Semantic Relatedness Measures Research Articles

Property-based Semantic Similarity and Relatedness for Improving Recommendation Accuracy and Diversity

The authors introduce new measures of semantic similarity and relatedness for ontological concepts, based on the properties associated to them. They consider two concepts similar if, for some properties they have in common, they also have the same values assigned to these properties. On the other hand, the authors consider two concepts related if they have the same values assigned to different properties. These measures are used in the propagation of user interest values in ontology-based user models to other similar or related concepts in the domain. The authors tested their algorithm in event recommendation domain and in recipe domain and showed that property-based propagation based on similarity outperforms the standard edge-based propagation. Adding relatedness as a criterion for propagation improves diversity without sacrificing accuracy. In addition, assigning a certain relevance to each property improves the accuracy of recommendation. Finally, the property-based spreading activation is effective for cross-domain recommendation.

Harmony Search Algorithm for Word Sense Disambiguation.

Word Sense Disambiguation (WSD) is the task of determining which sense of an ambiguous word (word with multiple meanings) is chosen in a particular use of that word, by considering its context. A sentence is considered ambiguous if it contains ambiguous word(s). Practically, any sentence that has been classified as ambiguous usually has multiple interpretations, but just one of them presents the correct interpretation. We propose an unsupervised method that exploits knowledge based approaches for word sense disambiguation using Harmony Search Algorithm (HSA) based on a Stanford dependencies generator (HSDG). The role of the dependency generator is to parse sentences to obtain their dependency relations. Whereas, the goal of using the HSA is to maximize the overall semantic similarity of the set of parsed words. HSA invokes a combination of semantic similarity and relatedness measurements, i.e., Jiang and Conrath (jcn) and an adapted Lesk algorithm, to perform the HSA fitness function. Our proposed method was experimented on benchmark datasets, which yielded results comparable to the state-of-the-art WSD methods. In order to evaluate the effectiveness of the dependency generator, we perform the same methodology without the parser, but with a window of words. The empirical results demonstrate that the proposed method is able to produce effective solutions for most instances of the datasets used.

Semantic approach for Web service classification using machine learning and measures of semantic relatedness

Web service classification, a task of assigning a category to the service from a predefined set, is a challenging task nowadays as manually organizing and searching services are simply not feasible, given the time constraints or the exponentially growing number of services. In this paper, a hybrid approach independent of service description models is suggested for automatic classification of Web services to improve classification accuracy. The proposed classification approach assists the repository administrator and the users during registration and service retrieval, respectively. It utilizes the semantic as well as syntactic information present within the service description by combining the techniques from machine learning, data mining, logical reasoning, statistical methods and measures of semantic relatedness. The proposed approach applies Omiotis measure of semantic relatedness to transform the service vectors into semantically enriched service vectors which are used by the classification algorithms. Supervised machine learning-based support vector machines and k-Nearest Neighbor classifiers are used to categorize service profiles under different categories. Empirical evaluation and comparison of the proposed approach implemented on OWL-X dataset is presented for enabling the discovery and reusability of the existing services.

A Novel Method of Semantic Relatedness Measurements for Word Sense Disambiguation on English AlQuran

The methods and background introduced in this article concern on the interpretation of the Quranic text in English translation using word sense disambiguation. Three measures of semantic similarity measures: Wu-palmer, Lin and Jiang-Conrath, and their combination were used to identify words sense on the English Quranic text, in which comparison and detailed evaluation was carried out. The finding results showed that the Wu–Palmer approach determine the similarity of senses as the most accurate, followed by the Lin, and Jiang–Conrath. However, the combination of these measurements (novel method) achieves higher accuracy in comparison with the individual result of each measure before.

Novel Semantic Model for Intelligent Discovery of Web Services

Main challenges to the current semantic web service technologies are exponential continuous growth in the number of services on the Internet, syntax based discovery, lack of common agreed upon semantic service standards and heterogeneity of ontologies. In this paper, a service discovery approach independent of semantic service description models is proposed to solve the challenges of the current web service discovery. The idea is to combine principles from machine learning, data mining, statistical techniques and measures of semantic relatedness to make the semantic web service discovery process more intelligent, efficient and effective. The proposed approach exploits the use of semantic as well as syntactic information present within the service description profiles. Our approach is unique in terms of its application to any web service description language and the use of Omiotis measure of semantic relatedness for service discovery. The proposed approach has been implemented on OWL-S based service descriptions profiles and is able to find semantic relationship between the services which were otherwise discarded by the OWL-MX matchmaker. Empirical analysis shows that the proposed method out performs the

