11,909 research outputs found
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
SEMANTIC-FIRST DECISION TREE NATURALIZATION FOR ENHANCED CONVERSATIONAL ARTIFICIAL INTELLIGENCE
Presented herein are techniques for improving Conversational Artificial Intelligence (AI) systems through the incorporation of domain expert knowledge, leveraging the Retrieval-Augmented Generation (RAG) process. Techniques described herein provide an approach that begins with the establishment of semantic relationships within the questions, choices, and solutions components of a decision tree. Semantically-structured questions are sent to GPT-4 for keyword and semantic tagging, incorporating domain-specific information. After integrating these new tags, the enriched dataset is again processed by GPT-4 for natural language translation. This process can be utilized for RAG, model building, prompt engineering, and fine-tuning. The final output is utilized by applications that leverage GPT-based text embedding, significantly enhancing context-aware interactions and domain-specific information retrieval
A framework for automatic semantic video annotation
The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the âsemantic gapâ. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation
Ontology-Based MEDLINE Document Classification
An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents
Indexing with WordNet synsets can improve Text Retrieval
The classical, vector space model for text retrieval is shown to give better
results (up to 29% better in our experiments) if WordNet synsets are chosen as
the indexing space, instead of word forms. This result is obtained for a
manually disambiguated test collection (of queries and documents) derived from
the Semcor semantic concordance. The sensitivity of retrieval performance to
(automatic) disambiguation errors when indexing documents is also measured.
Finally, it is observed that if queries are not disambiguated, indexing by
synsets performs (at best) only as good as standard word indexing.Comment: 7 pages, LaTeX2e, 3 eps figures, uses epsfig, colacl.st
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
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