The mission of Sease is to make research in Information Retrieval more accessible to an industry audience, transforming the best research principles, ideas and implementations from academia into real world products.
Researching to improve how people interact with search technologies is a key value in Sease.
Apache Lucene/Solr: We can design and develop search applications for you and implement new features for your use cases.
Search Relevancy Tuning: We can model your data, design better queries, tune search components and features to target your objectives, integrate advance boosting/techniques to improve the quality of results and evaluate relevancy improvements in a precise and user friendly manner.
Machine Learning Integration: We can integrate Machine Learning and semantic technologies with your search application to give a strong boost to your search ecosystem.
Apache Lucene/Solr: We can design and develop search applications for you and implement new features for your use cases.
Search Relevancy Tuning: We can model your data, design better queries, tune search components and features to target your objectives, integrate advance boosting/techniques to improve the quality of results and evaluate relevancy improvements in a precise and user friendly manner.
Machine Learning Integration: We can integrate Machine Learning and semantic technologies with your search application to give a strong boost to your search ecosystem.
Log Monitoring/Search: We can model your log messages in machine readable formats to then search them and monitor them through powerful frameworks.
More than 80% of all potentially usable business information may originate in unstructured form.
Meaningfully structuring content is critical for any domain, Natural Language Processing and Semantic Enrichment is becoming increasingly important to improve the quality of tasks related to information retrieval.With the Semantic Web moving towards full realisation thanks to the Linked Data initiative and with the interest of major search engines in structured data, the search world is finding it more attractive to make its information machine readable and exploit that information to improve search over its content.
Entity-oriented search. Searching not by keyword, but by entities that represent specific concepts in a certain domain.
Knowledge graphs. Leveraging relationships amongst entities: Linked Data datasets (Wikidata, DbPedia….) or custom companies’ knowledge bases.
Search assistance. Autocomplete and spellchecking are now common features, but making use of semantic data makes it possible to offer smarter features, guiding the users to what they want, in a natural way.
Read more on the Sease blog.
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