Effects of domain on measures of semantic relatedness

Measures of semantic relatedness have been used in a variety of applications in information retrieval and language technology, such as measuring document similarity and cohesion of text. Definitions of such measures have ranged from using distance‐based calculations over WordNet or other taxonomies to statistical distributional metrics over document collections such as Wikipedia or the Web. Existing measures do not explicitly consider the domain associations of terms when calculating relatedness: This article demonstrates that domain matters. We construct a data set of pairs of terms with associated domain information and extract pairs that are scored nearly identical by a sample of existing semantic‐relatedness measures. We show that human judgments reliably score those pairs containing terms from the same domain as significantly more related than cross‐domain pairs, even though the semantic‐relatedness measures assign the pairs similar scores. We provide further evidence for this result using a machine learning setting by demonstrating that domain is an informative feature when learning a metric. We conclude that existing relatedness measures do not account for domain in the same way or to the same extent as do human judges.

개체중의성해소에서 의미관련도 활용 효과 분석: 한국어 위키피디아를 사용하여

Abstract Entity linking is to link entity's name mentions occurring in t ext to corresponding entities within knowledge bases. Since the same entity mention may refer to different entities according to their context, entity linking needs to deal with entity disambiguation. Most recent works on entity disambiguation focus on semantic related-ness between entities and attempt to integrate semantic related ness with entity prior probabilities and term co-occurrence. To the best of my knowledge, however, it is hard to find studies that analyze and present the pure effects of semantic relatedness on entity disambiguation. From the experimentation on Korean Wikipedia da-ta set, this article empirically evaluates entity disambiguation approaches using semantic relatedness in terms of the following aspects: (1) the difference among semantic relatedness measures such as NGD, PMI, Jaccard, Dice,Simpson, (2) the influence of ambiguities in co-occurring entity mentions' set, and (3) the difference between in-dividual and collective disambiguation approaches.Key Words : Entity Linking, Entity Disambiguation, Semantic Relatedness, WikipediaReceived: Nov. 5, 2014Revised : Feb. 16, 2015Accepted: Mar. 10, 2015

Morphological Decomposition in Japanese De-adjectival Nominals: Masked and Overt Priming Evidence.

Whether morpheme-based processing extends to relatively unproductive derived words remains a matter of debate. Although whole-word storage and access has been proposed for some derived words, such as Japanese de-adjectival nominals with the unproductive (-mi) suffix (e.g., Hagiwara etal. in Language 75:739-763, 1999), Clahsen and Ikemoto (Ment Lex 7:147-182, 2012) found masked priming from de-adjectival nominals with productive (-sa) and unproductive (-mi) suffixes to their adjectivally-inflected base morpheme. Using masked and unmasked priming, we examine whether adjectivally-inflected base morpheme primes facilitate the processing of Japanese de-adjectival nominal targets with a productive or unproductive affix, including an orthographic-overlap condition and semantic relatedness measure that Clahsen and Ikemoto (2012) did not include. Our results replicate and extend Clahsen and Ikemoto (2012), revealing significant, statistically-equivalent morphological priming effects for -sa and -mi affixed targets, independent of orthographic and semantic relatednesss, suggesting that the processing of derived words with the unproductive -mi affix makes recourse to morpheme-level representations.

Evaluating semantic similarity and relatedness over the semantic grouping of clinical term pairs

IntroductionThis article explores how measures of semantic similarity and relatedness are impacted by the semantic groups to which the concepts they are measuring belong. Our goal is to determine if there are distinctions between homogeneous comparisons (where both concepts belong to the same group) and heterogeneous ones (where the concepts are in different groups). Our hypothesis is that the similarity measures will be significantly affected since they rely on hierarchical is-a relations, whereas relatedness measures should be less impacted since they utilize a wider range of relations. In addition, we also evaluate the effect of combining different measures of similarity and relatedness. Our hypothesis is that these combined measures will more closely correlate with human judgment, since they better reflect the rich variety of information humans use when assessing similarity and relatedness. MethodWe evaluate our method on four reference standards. Three of the reference standards were annotated by human judges for relatedness and one was annotated for similarity. ResultsWe found significant differences in the correlation of semantic similarity and relatedness measures with human judgment, depending on which semantic groups were involved. We also found that combining a definition based relatedness measure with an information content similarity measure resulted in significant improvements in correlation over individual measures. AvailabilityThe semantic similarity and relatedness package is an open source program available from http://umls-similarity.sourceforge.net/. The reference standards are available at http://www.people.vcu.edu/∼{}btmcinnes/downloads.html.

