Semantic Search

This paper describes a novel approach to satisfy the needs
of museum's website visitors with a unique experience that cannot be reproduced in the museum itself. We aim at providing a continuous and lasting experience, without the emphasis of a single, final result - a process we call digital strolling. The view supports this process by  displaying results as a path on which the user strolls. To enable the user to find new and unexpected inspiration, recommendations to related exhibits are proposed in dierent dimensions to vary the user's path. The common
approach of image retrieval as the sole method to generate recommendations of related exhibits is not sucient. Authored tagging is still the better but more costly solution. The proposed approach claims to fill the gap between current digital museums and the needs of the digital museums' visitors.

Term ambiguity - the challenge of having multiple
potential meanings for a keyword or phrase - can be a major
problem for search engines. Contextual information is essential
for word sense disambiguation, but search queries are often
limited to very few keywords, making the available textual context
needed for disambiguation minimal or non-existent. In this paper
we propose a novel system to identify and resolve term ambiguity
in search queries using large-scale user behavioral data. The
proposed system demonstrates that, despite the lack of context
in most keyword queries, multiple potential senses of a keyword
or phrase within a search query can be accurately identified,
disambiguated, and expressed in order to maximize the likelihood
of fulfilling a user’s information need. The proposed system
overcomes the immediate lack of context by leveraging largescale
user behavioral data from historical query logs. Unlike
traditional word sense disambiguation methods that rely on
knowledge sources or available textual corpora, our system is
language-agnostic, is able to easily handle domain-specific terms
and meanings, and is automatically generated so that it does
not grow out of date or require manual updating as ambiguous
terms emerge or undergo a shift in meaning. The system has
been implemented using the Hadoop eco-system and integrated
within CareerBuilder’s semantic search engine.

"In the context of the emergent Web of Data, a large number of organizations, institutes and companies (e.g., DBpedia, Geonames, PubMed ACM, IEEE, NASA, BBC) adopt the Linked Data practices and publish their data utilizing Semantic Web (SW) technologies. On the other hand, the dominant standard for information exchange in the Web today is XML. Many international standards (e.g., Dublin Core, MPEG-7, METS, TEI, IEEE LOM) have been expressed in XML Schema resulting to a large number of XML datasets. The SW and XML worlds and their developed infrastructures are based on different data models, semantics and query languages. Thus, it is crucial to provide interoperability and integration mechanisms to bridge the gap between the SW and XML worlds.
In this chapter, we give an overview and a comparison of the technologies and the standards adopted by the XML and SW worlds. In addition, we outline the latest efforts from the W3C groups, including the latest working drafts and recommendations (e.g., OWL 2, SPARQL 1.1, XML Schema 1.1). Moreover, we present a survey of the research approaches which aim to provide interoperability and integration between the XML and SW worlds. Finally, we present the SPARQL2XQuery and XS2OWL Frameworks, which bridge the gap and create an interoperable environment between the two worlds. These Frameworks provide mechanisms for: (a) Query translation (SPARQL to XQuery translation); (b) Mapping specification and generation (Ontology to XML Schema mapping); and (c) Schema transformation (XML Schema to OWL transformation)."