Semantic Search

Semantic web

Google Hummingbird

Semantic search seeks to improve search accuracy by understanding searcher 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. There are 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 that he is trying to get to. Rather, the user is trying to locate a number of documents which together will give him the information he is trying to find. Semantic Search lends itself well here.

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. Other authors primarily regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies as found on the Semantic Web (a movement that promotes common formats for data on the Web, and the inclusion of semantic content in web pages). Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify his 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 ship’), 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’); and collocations (‘credit card,’ ‘university degree,’ ‘treasury stock,’ ‘go forward’). The semantic relationships (links), which identify the semantic relationships between the synsets, are the order principals for the organization of the semantic network concepts.

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