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. Major web search engines like Google and Bing incorporate some elements of semantic search. In vertical search, LinkedIn publishes their semantic search approach to job search by recognizing and standardizing entities in both queries and documents, e.g., companies, titles and skills, then constructing various entity-awared features based on the entities.
Guha et al. distinguish two major forms of search: navigational and research. In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic search is not applicable to navigational searches. In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search.
Rather than using ranking algorithms such as Google's PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. However, in 2012 Google also announced its own Semantic Search project.
Author Seth Grimes lists "11 approaches that join semantics to search", and Hildebrand et al. provide an overview that lists semantic search systems and identifies other uses of semantics in the search process.
Other authors primarily regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web. Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.
In order to understand what a user is searching for, word sense disambiguation must occur. When a term is ambiguous, meaning it can have several meanings (for example, if one considers the lemma "bark", which can be understood as "the sound of a dog," "the skin of a tree," or "a three-masted sailing vessel"), the disambiguation process is started, thanks to which the most probable meaning is chosen from all those possible.
Such processes make use of other information present in a semantic analysis system and takes into account the meanings of other words present in the sentence and in the rest of the text. The determination of every meaning, in substance, influences the disambiguation of the others, until a situation of maximum plausibility and coherence is reached for the sentence. All the fundamental information for the disambiguation process, that is, all the knowledge used by the system, is represented in the form of a semantic network, organized on a conceptual basis.
In a structure of this type, every lexical concept coincides therefore with a semantic network node and is linked to others by specific semantic relationships in a hierarchical and hereditary structure. In this way, each concept is enriched with the characteristics and meaning of the nearby nodes.
Every node of the network (called Synset) groups a set of synonyms which represent the same lexical concept (called Synsets) and can contain:
- single lemmata ('seat', 'vacation'; 'work', 'quick'; 'quickly', 'more', etc.)
- compounds ('non-stop', 'abat-jour', 'policeman')
- collocations ('credit card', 'university degree', 'treasury stock', 'go forward', etc.)
The semantic relationships (links), which identify the semantic relationships between the synsets, are the order principals for the organization of the semantic network concepts.
Commonly used searching methodologies
- RDF Path Traversal - traversing the net formed by a graph of information that uses the RDF data model.
- Keyword to Concept Mapping
- Graph Patterns - used to formulate patterns for locating interesting connecting paths between resources. Also commonly used in data visualization.
- Logics - by using inference based on OWL
- Fuzzy concepts, fuzzy relations, and fuzzy logics
- Related Searches Engines and Technologies
- Semantic Search Engines and Technologies for Reference Results
- Search Engines and Technologies for Semantically Annotated Results
- Full-text Similarity Search Engines and Technologies
- Search Engines and Technologies on Semantic/Syntactic Annotations
- Concept Search Engines and Technologies
- Ontology-based Search Engines and Technologies
- Semantic Web Search Engines and Technologies
- Faceted Search Engines and Technologies
- Clustered Search Engines and Technologies
- Natural Language Search Engines and Technologies
Ten defining attributes
The attributes of semantic search (those qualities that make it distinct from non-semantic search) are not all necessarily advantages by definition. For example, some attributes may improve search accuracy because of an exhaustive reiterative process but by effect overconsume time and/or resources. Accordingly, these ten attributes are merely salient features although the underlying assumption is that under perfect conditions they are generally preferable.
- Handling morphological variations
- Handling synonyms with correct senses
- Handling generalizations
- Handling concept matching
- Handling knowledge matching
- Handling natural language queries and questions
- Ability to point to uninterrupted paragraph and the most relevant sentence
- Ability to Customize and Organic Progress
- Ability to operate without relying on statistics, user behavior, and other artificial means
- Ability to detect its own performance
- John, Tony (March 15, 2012). "What is Semantic Search?". Techulator. Retrieved July 13, 2012.
- Li, Jia; Arya, Dhruv; Ha-Thuc, Viet; Sinha, Shakti (2016-01-01). "How to Get Them a Dream Job?: Entity-Aware Features for Personalized Job Search Ranking" (PDF). Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/2939672.2939721.
- Guha, R.; McCool, Rob; Miller, Eric (May 24, 2003). "Semantic Search". WWW2003. Retrieved July 13, 2012.
- Efrati, Amir (March 15, 2012). "Google Gives Search a Refresh". The Wall Street Journal. Retrieved July 13, 2012.
- Grimes, Seth (January 21, 2010). "Breakthrough Analysis: Two + Nine Types of Semantic Search". InformationWeek. Retrieved June 18, 2017.
- Dong, Hai (2008). A survey in semantic search technologies. IEEE. pp. 403–408. Retrieved 1 May 2009.
- Ruotsalo, T. (May 2012). "Domain Specific Data Retrieval on the Semantic Web". ESWC2012: 422–436. doi:10.1007/978-3-642-30284-8_35. Retrieved August 14, 2012.
- Mäkelä, Eetu. "Survey of Semantic Search Research" (PDF). Retrieved July 13, 2012.
- Dong, Hai; Hussain, Farookh (2015). "Service-requester-centered service selection and ranking model for digital transportation ecosystems". Computing. 97 (1): 79-102.
- Portmann, Edy (2012). The FORA Framework. Springer. p. 204. ISBN 978-3-642-33232-6.
- Dong, Hai (2010). "Semantic Search Engines and Related Technologies" in A Customized Semantic Service Retrieval Methodology for the Digital Ecosystems Environment (PDF). PhD Thesis, Curtin University. p. 71-104.
Several scientific events cover the topic of semantic search explicitly: