3,704 research outputs found
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches
An infrastructure for building semantic web portals
In this paper, we present our KMi semantic web portal infrastructure, which supports two important tasks of semantic web portals, namely metadata extraction and data querying. Central to our infrastructure are three components: i) an automated metadata extraction tool, ASDI, which supports the extraction of high quality metadata from heterogeneous sources, ii) an ontology-driven question answering tool, AquaLog, which makes use of the domain specific ontology and the semantic metadata extracted by ASDI to answers questions in natural language format, and iii) a semantic search engine, which enhances traditional
text-based searching by making use of the underlying ontologies and the extracted metadata. A semantic web portal application has been built, which illustrates the usage of this infrastructure
From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data
We compare two distinct approaches for querying data in the context of the
life sciences. The first approach utilizes conventional databases to store the
data and intuitive form-based interfaces to facilitate easy querying of the
data. These interfaces could be seen as implementing a set of "pre-canned"
queries commonly used by the life science researchers that we study. The second
approach is based on semantic Web technologies and is knowledge (model) driven.
It utilizes a large OWL ontology and same datasets as before but associated as
RDF instances of the ontology concepts. An intuitive interface is provided that
allows the formulation of RDF triples-based queries. Both these approaches are
being used in parallel by a team of cell biologists in their daily research
activities, with the objective of gradually replacing the conventional approach
with the knowledge-driven one. This provides us with a valuable opportunity to
compare and qualitatively evaluate the two approaches. We describe several
benefits of the knowledge-driven approach in comparison to the traditional way
of accessing data, and highlight a few limitations as well. We believe that our
analysis not only explicitly highlights the specific benefits and limitations
of semantic Web technologies in our context but also contributes toward
effective ways of translating a question in a researcher's mind into precise
computational queries with the intent of obtaining effective answers from the
data. While researchers often assume the benefits of semantic Web technologies,
we explicitly illustrate these in practice
CROEQS: Contemporaneous Role Ontology-based Expanded Query Search: implementation and evaluation
Searching annotated items in multimedia databases becomes increasingly important. The traditional approach is to build a search engine based on textual metadata. However, in manually annotated multimedia databases, the conceptual level of what is searched for might differ from the high-levelness of the annotations of the items. To address this problem, we present CROEQS, a semantically enhanced search engine. It allows the user to query the annotated persons not only on their name, but also on their roles at the time the multimedia item was broadcast. We also present the ontology used to expand such queries: it allows us to semantically represent the domain knowledge on people fulfilling a role during a temporal interval in general, and politicians holding a political office specifically. The evaluation results show that query expansion using data retrieved from an ontology considerably filters the result set, although there is a performance penalty
A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs
While contemporary semantic search systems offer to improve classical
keyword-based search, they are not always adequate for complex domain specific
information needs. The domain of prescription drug abuse, for example, requires
knowledge of both ontological concepts and 'intelligible constructs' not
typically modeled in ontologies. These intelligible constructs convey essential
information that include notions of intensity, frequency, interval, dosage and
sentiments, which could be important to the holistic needs of the information
seeker. We present a hybrid approach to domain specific information retrieval
(or knowledge-aware search) that integrates ontology-driven query
interpretation with synonym-based query expansion and domain specific rules, to
facilitate search in social media. Our framework is based on a context-free
grammar (CFG) that defines the query language of constructs interpretable by
the search system. The grammar provides two levels of semantic interpretation:
1) a top-level CFG that facilitates retrieval of diverse textual patterns,
which belong to broad templates and 2) a low-level CFG that enables
interpretation of certain specific expressions that belong to such patterns.
These low-level expressions occur as concepts from four different categories of
data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and
sentiments), 3) concepts in lexicons with only partial ontology representation,
called lexico-ontology concepts (such as side effects and routes of
administration (ROA)), and 4) domain specific expressions (such as date, time,
interval, frequency and dosage) derived solely through rules. Our approach is
embodied in a novel Semantic Web platform called PREDOSE developed for
prescription drug abuse epidemiology.
Keywords: Knowledge-Aware Search, Ontology, Semantic Search, Background
Knowledge, Context-Free GrammarComment: Accepted for publication: Journal of Web Semantics, Elsevie
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