28,595 research outputs found
Sematch: semantic entity search from knowledge graph
As an increasing amount of the knowledge graph is published as Linked Open Data, semantic entity search is required to develop new applications. However, the use of structured query languages such as SPARQL is challenging for non-skilled users who need to master the query language as well as acquiring knowledge of the underlying ontology of Linked Data knowledge bases. In this article, we propose the Sematch framework for entity search in the knowledge graph that combines natural language query processing, entity linking, entity type linking and semantic similarity based query expansion. The system has been validated in a dataset and a prototype has been developed that translates natural language queries into SPARQL
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
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
Semantic keyword search for expert witness discovery
In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach
Semantic keyword search for expert witness discovery
In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach
Video databases annotation enhancing using commonsense knowledgebases for indexing and retrieval
The rapidly increasing amount of video collections, especially on the web, motivated the need for intelligent automated annotation tools for searching, rating, indexing and retrieval purposes. These videos collections contain all types of manually annotated videos. As this annotation is usually incomplete and uncertain and contains misspelling words, search using some keywords almost do retrieve only a portion of videos which actually contains the desired meaning. Hence, this annotation needs filtering, expanding and validating for better indexing and retrieval.
In this paper, we present a novel framework for video annotation enhancement, based on merging two widely known commonsense knowledgebases, namely WordNet and ConceptNet. In addition to that, a comparison between these knowledgebases in video annotation domain is presented.
Experiments were performed on random wide-domain video clips, from the \emph{vimeo.com} website. Results show that searching for a video over enhanced tags, based on our proposed framework, outperforms searching using the original tags. In addition to that, the annotation enhanced by our framework outperforms both those enhanced by WordNet and ConceptNet individually, in terms of tags enrichment ability, concept diversity and most importantly retrieval performance
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