Ontology-based Similarity Measures Research Articles

An Interval Type-2 Fuzzy Ontological Similarity Measure

Human language is naturally fuzzy by nature, with words meaning different things to different people, depending on the context. Fuzzy words are words with a subjective meaning, which are typically used in everyday human natural language dialogue and are often ambiguous and vague in meaning and are based on an individual’s perception. Fuzzy Sentence Similarity Measures (FSSM) are algorithms that can compare two or more short texts which contain human-perception-based words and return a numeric measure of similarity of meaning between them. This paper proposes a new FSSM called FUSE (FUzzy Similarity mEasure), to assess an individual’s perception within a FSSM. FUSE is an ontology-based similarity measure that uses Interval Type-2 fuzzy sets to model relationships between categories of human perception-based words. The FUSE algorithm has been developed over four versions and evaluated on several published and newly created datasets. Typically, results have shown that calculating the semantic similarity of two short texts using FUSE, gives a higher correlation with the average human ratings (AHR) compared to traditional sentence similarity measures that do not consider the presence of fuzzy words. This paper focuses on the second version of the FUSE algorithm, referred to as FUSE_2.0 which has been compared to several state-of-the-art, semantic similarity measures (SSM), including the only published FSSM, FAST (Fuzzy Algorithm for Similarity Testing), which has a limited dictionary of fuzzy words and uses Type-1 to model relationships between categories of human perception-based words. Results have shown that FUSE_2.0 achieves a higher correlation with the average human ratings (AHR) compared to traditional SSM’s and FAST. The key contributions of this work can be summarised as follows: The development of a new methodology to model fuzzy words using Interval Type-2 fuzzy sets. This has led to the creation of a fuzzy dictionary for nine fuzzy categories, a useful resource which can be used by other researchers in the field of natural language processing and Computing with Words (CWW) with other fuzzy applications such as semantic clustering.

Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts.

Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that concepts are not effectively referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework that incorporates domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text. We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based part, we use the Medical Subject Headings (MeSH) ontology and three state-of-the-art ontology-based similarity measures. In our approach, we propose a new learning objective, modified from the sigmoid cross-entropy objective function. We used two established datasets of semantic similarities among biomedical concept pairs to evaluate the quality of the generated word embeddings. On the first dataset with 29 concept pairs, with similarity scores established by physicians and medical coders, MORE's similarity scores have the highest combined correlation (0.633), which is 5.0% higher than that of the baseline model, and 12.4% higher than that of the best ontology-based similarity measure. On the second dataset with 449 concept pairs, MORE's similarity scores have a correlation of 0.481, based on the average of four medical residents' similarity ratings, and that outperforms the skip-gram model by 8.1%, and the best ontology measure by 6.9%. Furthermore, MORE outperforms three pre-trained transformer-based word embedding models (i.e., BERT, ClinicalBERT, and BioBERT) on both datasets. MORE incorporates knowledge from several biomedical ontologies into an existing corpus-based distributional semantics model, improving both the accuracy of the learned word embeddings and the extensibility of the model to a broader range of biomedical concepts. MORE allows for more accurate clustering of concepts across a wide range of applications, such as analyzing patient health records to identify subjects with similar pathologies, or integrating heterogeneous clinical data to improve interoperability between hospitals.

The ReCAP Project

Argumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a comprehensive investigation in finding arguments and argument graphs. We introduce a probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.

Mapping Crisp Structural Semantic Similarity Measures to Fuzzy Context: A Generic Approach

Ontology-based similarity measures have received much importance in recent years. In many real-world cases, the domain considered in the ontological similarity assessment consists of uncertainty or incomplete information. Such vagueness has led to the successful implementation of fuzzy ontology (FO)-based similarity measures. Despite various applications of FO-based similarity measures, limited methods have so far been proposed for this purpose. Accordingly, this paper presents a generic model for semantic similarity assessment based on a fuzzy ontology. The proposed approach relies on the broad literature of Crisp Ontology-based Structural Semantic Similarity Measures (CO-SSSM). It provides an approach for mapping CO-SSSMs to fuzzy context. Consequently, the proposed generic model can be applied to various CO-SSSMs to develop their corresponding FO-SSSMs. In this regard, as an empirical investigation, four of the common CO-SSSMs were selected, their equivalent FO-SSSMs were developed by means of the proposed approach, and the accuracy of their similarity assessment was compared with each other. The results show the power of FO-SSSMs in describing the relations between concepts and their superiority over CO-SSSMs.

