saurav kumar

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The architecture of a knowledge base system based on description logics is shown in Figure 6.3. It provides facilities to set up knowledge bases, to reason about their content, and to manipulate them. A knowledge base comprises two components: the TBOX and the ABOX. The TBOX introduces the terminology – i.e., the vocabulary of an application domain – while the ABOX contains assertions about named individuals in terms of this vocabulary.
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
A description logic is the result of the merging of two traditions: knowledge representation and automated reasoning. Arvor et al. (2013) stated: “Description Logic allows the formal representation of knowledge by modeling the interrelationships among subject (Concept), predicate (Property) and object (Concept, Property) as axioms.”
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
A family of formal knowledge representation languages is known as description logics (DLs). A description logic is set of formal logic-based knowledge representation formalisms aimed at describing domain-specific knowledge in a structured and well-understood manner. In artificial intelligence, DLs are used to characterize and reason about the relevant concepts in a domain (known as terminological knowledge). Each description logic describes a language, and each language differs in expressibility versus reasoning complexity, defined by allowing or disallowing different constructs (conjunction, …
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
Moreover, ontologies provide an additional benefit of having multiple layers of representation, which can streamline the application of NLP to similar problems.
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
Furthermore, difference between knowledge graph and property graph are explained.
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
In principle, an ontology may be described in any language, but in order to share, exchange, and map ontologies, a formal language must be chosen. The use of natural language alone is not sufficient for this as it leaves a significant amount of interpretation to the user, which means that significant aspects of the ontology may be missed. This issue can be resolved and the knowledge can be made automatically processable by a machine by expressing it as simply as possible in an adequate format. Several syntactic and semantic languages have been developed and standardized for this purpose, such …
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
The semiotic triangle defined by Ogden et al. (1925) is composed of concept, symbol, and referent. A referent is conceptualized into a concept which is expressed as a symbol. The symbol designates the concept which refers to the referent (as explained in Figure 6.1). Ogden’s triangle (a.k.a. Ullman’s triangle) provides the philosophical base for conceptualizing ontology construction.
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
The study of how things gain or convey meaning is semiotics.
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
“An Ontology is a formal explicit specification of shared conceptualization” (Gruber, 1993). A “conceptualization” is an abstract representation of some phenomenon in the world that identifies the relevant concept of that phenomenon. “Explicit” means that the types of concepts and constraints on their use are explicitly defined. The term “formal” describes the ontology as having a machine-readable format, which results in varying degrees of formality. “Shared” refers to the idea that ontology captures consensus knowledge – i.e., it does not belong exclusively to a particular individual, but is…
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
Semantic technologies differ fundamentally from other data technologies such as relational databases because they focus on the meaning of data rather than its structure.
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
Compositional semantic analysis – Although knowing the meaning of each word in a text is important, understanding the meaning of the text in its entirety is not. As a result, we strive to comprehend how individual words combine to generate the meaning of the text via compositional semantic analysis. Both lexical and compositional semantic can be supported by semantic technology, but the technology has a greater utility for compositional semantics, which is quite complex.
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
“Semantics” refers to the analysis of natural language text as it relates to the concept of meaning, taking into account the deep understanding of entities. It is the study of the meaning of natural language expressions. Traditionally, meaning has been associated with definition, intention, and reference (Jurasfsky et al., 2012): meaning implies defining an entity in a sentence, intention corresponds to describing the state of action of the subject, and reference specifies an equivalent relationship with the object in a sentence. Two important aspects of semantics in natural language are descr…
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing | Graph Learning and Network Science for Natural Language Processing
Although OWL ontology files are text, it’s possible to have two identical ontologies saved to two files with very different structure. ROBOT provides a diff command that compares two ontologies while ignoring these differences:
Diff (Compare) | robot
Manchester expressions can also be used within the cells. To avoid ambiguity, it’s best to enclose expressions in parentheses:
Template | robot
writing OWL class expressions.
Manchester OWL Syntax - Protege Wiki
ROBOT uses the same syntax for property expressions as Protégé: Manchester Syntax. If it does not recognize a name, ROBOT will assume that you’re trying to refer to an entity by its IRI or CURIE.
Template | robot
ble to jointly reason across three fundamental biological properties of proteins: sequence, structure, and function.
evolutionaryscale/esm
No, the BFO hierarchy is imported in PBPKO and ignored completely for all the PBPKO native classes, which include both continuants and occurrents.
Request for new ontology pbpko · Issue #2563 · OBOFoundry/OBOFoundry.github.io
PBPKO should import their statistical test classes from an established ontology.
Request for new ontology pbpko · Issue #2563 · OBOFoundry/OBOFoundry.github.io
The identifier scheme must be fixed.
Request for new ontology pbpko · Issue #2563 · OBOFoundry/OBOFoundry.github.io
ROBOT uses the same syntax for property expressions as Protégé
Template | robot
A UI schema is basically an object literal providing information on how the form should be rendered, while the JSON schema tells what.
uiSchema | react-jsonschema-form
The catch is that the pickle format can change between python or numpy versions, so a different version of numpy or python won't necessarily be able to read it (although numpy developers try to keep the pickle reader as backwards-compatible as possible):
python - Numpy Array to SQL Table - Stack Overflow
Computer Science with familiarity graph and neural network concepts and an excellent publication record. Experience in Cheminformatics, ontologies and semantics as well as Generative AI is a plus.
Research Associate - Institute of Cancer and Genomic Sciences - 103978 - Grade 6 - UoB Employee Careers
CERJ consists of 12 faculty members and is/has recruited four additional academics who together have the mission to help remedy harm to human health and the environment caused by pollution.
