Semantic Flooding: Semantic Search across Distributed Lightweight Ontologies

Lightweight ontologies are trees where links between nodes codify the fact that a node lower in the hierarchy describes a topic (and contains documents about this topic) which is more specific than the topic of the node one level above. In turn, multiple lightweight ontologies can be connected by semantic links which represent mappings among them and which can be computed, e.g., by ontology matching. In this paper we describe how these two types of links can be used to define a semantic overlay network which can cover any number of peers and which can be flooded to perform a semantic search on documents, i.e., to perform semantic flooding. We have evaluated our approach by simulating a network of 10,000 peers containing classifications which are fragments of the DMoz web directory. The results are promising and show that, in our approach, only a relatively small number of peers needs to be queried in order to achieve high accuracy

A Metadata-Enabled Scientific Discourse Platform

Scientific papers and scientific conferences are still, despite the emergence of several new dissemination technologies, the de-facto standard in which scientific knowledge is consumed and discussed. While there is no shortage of services and platforms that aid this process (e.g. scholarly search engines, websites, blogs, conference management programs), a widely accepted platform used to capture and enrich the interactions of research community has yet to appear. As such, we aim to create new ways for the members and interested people working in research communities to interact; before, during and after their conferences. Furthermore, to serve as a base to these interactions, we want not only to obtain, format and manage a body of legacy and new papers related to this community but also to aggregate several useful information and services to the environment of a discourse platform

P2P Semantic Search

We consider P2P Semantic Search as a process of finding documents, which are semantically, i.e., with respect to the meaning, related to the user information needs, in a document collections distributed among a group of peers, i.e., autonomous information sources. To organize documents stored on a single peer efficiently for the search, documents are classified to the user-generated classifications. Nodes in the classification specify concepts which the user is interested in. Accordingly, the whole classification specifies the user interest profile. To provide effective search in the P2P network, peers in the network should have some ways for cooperation. In our approach, related nodes in classifications on different peers are interconnected by means of semantic links, which allow peers in the network to reason about the contents of each other and efficiently cooperate. The main foci of the current PhD thesis are the development of an algorithm for P2P Semantic Search in a distributed system of interconnected classifications; the development of a P2P Semantic search system implementing the algorithm; and the development of a testing methodology allowing for a comprehensive evaluation of the system

Automatic Generation of a Large Scale Semantic Search Evaluation Data-Set

To compare the performance of information retrieval techniques in various settings, the data-sets which model these settings need to be generated. Although there are already available collections, such as those used in TREC conference series, which are used for evaluation of various retrieval tasks, there is a lack of collections which are specially developed for evaluation of the effectiveness of semantically enhanced text retrieval techniques. In this paper, we propose an approach for the automatic generation of such data-sets, by using search engines query logs and data from human-edited web directories. The evaluation is performed by comparing the performance of Lucene, a popular syntactic search engine, and Concept Search, a search engine which extends Lucene's syntactic search with semantics

P2P Concept Search: Some Preliminary Results

Concept Search extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations between complex concepts. It allows us to deal with ambiguity of natural language. P2P Concept Search extends Concept Search by allowing distributed semantic search over structured P2P network. The key idea is to exploit distributed, rather than centralized, background knowledge and indices

Concept Search: Semantics Enabled Syntactic Search

Historically, information retrieval (IR) has followed two principally different paths that we call syntactic IR and semantic IR. In syntactic IR, terms are represented as arbitrary sequences of characters and IR is performed through the computation of string similarity. In semantic IR, instead, terms are represented as concepts and IR is performed through the computation of semantic relatedness between concepts. Semantic IR, in general, demonstrates lower recall and higher precision than syntactic IR. However, so far the latter has definitely been the winner in practical applications. In this paper we present a novel approach which allows it to extend syntactic IR with semantics, thus leverage the advantages of both syntactic and semantic IR. First experimental results, reported in the paper, show that the combined approach performs at least as good as syntactic IR, often improving results where semantics can be exploited

Concept Search: Semantics Enabled Information Retrieval

In this paper we present a novel approach, called Concept Search, which extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations between concepts. The key idea of Concept Search is to operate on complex concepts and to maximally exploit the semantic information available, reducing to syntactic search only when necessary, i.e., when no semantic information is available. The experimental results show that Concept Search performs at least as well as syntactic search, improving the quality of results as a function of the amount of available semantics

Concept Search

In this paper we present a novel approach, called Concept Search, which extends syntactic search, i.e., search based on the computation of string similarity between words, with semantic search, i.e., search based on the computation of semantic relations between concepts. The key idea of Concept Search is to operate on complex concepts and to maximally exploit the semantic information available, reducing to syntactic search only when necessary, i.e., when no semantic information is available. The experimental results show that Concept Search performs at least as well as syntactic search, improving the quality of results as a function of the amount of available semantics

Formalizing the Get-Specific Document Classification Algorithm

The paper represents a first attempt to formalize the get- specific document classification algorithm and to fully automate it through reasoning in a propositional concept language without requiring user involvement or a training dataset. We follow a knowledge-centric approach and convert a natural language hierarchical classification into a formal classification, where the labels are defined in the concept language. This allows us to encode the get-specific algorithm as a problem in the concept language. The reported experimental results provide evidence of practical applicability of the proposed approach

Two-layered architecture for peer-to-peer concept search

The current search landscape consists of a small number of centralized search engines posing serious issues including centralized control, resource scalability, power consumption and inability to handle long tail of user interests. Since, the major search engines use syntactic search techniques, the quality of search results are also low, as the meanings of words are not considered effectively. A collaboratively managed peer-to-peer semantic search engine realized using the edge nodes of the internet could address most of the issues mentioned. We identify the issues related to knowledge management, word-to-concept mapping and efficiency in realizing a peer-to-peer concept search engine, which extends syntactic search with background knowledge of peers and searches based on concepts rather than words. We propose a two-layered architecture for peer-to-peer concept search to address the identified issues. In the two-layered approach, peers are organized into communities and background knowledge and document index are maintained at two levels. Universal knowledge is used to identify the appropriate communities for a query and search within the communities proceed based on the background knowledge developed independently by the communities. We developed proof-of-concept implementations of peer-to-peer syntactic search, straightforward single-layered and the proposed two-layered peer-to-peer concept search approaches. Our evaluation concludes that the proposed two-layered approach improves the quality and network efficiency substantially compared to a straightforward single-layered approach