Browse … Search and find … Explore and discover … Share and publish
Semantic search seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results
Augment your SolR or ElasticSearch capabilities
Understand the intent, contextualise search results
Search using business terms instead of keywords
ElasticSearch or SolR
We know the importance of search in today’s world. We augment your SolR or ElasticSearch capacities.
Ships with graphical interface to explore and visualize semantic data. Graphs, charts, maps and other widgets. Use multiple navigation aids: topics, groups, faceted search, hyperlink navigation,. Get the results regardless of the language. Implement spelling aids, synonyms, autocomplete and query understanding features. Flat, hierarchical or graph presentation.
Navigational search – quickly locate specific content or resource. IInformational search – learn more about a specific subject. Compound term processing, concept search, fuzzy search, simple but smart search, controlled terms, full text or metadata, relevancy scoring. Takes care of language, spelling, accents, case. Boolean expressions, autocomplete, suggestions. Disambiguated queries, suggests alternatives to the original query. Relevance feedback: modify the original query with additional terms. Contextualize by user profile, location, search activity and more.
Initial results are refined, annotated and easy to explore. Sorted by relevancy, important terms are highlighted: easy to decide which one are relevant. Sophisticated facet based filters. Refining results set: more like this, this one, statistical and semantic methods, more like these: graph based activation ranking. Suggestions to help refine results set: new queries based on inferred or combined tags. Related searches and queries.
Explore and discover
More than just search results. Different result clustering methods: statistical cluster or semantic tags clusters. Clouds of semantic tags. Personalized statistical dashboard including analytics and immediate access to datasets. Data and search results augmented using linked open data (eg. Google Knowledge Graph). Mash-up pages generations.
Share and publish
Enrich results with structured data from schema.org or business specific ontology. Integrated RDFa, micro formats, GWA snippets, schema.org. Results downloadable from « shopping cart ». Sparql endpoint and Json-LD simple and fast REST API.
Semantic Search associates search engine (SolR our ElasticSearch) with RDF or graph datastore. A simple and performant API layer makes it a perfect publication server.
Mondeca’s semantic search improves results relevancy and ensures high speed results delivery.
We know how to build content analytics applications on the top of your search engine and other data sources. We use Kibana to analyse ElasticSearch data. We embed Zoomdata for combinations of search engine, database, application, social media and streaming sources.
Schedule a demo
Request a demo today to see how Semantic Search improves your content findability.