Towards an ontology-supported case-based reasoning approach for computer-aided tolerance specification

In this paper, an ontology-supported case-based reasoning approach for computer-aided tolerance specification is proposed. This approach firstly considers the past tolerance specification problems and their schemes as previous cases and the new tolerance specification problems as target cases and uses an ontology to represent previous and target cases. Then certain ontology-based similarity measure is used to assess the similarity between the toleranced features of target and previous cases, the similarity between the part features of target and previous cases, and the similarity between the topological relations of target and previous cases. Based on these similarities, an ontology-based similarity measure for computing the similarity between target and previous cases is designed, and an algorithm for establishing such similarity measure with high accuracy and retrieving similar previous cases for a target case with this similarity measure is presented. This algorithm shows how to linearly combine the similarity of toleranced features, the similarity of part features, and the similarity of topological relations to assess the similarity between target and previous cases to implement retrieval of previous cases under the prerequisite of ensuring the highest accuracy of the similarity measure. The paper also reports a prototype implementation of the proposed approach, provides an example to illustrate how the approach works, and evaluates the approach via theoretical and experimental comparisons.

Investigating the impact human protein\u2013protein interaction networks have on disease-gene analysis

Advances in high-throughput technologies along with the curation of small-scale experiments has aided in the construction of reference maps of the interactome. These maps are critical to our understanding of genotype-phenotype relationships and disease. However, our knowledge of disease associated genes and the map of the human interactome still remains incomplete. In this study we investigate whether protein–protein interaction networks (PPINs) constructed from either experimental or curated data have an impact upon disease network analysis. An integrative network-driven framework is implemented to integrate diverse heterogeneous data including: gene-expression, PPIN, ontology-based similarity, degree connectivity and betweenness centrality measures to uncover potential Alzhemier disease (AD) candidate genes. Two PPINs have been selected and constructed from (1) experimental high-throughput data and (2) literature-curated sources. Only a marginal overlap of protein pairs between the two PPINs (305 protein pairs) was observed. A total of 17 significant AD gene candidate genes were identified using the literature derived PPIN compared to 20 genes using the PPIN constructed from high-throughput data. Both approaches correctly identified the AD susceptible TRAF1, a critical regulator of cerebral ischaemia–reperfusion injury and neuronal death. Biological process enrichment analysis revealed genes candidates from the literature based PPIN are modulated in AD pathogenesis such as neuron differentiation and involved in KEGG pathways such as neurotrophin signaling pathways. Tissue specific analysis revealed 48 % of AD gene candidates obtained from the literature curated PPIN were expressed in tissues where AD is observed compared to 19 % of gene candidates extracted using the high-throughput PPIN.

A novel family of IC-based similarity measures with a detailed experimental survey on WordNet

This paper introduces a novel family of ontology-based similarity measures based on the Information Content (IC) theory, a detailed state of the art, a large experimental survey into ontology-based similarity measures on WordNet, and a new comparison between intrinsic and corpus-based IC models. Our experiments are based on our implementation of a large set of similarity measures, intrinsic and corpus-based IC models, which are evaluated on two known datasets and three different WordNet versions. The new measures are called weighted Jiang–Conrath distance (wJ&Cdist) and similarity (wJ&Csim), cosine-normalized Jiang–Conrath similarity (cosJ&Csim) and cosine-normalized weighted Jiang–Conrath similarity (coswJ&Csim). Two of our similarity measures outperform the state-of-the-art measures on the RG65 dataset, and one of them obtains the third overall score on all the datasets and evaluated WordNet versions. The cosine-normalized similarity measures are a non-linear normalization of the classic Jiang–Conrath (J&C) distance and the new wJ&C distance. On the other hand, the wJ&C distance is a generalization of the classic J&C distance which is based on the length of the shortest path between concepts within an IC-based weighted graph. Our measures are based on two not previously considered notions: (1) a generalization of the classic J&C distance to any type of taxonomy, based on an IC-based weighted graph derived from the conditional probabilities between child and parent concepts, and (2) a non-linear normalization function that converts the ontology-based semantic distances into similarity functions. Finally, the corpus-based IC models based on the Resnik method obtain rivaling results as regards the state-of-the-art intrinsic IC models, when they are used with some unexplored WordNet-based frequency files. Therefore, this latter fact allows us to reconsider some previous conclusions about the outperformance of the intrinsic IC models over the corpus-based ones.

