## 1 Introduction  
Parliament libraries provide Members of Parliament (MP) and their assistants, journalists, political analysts and researchers information and documentation for parliamentary issues. Besides traditional publications, the web and the social web play an increasingly important role as information sources, which provide important and crucial background information, like reactions to political events and comments made by the general public. It is in the interest of the parliaments to create a platform for preserving, managing, mining and analyzing all of the information provided on the web and in social media. The ARCOMEM application (the official release of the ARCOMEM system is available online [1]) presented in this paper is driven by two European parliaments (the Greek and the Austrian parliament) and targets the effective creation of political archives based on the web and social media. Through ARCOMEM, the Greek and Austrian parliaments aspire to transform their flat digital content archives to historical and community memories. Community memories largely revolve around events, as well as entities, topics and opinions related to these events. These may be unique events, such as the first landing on the moon or a natural disaster, or regularly occurring events, such as elections or TV serials. In this context, the main logical concepts considered in ARCOMEM extraction and enrichment activities are entities, topics, opinions and events (ETOEs). To create incrementally-enriched web archives that allow access to all sorts of web content in a structured and semantically meaningful way, extraction, enrichment and consolidation of ETOEs are of crucial importance [2]. To this extent, the main challenges we face are related to the extraction, detection and correlation of ETOEs and related information in a vast number of heterogeneous web resources. These processes face issues arising from the diversity of the nature and quality of web content, in particular when considering social media and user-generated content, where further issues are posed by the informal use of language. While entities and events resulting from the automatic extraction processes provide an initial classification and structure for the crawled web documents, they can be heterogeneous, ambiguous and provide only very limited information. Therefore, data enrichment and consolidation in ARCOMEM follows two aims: (1) enrich and disambiguate extracted entities with related publicly available knowledge; and (2) identify data correlations by aligning ARCOMEM entities with reference datasets. To achieve these aims, we enrich ARCOMEM entities using Linked Open Data (LOD) sources, such as DBpedia [3] and Freebase [4], and correlate the entities using their direct and indirect relationships within the LOD graph (see, e.g., [5]). In addition to entity and event extraction, topic discovery and analysis techniques provide an effective way of analyzing and browsing large collections of textual data, such as the ones collected during the ARCOMEM's crawling campaigns, as they are able to uncover the hidden thematic and semantic structure of the data. By exploiting the potentiality of this kind of analysis, the ARCOMEM framework provides state-of-the-art tools for probabilistic topic detection and to analyze the trendiness and dynamical change of topics over time. These tools can provide a high level perspective of the textual data retrieved during a crawling campaign and help the user to understand the semantic concepts behind each document. In addition, we can investigate relationships between topics and their social influence ofusers. A proposed topic-aware social influence framework jointly learns topics and the users' influence in those topics [6]. In this paper, we first provide an overview of the ARCOMEM offline processing chain that supports the extraction of semantic information from web archives and then present the search and retrieval application (SARA) that enables users to navigate through the archived content using semantic information.