Semantic similarity from natural language and ontology analysis 3. Knowledge based semantic measures 3.1 Ontologies as graphs and formal notations 3.1.1 Ontologies as graphs 3.1.2 Relationships 3.1.3 Graph traversals 3.1.4 Notations for taxonomies 3.2 Types of semantic measures and graph properties 3.2.1 Semantic measures on cyclic semantic graphs 3.2.2 Semantic measures on acyclic graphs 3.3 Semantic evidence in semantic graphs and their interpretations 3.3.1 Semantic evidence in taxonomies 3.3.2 Concept specificity 3.3.3 Strength of connotations between concepts 3.4 Semantic similarity between a pair of concepts 3.4.1 Structural approach 3.4.2 Feature based approach 3.4.3 Information theoretical approach 3.4.4 Hybrid approach 3.4.5 Considerations when comparing concepts in semantic graphs 3.4.6 List of pairwise semantic similarity measures 3.5 Semantic similarity between groups of concepts 3.5.1 Direct approach 3.5.2 Indirect approach 3.5.3 List of groupwise semantic similarity measures 3.6 Other knowledge based measures 3.6.1 Semantic measures based on logic based semantics 3.6.2 Semantic measures for multiple ontologies 3.7 Advantages and limits of knowledge based measures 3.8 Mixing knowledge based and corpus based approaches 3.8.1 Generalities 3.8.2 Wikipedia based measure: how to benefit from structured encyclopedia knowledge 3.9 Conclusion 5. Conclusion and research directions A. Examples of syntagmatic contexts B. A brief introduction to singular value decomposition C. A brief overview of other models for representing units of language D. Software tools and source code libraries Bibliography Authors biographies 1. Introduction to semantic measures 1.1 Semantic measures in action 1.1.1 Natural language processing 1.1.2 Knowledge engineering semantic web and linked data 1.1.3 Biomedical informatics and bioinformatics 1.1.4 Other applications 1.2 From similarity toward semantic measures 1.2.1 Human cognition similarity and existing models 1.2.2 Definitions of semantic measures and related vocabulary 1.2.3 From distance and similarities to semantic measures 1.3 Classification of semantic measures 1.3.1 How to classify semantic measures 1.3.2 A general classification of semantic measures 4. Methods and datasets for the evaluation of semantic measures 4.1 A general introduction to semantic measure evaluation 4.2 Criteria for semantic measure evaluation 4.2.1 Accuracy precision and robustness 4.2.2 Computational complexity 4.2.3 Mathematical properties 4.2.4 Semantics 4.2.5 Technical details 4.3 Existing protocols and datasets 4.3.1 Protocols used to compare measures 4.3.2 Datasets 4.4 Discussions 2. Corpus based semantic measures 2.1 From text analysis to semantic measures 2.2 Semantic evidence of word similarity in natural language 2.2.1 The meaning of words 2.2.2 Structural relationships: paradigmatic and syntagmatic 2.2.3 The notion of context 2.2.4 Distributional semantics 2.3 Distributional measures 2.3.1 Implementation of the distributional hypothesis 2.3.2 From distributional model to word similarity 2.3.3 Capturing deeper co occurrences 2.4 Other corpus based measures 2.5 Advantages and limits of corpus based measures 2.5.1 Advantages of corpus based measures 2.5.2 Limits of corpus based measures 2.6 Conclusion Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments.most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language e.g. words sentences or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics i.e. their meaning.intuitively the words tea and coffee which both refer to stimulating beverage will be estimated to be more semantically similar than the words toffee (confection) and coffee despite that the last pair has a higher syntactic similarity. The two state of the art approaches for estimating and quantifying semantic similarities relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal computer readable and workable forms of knowledge such as semantic networks thesauri or ontologies. Semantic measures are widely used today to compare units of language concepts instances or even resources indexed by them (e.g. documents genes). They are central elements of a large variety of Natural Language Processing applications and knowledge based treatments and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures the aim of this monograph is to convey novices as well as researchers of these domains toward a better understanding of semantic similarity estimation and more generally semantic measures. To this end we propose an in depth characterization of existing proposals by discussing their features the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures our aim is to give the reader key knowledge required to: (i) select the more relevant method ...