Research Associate - Institute of Cancer and Genomic Sciences - 103978 - Grade 6 - UoB Employee Careers
The successful candidate will have a strong background in computer science, state-of the-art machine learning, artificial intelligence and ontology harmonisation and integration. Experience with developing and applying explainable machine learning/AI within a cheminformatic setting is a clear advantage
Research Associate - Institute of Cancer and Genomic Sciences - 103978 - Grade 6 - UoB Employee Careers
which aims to develop integrated computational approaches for addressing new market goals for the introduction of safe-and-sustainable-by-design chemicals and material.
Research Associate - Institute of Cancer and Genomic Sciences - 103978 - Grade 6 - UoB Employee Careers
We are also advertising this position at Grade 7 Research Fellow level (post 103965), and only one hire will be made for both advertisements.
Research Associate - Institute of Cancer and Genomic Sciences - 103978 - Grade 6 - UoB Employee Careers
The microcystin-RR structures are compared with the structures of microcystin-LR in solution as well as in the crystal structure of the complex with protein phosphatase.
The solution NMR structure of a blue-green algae hepatotoxin, microcystin-RR--a comparison with the structure of microcystin-LR - PubMed
Because topics are organized hierarchically, documents can be modeled with different degrees of coarse graining. On the one hand, lower levels in the hierarchy tend to describe very specific concepts (in the case of word topics) or to group together law articles of the same law (in the case of legislation topics, more than expected by chance, Supplementary Text S4 and Fig. S2 in Additional file 1).
Socially disruptive periods and topics from information-theoretical analysis of judicial decisions | EPJ Data Science | Full Text
he hierarchical structure of the model is illustrated by expanding a particular topic: from left to right, we take a topic at the highest level of the hierarchy (level 3) and expand specific sub-topics at successively lower levels.
Socially disruptive periods and topics from information-theoretical analysis of judicial decisions | EPJ Data Science | Full Text
Unlike other approaches, the number of topics and levels in the hierarchy is inferred from the data rather than chosen manually.
Socially disruptive periods and topics from information-theoretical analysis of judicial decisions | EPJ Data Science | Full Text
Text: The bipartite network of words and documents as nodes, where edges represent the number of times a word appears in a document (same as above)
TopSBM: Topic Models based on Stochastic Block Models
We can incorporate these different types of data into a unified topic model by extending the above approach using multilayer stochastic block models. In this approach, each type of data is represented as a different layer in the network. We can represent the information contained in the Wikipedia dataset with the following three layers:
TopSBM: Topic Models based on Stochastic Block Models
In many cases, we have additional information available about the documents, such as metadata or hyperlinks.
TopSBM: Topic Models based on Stochastic Block Models
63 Wikipedia articles related to Physics
TopSBM: Topic Models based on Stochastic Block Models
In addition to this, the SBM method is based a nonparametric “symmetric” formulation that allows for the simultaneous hierarchical clustering of documents as well as words.
TopSBM: Topic Models based on Stochastic Block Models
The SBM formulation, on the other hand, can easily accommodate this kind of heterogeneity, since it is based on more general priors (see here, here and here).
TopSBM: Topic Models based on Stochastic Block Models
We analyze the time evolution of each topic by calculating its importance in a given year relative to its maximum importance across years
Socially disruptive periods and topics from information-theoretical analysis of judicial decisions | EPJ Data Science | Full Text
we use the network-based method proposed by Gerlach et al. [29], which infers a hierarchical stochastic block model (SBM) from the bipartite network formed by documents and their words [29] (Fig. 1B and Fig. 2).
Socially disruptive periods and topics from information-theoretical analysis of judicial decisions | EPJ Data Science | Full Text
Here, we use an information-theoretic methodology to quantitatively track trends and shifts in the evolution of large corpora of judicial decisions, and thus to detect periods in which disruptive topics arise.
Socially disruptive periods and topics from information-theoretical analysis of judicial decisions | EPJ Data Science | Full Text
However, if you would like to be able to connect to your MongoDB server from a remote location, you have to allow incoming connections to the port where the database is listening by adding a new UFW rule.
How To Configure Remote Access for MongoDB on Ubuntu 20.04 | DigitalOcean
Certifications: AWS, Unix/Linux OS and shell scripting, Python, Java, Rstudio, Jupyter, Hadoop, Spark, Hive
Biomedical Text Mining Jobs, Employment | Indeed.com
The Trie can be constructed based on the word vectors generated by ESA, allowing for efficient search of words with similar meanings
can we integrate Trie data structure with Word2vec model for efficent search
The text search techniques mentioned in the search results, such as ESA (Explicit Semantic Analysis) and Word2vec, can be combined with a Trie data structure to improve the efficiency and accuracy of the search.
can we integrate Trie data structure with Word2vec model for efficent search
all occurrences of pattern/search string P (of length m).
Notes - Suffix Tree
Suffix Tree provides a particularly fast implementation for many important (long) string operations.
Notes - Suffix Tree
In terms of speed, a regular trie tree would be slightly faster because its operations don’t involve any string operations, they are simple loops.
Compressed Tries - GeeksforGeeks
If still unsuccessful, preon calculates the normalized pairwise edit distances to all reference names. Steps two and three apply individual thresholds to define matches.
preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology | Bioinformatics | Oxford Academic
In 1995, Esko Ukkonen proposed a linear time algorithm for creating suffix trees i.e., Ukkonen's Algorithm. Initially, an implicit suffix tree is created that contains the first character of the string.
Ukkonen's Suffix Tree Algorithm (Python, C++ & Java code)