A new family of information content models with an experimental survey on WordNet

This paper introduces a new family of intrinsic and corpus-based Information Content (IC) models for ontology-based similarity measures based on the IC theory, a detailed state of the art, an experimental survey of IC models and IC-based similarity measures on WordNet, and a comparison between intrinsic and corpus-based IC models. The family of IC models is made up of five intrinsic IC models, called CondProbHypo, CondProbUniform, CondProbLeaves, CondProbLogistic, and CondProbCosine, and one corpus-based IC model called CondProbCorpus which completes the family. The proposed IC models rely on two previously unconsidered notions: (1) the preservation of the probabilistic structure of the taxonomy associated to the conditional probabilities between child and parent concepts, and (2) the explicit consideration of a cognitive similarity notion in the definition of the IC model. The family of IC models defines a new method for the proposal of new intrinsic IC models based on the exploration of other alternatives for the intrinsic estimation of the conditional probabilities between child and parent concepts. Our work is inspired by an unexplored relationship between the Jiang–Conrath distance and a shortest path on an IC-based weighted graph, derived from the conditional probabilities between concepts, as well as certain cognitive evidence about the perception distance between concepts. The new IC models obtain results comparable to the state of the art and satisfy a set of well-founded structure axioms. In addition, we prove that most of intrinsic IC models and IC-based similarity measures do not show a significant statistical difference as regards a baseline corpus-based IC model and the Jiang–Conrath similarity, with the exception of the overall outperformance shown by the Sánchez et al. (2012) IC model and the cosJ&C similarity measure, which has recently been introduced by the authors.

A computational framework for the prioritization of disease-gene candidates

BackgroundThe identification of genes and uncovering the role they play in diseases is an important and complex challenge. Genome-wide linkage and association studies have made advancements in identifying genetic variants that underpin human disease. An important challenge now is to identify meaningful disease-associated genes from a long list of candidate genes implicated by these analyses. The application of gene prioritization can enhance our understanding of disease mechanisms and aid in the discovery of drug targets. The integration of protein-protein interaction networks along with disease datasets and contextual information is an important tool in unraveling the molecular basis of diseases.ResultsIn this paper we propose a computational pipeline for the prioritization of disease-gene candidates. Diverse heterogeneous data including: gene-expression, protein-protein interaction network, ontology-based similarity and topological measures and tissue-specific are integrated. The pipeline was applied to prioritize Alzheimer's Disease (AD) genes, whereby a list of 32 prioritized genes was generated. This approach correctly identified key AD susceptible genes: PSEN1 and TRAF1. Biological process enrichment analysis revealed the prioritized genes are modulated in AD pathogenesis including: regulation of neurogenesis and generation of neurons. Relatively high predictive performance (AUC: 0.70) was observed when classifying AD and normal gene expression profiles from individuals using leave-one-out cross validation.ConclusionsThis work provides a foundation for future investigation of diverse heterogeneous data integration for disease-gene prioritization.

Clustering rule bases using ontology-based similarity measures

Rules are increasingly becoming an important form of knowledge representation on the Semantic Web. There are currently few methods that can ensure that the acquisition and management of rules can scale to the size of the Web. We previously developed methods to help manage large rule bases using syntactical analyses of rules. This approach did not incorporate semantics. As a result, rule categorization based on syntactic features may not be effective. In this paper, we present a novel approach for grouping rules based on whether the rule elements share relationships within a domain ontology. We have developed our method for rules specified in the Semantic Web Rule Language (SWRL), which is based on the Web Ontology Language (OWL) and shares its formal underpinnings. Our method uses vector space modeling of rule atoms and an ontology-based semantic similarity measure. We apply a clustering method to detect rule relatedness, and we use a statistical model selection method to find the optimal number of clusters within a rule base. Using three different SWRL rule bases, we evaluated the results of our semantic clustering method against those of our syntactic approach. We have found that our new approach creates clusters that better match the rule bases’ logical structures. Semantic clustering of rule bases may help users to more rapidly comprehend, acquire, and manage the growing numbers of rules on the Semantic Web.

