<strong>the</strong> manual verification <strong>of</strong> machine-learned facts for enriching <strong>the</strong> knowledgebase.
The rest <strong>of</strong> <strong>the</strong> paper is organised as follows: section 2 introduces <strong>the</strong> topic <strong>of</strong> semantically driven
knowledge management, followed by explanation <strong>of</strong> <strong>the</strong> case study that motivates this research in
section 3. Section 4 discusses <strong>the</strong> requirement analysis for <strong>the</strong> investigated commercial semantic
retrieval application, while section 5 explores our proposed roadmap to integrating information
extraction, ontology engineering, and knowledge management technologies to address <strong>the</strong>se
requirements. Finally section 6 concludes <strong>the</strong> paper and presents future challenges.
2. Overview <strong>of</strong> semantic knowledge management
The main distinction <strong>of</strong> semantic-based over traditional knowledgebase systems is <strong>the</strong> requirement for
a meta-data layer that provides for machine-level understanding <strong>of</strong> <strong>the</strong> knowledge representation.
This layer, known in <strong>the</strong> Semantic web terminology as Ontology, comprises taxonomy <strong>of</strong> entity types,
<strong>the</strong>ir attributes, and relationships between <strong>the</strong>m, described using an open standard (formalism).
Hence <strong>the</strong> ontology represents a schema that is used to semantically annotate domain entities and
<strong>the</strong>ir interrelations. These annotations represent <strong>the</strong> formal body <strong>of</strong> knowledge about <strong>the</strong> entities
traditionally known as a knowledgebase (KB). The machine-understandable semantic knowledgebase
can be autonomously reasoned by s<strong>of</strong>tware agents in order to map storage and retrieval queries to
<strong>the</strong> annotated information.
The standardisation <strong>of</strong> Semantic web formalism provides for linking to and using knowledge held at
o<strong>the</strong>r information repositories to bootstrap and/or enrich <strong>the</strong> knowledgebase. A prime example <strong>of</strong> <strong>the</strong>
latter is <strong>the</strong> direct links at <strong>the</strong> ontology (schema) and data (information instances) levels between
semantic datasets in <strong>the</strong> Linked Open Data Cloud project (Hausenblas, 2009). These advantages
enthused <strong>the</strong> utilisation <strong>of</strong> semantic technologies for knowledgebase management in a variety <strong>of</strong>
fields. For instance, The SEKT project at British Telecom investigates <strong>the</strong> utilisation <strong>of</strong> semantic
knowlegebases and semantic search for improving <strong>the</strong> end-user experience while browsing scientific
and business articles (Warren, 2005). A similar effort was reported in (Zhou, 2004), where semanticsbased
computer-aided manufacturing knowledge management systems was deployed to support
knowledge-intensive manufacturing processes. Semantic manufacturing knowledge management
provides a new solution for meeting <strong>the</strong> requirements in such environments such as knowledge
accuracy, lower knowledge cost, knowledge timeliness and knowledge unification.
Reviewing semantic-based knowledge management cannot be concluded without drawing attention to
<strong>the</strong> problem <strong>of</strong> semantic annotation. Semantic annotation is a labour-intensive process, <strong>the</strong> cost <strong>of</strong>
which can be especially prohibitive for organisations that regularly add large amounts <strong>of</strong> data to <strong>the</strong>ir
repositories. Hence, information extraction (IE) technologies are <strong>of</strong>ten utilised to automate <strong>the</strong> process
<strong>of</strong> annotating unstructured textual data (documents, image captions, web pages, etc.) with semantic
descriptions (Osman, 2007).
3. Case study: utilising semantic knowledge management in information
retrieval applications
This study is motivated by <strong>the</strong> authors’ involvement in a project aiming to utilise semantic web
technologies to improve <strong>the</strong> image browsing experience for customers <strong>of</strong> PA Images, <strong>the</strong> photography
arm <strong>of</strong> <strong>the</strong> Press Association (PA) - <strong>the</strong> UK’s leading multimedia news and information provider and
supplier <strong>of</strong> business-to-business media services.
The Press Association’s current digital archive holds about 7.5 million images, which is growing by an
average <strong>of</strong> 35.000 new images per week. The images are supplied by <strong>the</strong> company’s own
photographers and subcontracted photo agencies around <strong>the</strong> world. However, currently PA Images’
customers can only interact with <strong>the</strong> images repository via an on-line free-text search <strong>of</strong> manually
annotated image captions which does not deliver <strong>the</strong> level <strong>of</strong> recall and accuracy that does justice to
PA’s rich repository <strong>of</strong> images. Hence, PA Images is investing in <strong>the</strong> utilization <strong>of</strong> semantic web
technologies in a bid to improve <strong>the</strong> image retrieval experience for <strong>the</strong>ir customers. The core
component <strong>of</strong> any semantic-based intelligent retrieval mechanism is <strong>the</strong> semantic knowledgebase,
which is <strong>the</strong> focal point <strong>of</strong> this investigation. The objective <strong>of</strong> <strong>the</strong> documented research in this paper is
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