MESOC SERAPEUM

Place where Artificial Intelligence meets CULTURE

AI analysis of scientific papers regarding impact of culture

MESOC


        MESOC is a Research and Innovation Action designed to propose, test and validate an innovative and original approach to measuring the societal value and impacts of culture and cultural policies and practices, related to three crossover themes of the new European Agenda for Culture :
         1) Health and Wellbeing,
         2)Urban and Territorial Renovation and
        3)People’s Engagement and Participation.

The global aim is to respond to the challenge posed by the H2020 Call ”To develop new perspectives and improved methodologies for capturing the wider societal value of culture, including but also beyond its economic impact”


MESOC SERAPEUM


        Project idea is to enable MESOC consortium members and other cultural stakeholders to become acquainted with the achievements of artificial intelligence and gain insight into the possibilities of AI. It is work in progress and will be updated regularly.AI tools are especially used to identify transaction variables that define contextualisation of research findings.

Search


Search documents and data for cultural artifacts

Analysis


Analysis of cultural artifacts using Artificial Intelligence.

Transition variables

Transition variables from documents using AI

Indicators


Social impact indicators from transition variables

Tools


Aditional tools showing AI impact on culture.

About


Mesoc website - all data and results from MESOC project


Semantic search

Semantic search on articles. Semantic search describes a search engine’s attempt to generate the most accurate search engine results possible by understanding based on searcher intent, query context, and the relationship between words.Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. Some authors regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources


Questions and answers

Questions and answers from all documents

Document Semantic search

Find articles best representing query

Text semantic search


Semantic search on full text of all documents

Analysis

Analysis of documents with Artificial Intelligence

The system reviews documents to assess the social impact of culture. The review includes analysis of all and individual documents, clustering, summarizing, keyword analysis, and other analytical tools and methods.


Particular Document Analysis

Document analysis : resolving study technique, key phrases, keywords, summary for article, finding article's cultural category and social impact, ask questions on article, showing wordcloud, finding similar articles from same cluster

Document analysis

Analysis of particular document

Document map

Geographical document distribution

Similar documents

Find semantically similar documents


Documents cluster analysis


Text clustering is the task of grouping a set of unlabelled texts in such a way that texts in the same cluster are more similar to each other than to those in other clusters. Text clustering algorithms process text and determine if natural clusters (groups) exist in the data.
As computers work with numbers, text has to be transformed into multidimensional numbers as vectors. We use here 4096 dimensional space.
The idea is that documents can be represented numerically as vectors of features. The similarity in text can be compared by measuring the distance between these feature vectors. Objects that are near each other should belong to the same cluster. Objects that are far from each other should belong to different clusters

Cluster Analysis

Cluster analysis of full text documents

Documents by cluster

Documents defining each cluster

Clusterized documents

Most significant documents for cluster

Cluster Summary

Summary of full text documents for each cluster


Documents topic analysis


topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.
Given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently
The topics produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's of topics is.

Topic Analysis

Topic analysis using latent Dirichlet allocation

Topics by cluster

Keywords defining each topic cluster

Documents in topic cluster

Most significant documents for topic cluster

Topic Summary

Summary for each topic cluster


Cultural domain document analysis


Cultural domain phrases

Keywords and phrases for each cultural category

Cultural domain Q&A

Questions and answers on each cultural domain

Cultural domain text summary

Text summary based on cultural domain


Social impact document analysis


Social impact phrases

Keywords and phrases for each social impact

Social impact Q&A

Questions and answers on each social impact

Social impact text summary

Text summary based on each social impact


Cultural category and social impact document analysis


Category and impact phrases

Keywords and phrases for each cultural category and social impact

Category and impact Q&A

Questions and answers on each cultural category and social impact

Category and impact text summary

Text summary based on each cultural category and social impact


TRANSITION VARIABLES


contextual elements, which can be measured ensuring that the cultural policy or practice under inspection is generating public value and/or affecting, at least to some extent, the target individuals or groups.




Transition variables show us the paths of transformation and the channels of materialization of the impact, in a richer and more complex analysis than the cause-effect linearity.

Transition processes are complex and non linear, but induce changes across time.

Transition variables enable a better contextualisation of the concrete processes in concrete places and periods.They can be observed in shorter periods of time than the expected impacts.

We can obtain them from the experiences recorded in scientific literature and in grey material reports (evaluation reports, memos, programs...)

.



Transition variables search and view

We are using a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for a text classification task. The main goal of the model in a text classification task is to categorize a text into one of the predefined labels or tags, and in our case to read sentences and to classify them as normal sentences or potential transition variables.
BERT is pretrained on unlabeled data extracted from BooksCorpus, which has 800M words, and from Wikipedia, which has 2,500M words. We are fine-tuning it, by training on our sample of 3500 sentences each of which is manually marked, as trans. variable or anything else.

Transition variables


Transition variables for all documents


Social impact transition variables

Search and view of transition variables based on social impact

Cultural domain transition variables

Search and view of transition variables based on cultural domain

Combined transition variables

Search and view of transition variables based on cultural domain and social impact

Impact transition variables summary

Summary of transition variables based on social impact


Domain transition variables summary

Summary of transition variables based on cultural domain

Combined transition variables summary

Summary of transition variables based on cultural domain and social impact


Transition variables clustering and analysis


Transitional variables topics


Cluster of transitional variable topics

Transactional variables topics keywords

Keywords and topics by cluster of transitional variables

Transitional variables sentences by topic

Transitional variables sentences divided on cluster topic

Transitional variables summary


Transitional variables summary on cluster topic

Transitional variables cluster

Clustered transitional variables

Transitional variables by cluster


Transitional variables defining each cluster



THE CONVERGENT MODEL


Transformative process - Using AI to extract sentences defining social impacts



‘Social impacts’ is the term which describes the changes in the quality of life of the local residents. Changes that affect individuals’ surroundings (architecture, arts, customs, rituals etc.) constitute cultural impacts. The enormous range of impacts include arts and crafts through to the fundamental behaviour and beliefs of individuals and collective groups (Sharpley, 2008; Sharpley & Telfer, 2014).
.


Social impacts view


We are using semantic search engines to identify social impacts by finding a numerical representation of text queries using state-of-the-art language models, indexing them in a high-dimensional vector space and measuring how similar a query vector is to the indexed documents.

Predefined impacts

List of redefined impacts


Social impact sentences

Search and view of Social impact sentences

Impacts and sentences

Search and view of sentences based on predefined impacts

Semantic search

Search and view of sentences transition variables based on impacts

Impact keywords

Keywords from impact sentences based on social predefined impacts


Social impacts graph

Graph showing number of sentences for predefined impacts



ADITIONAL TOOLS

Aditional Artificial Intelligence tools for usage in culture.

Creating Images from Text

Creates images from text

AI MUSIC

Artificial intelligence creation of music

AI WRITING

Artificial intelligence writing free text.