A review of ranking approaches for semantic search on Web

Highlights

• An exhaustive review of ranking approaches for semantic search on Web.

• We identified three stages of ranking in semantic search process.

• We identified criteria for the comparison of semantic search approaches.

• We examined some open issues relevant to efficient semantic search.

Abstract

With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.

Introduction

Web search is a key application of the Web where present search technologies rely on link analysis techniques that exploit the structure of Web to determine important documents. At the same time, they rely on simple term statistics to identify documents that are most relevant to a query. Mark-up languages such as (X)HTML are primarily focused to documents whose content should be interpretable by human interpreters and hence focused on document structure and its presentation. Little efforts are paid to the representation of the semantics of the content itself.

The growing availability of structured information on the Web enables new opportunities for information access. Semantically oriented search engines and specifically that use ontologies as enabling technologies have gained considerable interest in the last decade. The ever growing amount of ontology-based semantic mark-up in the Web provides an opportunity to start working in the direction of a new generation of open intelligent applications (Motta & Sabou, 2006). Efficient search is one such major envisioned application of this next generation Web popularly known as Semantic Web (Burners-Lee, Hendler, & Lassila, 2001).

Current Web search techniques are not directly suited for indexing and retrieval of semantic mark-up. Document is treated as a bag of words where words or word variants are recognized as indexing terms. The existing semantic mark-up is either simply ignored by many search engines for indexing purposes or not processed in a way that allows the mark-up to be used distinguishably from other text during the search.

The upcoming Web search is no longer limited to matching keywords of the query against documents but instead complex information needs can be expressed in a structured way with precise and structured answers as results. The kind of search in which user’s information needs are addressed by considering the meaning of user’s query as well as available resources is referred to as Semantic Search (Tran, Haase, & Studer, 2009).

Due to the ever increasing move from data to knowledge and increasing popularity of the vision of Semantic Web, there is equally increasing interest and work in automatically extracting and representing the metadata as semantic annotation to the documents and services on the Web (Shah, Finin, Joshi, Cost, & Mayfield, 2002). It seems that each Web page would possess semantic annotation that record additional details concerning the page itself. Annotations are based on classes of concepts and relations among them. The “vocabulary” for the annotation is usually expressed by means of ontology. The information contained in such agreed upon ontology is quite valuable for determining the relevance of the retrieved documents based on the “known” facts, relationships or the other data. Table 1 shows a comparison of features of Traditional Keyword-based search and Semantic-based search based on various parameters.

The two elements of the ontology are quite significant from the “relevant information access” point of view. The first element is the named entities such as names of persons, objects, countries, places, research articles, artists, and museum. Available techniques had been developed for entity oriented search of documents (Aleman-Meza, Arpinar, Nural, & Sheth, 2010). The second element is the relationships which provide meaning to the entity. The value of such relationships relies on the fact that those are named relationships. Relationships play a vital role in the relevant information access as the Web evolves continuously (Sheth & Ramakrishnan, 2007).