263 research outputs found
Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System
Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology
LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification
Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.
This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems.
We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success.
Adviser: Dr Jitender Deogu
LEARNFCA: A FUZZY FCA AND PROBABILITY BASED APPROACH FOR LEARNING AND CLASSIFICATION
Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.
This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems.
We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success.
Adviser: Jitender Deogu
Formal Concept Analysis Applications in Bioinformatics
Bioinformatics is an important field that seeks to solve biological problems with the help of computation. One specific field in bioinformatics is that of genomics, the study of genes and their functions. Genomics can provide valuable analysis as to the interaction between how genes interact with their environment. One such way to measure the interaction is through gene expression data, which determines whether (and how much) a certain gene activates in a situation. Analyzing this data can be critical for predicting diseases or other biological reactions. One method used for analysis is Formal Concept Analysis (FCA), a computing technique based in partial orders that allows the user to examine the structural properties of binary data based on which subsets of the data set depend on each other. This thesis surveys, in breadth and depth, the current literature related to the use of FCA for bioinformatics, with particular focus on gene expression data. This includes descriptions of current data management techniques specific to FCA, such as lattice reduction, discretization, and variations of FCA to account for different data types. Advantages and shortcomings of using FCA for genomic investigations, as well as the feasibility of using FCA for this application are addressed. Finally, several areas for future doctoral research are proposed.
Adviser: Jitender S. Deogu
Ontology Learning Using Formal Concept Analysis and WordNet
Manual ontology construction takes time, resources, and domain specialists.
Supporting a component of this process for automation or semi-automation would
be good. This project and dissertation provide a Formal Concept Analysis and
WordNet framework for learning concept hierarchies from free texts. The process
has steps. First, the document is Part-Of-Speech labeled, then parsed to
produce sentence parse trees. Verb/noun dependencies are derived from parse
trees next. After lemmatizing, pruning, and filtering the word pairings, the
formal context is created. The formal context may contain some erroneous and
uninteresting pairs because the parser output may be erroneous, not all derived
pairs are interesting, and it may be large due to constructing it from a large
free text corpus. Deriving lattice from the formal context may take longer,
depending on the size and complexity of the data. Thus, decreasing formal
context may eliminate erroneous and uninteresting pairs and speed up idea
lattice derivation. WordNet-based and Frequency-based approaches are tested.
Finally, we compute formal idea lattice and create a classical concept
hierarchy. The reduced concept lattice is compared to the original to evaluate
the outcomes. Despite several system constraints and component discrepancies
that may prevent logical conclusion, the following data imply idea hierarchies
in this project and dissertation are promising. First, the reduced idea lattice
and original concept have commonalities. Second, alternative language or
statistical methods can reduce formal context size. Finally, WordNet-based and
Frequency-based approaches reduce formal context differently, and the order of
applying them is examined to reduce context efficiently
Formal concept matching and reinforcement learning in adaptive information retrieval
The superiority of the human brain in information retrieval (IR) tasks seems to come firstly
from its ability to read and understand the concepts, ideas or meanings central to documents, in
order to reason out the usefulness of documents to information needs, and secondly from its
ability to learn from experience and be adaptive to the environment. In this work we attempt to
incorporate these properties into the development of an IR model to improve document
retrieval. We investigate the applicability of concept lattices, which are based on the theory of
Formal Concept Analysis (FCA), to the representation of documents. This allows the use of
more elegant representation units, as opposed to keywords, in order to better capture
concepts/ideas expressed in natural language text. We also investigate the use of a
reinforcement leaming strategy to learn and improve document representations, based on the
information present in query statements and user relevance feedback. Features or concepts of
each document/query, formulated using FCA, are weighted separately with respect to the
documents they are in, and organised into separate concept lattices according to a subsumption
relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure
known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the
concepts in the lattice representation. This avoids implementation drawbacks faced by other
FCA-based approaches. Retrieval of a document for an information need is based on concept
matching between concept lattice representations of a document and a query. The learning
strategy works by making the similarity of relevant documents stronger and non-relevant
documents weaker for each query, depending on the relevance judgements of the users on
retrieved documents. Our approach is radically different to existing FCA-based approaches in
the following respects: concept formulation; weight assignment to object-attribute pairs; the
representation of each document in a separate concept lattice; and encoding concept lattices in
BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our
learning strategy makes use of relevance feedback information to enhance document
representations, thus making the document representations dynamic and adaptive to the user
interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are
presented and compared with published results. In particular, the performance of the system is
shown to improve significantly as the system learns from experience.The School of Computing,
University of Plymouth, UK
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