A Cross-Modal Approach for Extracting Semantic Relationships Between Concepts Using Tagged Images

This paper presents a cross-modal approach for extracting semantic relationships between concepts using tagged images. In the proposed method, we first project both text and visual features of the tagged images to a latent space using canonical correlation analysis (CCA). Then, under the probabilistic interpretation of CCA, we calculate a representative distribution of the latent variables for each concept. Based on the representative distributions of the concepts, we derive two types of measures: the semantic relatedness between the concepts and the abstraction level of each concept. Because these measures are derived from a cross-modal scheme that enables the collaborative use of both text and visual features, the semantic relationships can successfully reflect semantic and visual contexts. Experiments conducted on tagged images collected from Flickr show that our measures are more coherent to human cognition than the conventional measures that use either text or visual features, or the WordNet-based measures. In particular, a new measure of semantic relatedness, which satisfies the triangle inequality, obtains the best results among different distance measures in our framework. The applicability of our measures to multimedia-related tasks such as concept clustering, image annotation and tag recommendation is also shown in the experiments.

Word Sense Disambiguation for Biomedical Text Mining Using Definition-Based Semantic Relatedness and Similarity Measures

Word Sense Disambiguation for Biomedical Text Mining Using Definition-Based Semantic Relatedness and Similarity Measures

Distributionally Extended Network-based Word Sense Disambiguation in Semantic Clustering of Polish Texts

In the paper we present an extended version of the graph-based unsupervised Word Sense Disambiguation algorithm. The algorithm is based on the spreading activation scheme applied to the graphs dynamically built on the basis of the text words and a large wordnet. The algorithm, originally proposed for English and Princeton WordNet, was adapted to Polish and plWordNet. An extension based on the knowledge acquired from the corpus-derived Measure of Semantic Relatedness was proposed. The extended algorithm was evaluated against the manually disambiguated corpus. We observed improvement in the case of the disambiguation performed for shorter text contexts. In addition the algorithm application expressed improvement in document clustering task.

Theoretical Formulas of Semantic Measure: A Survey

In recent years, several semantic similarity and relatedness measures have been developed and applied in many domains including linguistics, biomedical informatics, GeoInformatics, and Semantic Web. This paper discusses different semantic measures which compute similarity and relatedness scores between concepts based on a knowledge representation model offered by ontologies and semantic networks. The benchmarks and approaches used for the evaluation of semantic similarity methods are also described. The aim of this paper is to give a comprehensive view of these measures which helps researchers to choose the best semantic similarity or relatedness metric for their needs.

SEBSem: simple and efficient biomedical semantic relatedness measure

Calculating semantic relatedness between terms is crucial in numerous knowledge and information processing tasks highly relevant to the biomedical domain. Examples include semantic search and automated processing of scientific texts. Most available methods rely heavily on highly specialised resources, which substantially limits their reusability in various applications within the domain. In this work we present a simple semantic relatedness measure that relies only on very general resources and its design features allow minimising the costs of online computations. The relatedness is computed through comparing automatically extracted key-phrases relevant to respective input terms. This simple strategy provides a method that gives promising early test results, comparable to those of human annotators and state-of-the-art methods, on a well established benchmark.

Semantic smoothing for text clustering

In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the representation of text documents. The basic hypothesis examined is that considering semantic relatedness between two text documents may improve the performance of the text document clustering task. For our experimental evaluation we analyze the performance of several semantic relatedness measures when embedded in the proposed (S-VSM) and present results with respect to different experimental conditions, such as: (i) the datasets used, (ii) the underlying knowledge sources of the utilized measures, and (iii) the clustering algorithms employed. To the best of our knowledge, the current study is the first to systematically compare, analyze and evaluate the impact of semantic smoothing in text clustering based on ‘wisdom of linguists’, e.g., WordNets, ‘wisdom of crowds’, e.g., Wikipedia, and ‘wisdom of corpora’, e.g., large text corpora represented with the traditional Bag of Words (BoW) model. Three semantic relatedness measures for text are considered; two knowledge-based (Omiotis[1] that uses WordNet, and WLM[2] that uses Wikipedia), and one corpus-based (PMI[3] trained on a semantically tagged SemCor version). For the comparison of different experimental conditions we use the BCubed F-Measure evaluation metric which satisfies all formal constraints of good quality cluster. The experimental results show that the clustering performance based on the S-VSM is better compared to the traditional VSM model and compares favorably against the standard GVSM kernel which uses word co-occurrences to compute the latent similarities between document terms.

Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text

In this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures. These measures quantify the degree of similarity or relatedness between concepts in the Unified Medical Language System (UMLS). The objective of this work is to develop a method that can disambiguate terms in biomedical text by exploiting similarity and relatedness information extracted from biomedical resources and to evaluate the efficacy of these measure on WSD. We evaluate our method on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. We show that information content-based measures derived from either a corpus or taxonomy obtain a higher disambiguation accuracy than path-based measures or relatedness measures on the MSH-WSD dataset. The WSD system is open source and freely available from http://search.cpan.org/dist/UMLS-SenseRelate/. The MSH-WSD dataset is available from the National Library of Medicine http://wsd.nlm.nih.gov.

Querying linked data graphs using semantic relatedness: A vocabulary independent approach

Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked Data on the Web, should be able to search and query data spread over potentially large numbers of heterogeneous, complex and distributed datasets. Ideally, a query mechanism for Linked Data should abstract users from the representation of data. This work focuses on the investigation of a vocabulary independent natural language query mechanism for Linked Data, using an approach based on the combination of entity search, a Wikipedia-based semantic relatedness measure and spreading activation. Wikipedia-based semantic relatedness measures address existing limitations of existing works which are based on similarity measures/term expansion based on WordNet. Experimental results using the query mechanism to answer 50 natural language queries over DBpedia achieved a mean reciprocal rank of 61.4%, an average precision of 48.7% and average recall of 57.2%.

A new semantic relatedness measurement using WordNet features

Computing semantic similarity/relatedness between concepts and words is an important issue of many research fields. Information theoretic approaches exploit the notion of Information Content (IC) that provides for a concept a better understanding of its semantics. In this paper, we present a complete IC metrics survey with a critical study. Then, we propose a new intrinsic IC computing method using taxonomical features extracted from an ontology for a particular concept. This approach quantifies the subgraph formed by the concept subsumers using the depth and the descendents count as taxonomical parameters. In a second part, we integrate this IC metric in a new parameterized multistrategy approach for measuring word semantic relatedness. This measure exploits the WordNet features such as the noun "is a" taxonomy, the nominalization relation allowing the use of verb "is a" taxonomy and the shared words (overlaps) in glosses. Our work has been evaluated and compared with related works using a wide set of benchmarks conceived for word semantic similarity/relatedness tasks. Obtained results show that our IC method and the new relatedness measure correlated better with human judgments than related works.

A Web Based Method for Measuring Semantic Relatedness between Words

Semantic relatedness measures play important roles in many fields, such as information retrieval and Nature Language Processing. There are mainly two kinds of traditional methods to measure semantic relatedness: dictionary based and corpus based. However, with the development of information technology, web search engine is used to do this work. In this paper, we propose a method integrating page counts and web-based kernel function for measuring semantic relatedness between words. It gets a better result than using page counts and web-based kernel function alone. Experimental results show Spearman rank correlation coefficient can reach 0.63 and Correlation reach 0.724.

Semantic Relatedness Measures in E-learning: A study

paper, a detailed study of works that have been carried out in finding the semantic relatedness or relatedness (in short) of Learning Objects (LO) in the context of E-learning has been presented. Learning Objects are small instructional chunks of learning elements which can be archived, extracted and shared in the learning process. Semantic relatedness in general specifies the degree of relatedness between two concepts in a taxonomy computed using different types of relations defined between the concepts. Semantic relatedness measures have been used in applications like Word sense Disambiguation, Information Retrieval, Natural Language Processing, Query Expansion etc. In the context of E-learning, there are several scenarios like learning object sequencing, query answering, scaffolding, clustering etc. where the computation of semantic relatedness between LO has promising scope. But only few works have been carried out in the quantification of semantic relatedness between LO. The objective of this paper is to present the existing semantic relatedness measures in general and with respect to learning object in specific and to analyze the adeptness of the measures. Keywords-learning, Learning Objects, Semantic Relatedness Measures