Towards the estimation of feature-based semantic similarity using multiple ontologies

A key application of ontologies is the estimation of the semantic similarity between terms. By means of this assessment, the comprehension and management of textual resources can be improved. However, most ontology-based similarity measures only support a single input ontology. If any of the compared terms do not belong to that ontology, their similarity cannot be assessed. To solve this problem, multiple ontologies can be considered. Even though there are methods that enable the multi-ontology similarity assessment by means of integrating concepts from different ontologies, most of them are based on simple terminological and/or partial matchings. This hampers similarity measures that exploit a broad set of taxonomic evidences of similarity, like feature-based ones. In this paper, we tackle this problem by proposing a method to identify all the suitable matchings between concepts of different ontologies that intervene in the similarity assessment. In addition to the obvious terminological matching, we exploit the ontological structure and the notion of concept subsumption to discover non-trivial equivalences between heterogeneous ontologies. Our final goal is to enable the accurate application of feature-based similarity measures in a multi-ontology setting. Our proposal is evaluated with regard human judgements of similarity for several benchmarks and ontologies. Results shows an improvement against related works, with similarity accuracies that even rival those obtained in an ideal mono-ontology setting.

An ontology-based similarity measure for biomedical data – Application to radiology reports

BackgroundDetermining similarity between two individual concepts or two sets of concepts extracted from a free text document is important for various aspects of biomedicine, for instance, to find prior clinical reports for a patient that are relevant to the current clinical context. Using simple concept matching techniques, such as lexicon based comparisons, is typically not sufficient to determine an accurate measure of similarity. MethodsIn this study, we tested an enhancement to the standard document vector cosine similarity model in which ontological parent–child (is-a) relationships are exploited. For a given concept, we define a semantic vector consisting of all parent concepts and their corresponding weights as determined by the shortest distance between the concept and parent after accounting for all possible paths. Similarity between the two concepts is then determined by taking the cosine angle between the two corresponding vectors. To test the improvement over the non-semantic document vector cosine similarity model, we measured the similarity between groups of reports arising from similar clinical contexts, including anatomy and imaging procedure. We further applied the similarity metrics within a k-nearest-neighbor (k-NN) algorithm to classify reports based on their anatomical and procedure based groups. 2150 production CT radiology reports (952 abdomen reports and 1128 neuro reports) were used in testing with SNOMED CT, restricted to Body structure, Clinical finding and Procedure branches, as the reference ontology. ResultsThe semantic algorithm preferentially increased the intra-class similarity over the inter-class similarity, with a 0.07 and 0.08 mean increase in the neuro–neuro and abdomen–abdomen pairs versus a 0.04 mean increase in the neuro–abdomen pairs. Using leave-one-out cross-validation in which each document was iteratively used as a test sample while excluding it from the training data, the k-NN based classification accuracy was shown in all cases to be consistently higher with the semantics based measure compared with the non-semantic case. Moreover, the accuracy remained steady even as k value was increased – for the two anatomy related classes accuracy for k=41 was 93.1% with semantics compared to 86.7% without semantics. Similarly, for the eight imaging procedures related classes, accuracy (for k=41) with semantics was 63.8% compared to 60.2% without semantics. At the same k, accuracy improved significantly to 82.8% and 77.4% respectively when procedures were logically grouped together into four classes (such as ignoring contrast information in the imaging procedure description). Similar results were seen at other k-values. ConclusionsThe addition of semantic context into the document vector space model improves the ability of the cosine similarity to differentiate between radiology reports of different anatomical and image procedure-based classes. This effect can be leveraged for document classification tasks, which suggests its potential applicability for biomedical information retrieval.

Ontology-based semantic similarity: A new feature-based approach

Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approach’s results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.

Computing With Words With the Ontological Self-Organizing Map

This paper addresses the computing-with-words paradigm by presenting an ontological self-organizing map (OSOM), which produces visualization and summarization information about datasets composed of words, namely, ontological data. The specific data that are used in this paper are the Gene Ontology (GO) annotations of genes and gene products. The OSOM is an extension of the SOM, which was initially developed by Kohonen. We adapt the SOM by integrating ontology-based similarity measures and relational-clustering distance measures. We also develop a novel prototype update. We present results on two datasets composed of GO annotations of genes and gene products. An OSOM-based summarization, which produces the term-based summarizations of the trained OSOM network, is also demonstrated. The results show that the OSOM-based visualization method correctly shows the cluster tendency of the genes and gene products and that the summarization provides useful information about the mapped groups of genes and gene products.

Ontology based Similarity Measure in Document Ranking

This paper presents a methodology for the ontology based semantic annotation of web pages with annotation weighting scheme that takes advantage of the different relevance of structured document fields. The retrieval model is based on the importance factors of the structural elements, which are used to re-rank the documents retrieval by the ontology based distance measure. The relevance concept similarity are combined with the annotation-weighting scheme to improve the relevance measures. The proposed method has been evaluated on USGS Science directory collection. Preliminary experiments results show that our method may generate relevant document in the top rank.